prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What argument components do the ML methods aim to identify?
Context 1: Introduction
The art of argumentation has been studied since the early work of Aristotle, dating back to the 4th century BC BIBREF0   It has been exhaustively examined from different perspectives, such as philosophy, psychology, communication studies, cognitive science, formal and informal logic, linguistics, computer science, educational research, and many others  In a recent and critically well-acclaimed study, Mercier Sperber 2011 even claim that argumentation is what drives humans to perform reasoning  From the pragmatic perspective, argumentation can be seen as a verbal activity oriented towards the realization of a goal BIBREF1 or more in detail as a verbal, social, and rational activity aimed at convincing a reasonable critic of the acceptability of a standpoint by putting forward a constellation of one or more propositions to justify this standpoint BIBREF2  
Analyzing argumentation from the computational linguistics point of view has very recently led to a new field called argumentation mining BIBREF3   Despite the lack of an exact definition, researchers within this field usually focus on analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and analyze textual data at hand 
Our motivation for argumentation mining stems from a practical information seeking perspective from the user-generated content on the Web  For example, when users search for information in user-generated Web content to facilitate their personal decision making related to controversial topics, they lack tools to overcome the current information overload  One particular use-case example dealing with a forum post discussing private versus public schools is shown in Figure FIGREF4   Here, the lengthy text on the left-hand side is transformed into an argument gist on the right-hand side by (i) analyzing argument components and (ii) summarizing their content  Figure FIGREF5 shows another use-case example, in which users search for reasons that underpin certain standpoint in a given controversy (which is homeschooling in this case)  In general, the output of automatic argument analysis performed on the large scale in Web data can provide users with analyzed arguments to a given topic of interest, find the evidence for the given controversial standpoint, or help to reveal flaws in argumentation of others 
Satisfying the above-mentioned information needs cannot be directly tackled by current methods for, e g , opinion mining, questions answering, or summarization and requires novel approaches within the argumentation mining field  Although user-generated Web content has already been considered in argumentation mining, many limitations and research gaps can be identified in the existing works  First, the scope of the current approaches is restricted to a particular domain or register, e g , hotel reviews BIBREF5 , Tweets related to local riot events BIBREF6 , student essays BIBREF7 , airline passenger rights and consumer protection BIBREF8 , or renewable energy sources BIBREF9   Second, not all the related works are tightly connected to argumentation theories, resulting into a gap between the substantial research in argumentation itself and its adaptation in NLP applications  Third, as an emerging research area, argumentation mining still suffers from a lack of labeled corpora, which is crucial for designing, training, and evaluating the algorithms  Although some works have dealt with creating new data sets, the reliability (in terms of inter-annotator agreement) of the annotated resources is often unknown BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14  
Annotating and automatically analyzing arguments in unconstrained user-generated Web discourse represent challenging tasks  So far, the research in argumentation mining “has been conducted on domains like news articles, parliamentary records and legal documents, where the documents contain well-formed explicit arguments, i e , propositions with supporting reasons and evidence present in the text” BIBREF8   [p  50]Boltuzic Snajder 2014 point out that “unlike in debates or other more formal argumentation sources, the arguments provided by the users, if any, are less formal, ambiguous, vague, implicit, or often simply poorly worded ” Another challenge stems from the different nature of argumentation theories and computational linguistics  Whereas computational linguistics is mainly descriptive, the empirical research that is carried out in argumentation theories does not constitute a test of the theoretical model that is favored, because the model of argumentation is a normative instrument for assessing the argumentation BIBREF15   So far, no fully fledged descriptive argumentation theory based on empirical research has been developed, thus feasibility of adapting argumentation models to the Web discourse represents an open issue 
These challenges can be formulated into the following research questions:
In this article, we push the boundaries of the argumentation mining field by focusing on several novel aspects  We tackle the above-mentioned research questions as well as the previously discussed challenges and issues  First, we target user-generated Web discourse from several domains across various registers, to examine how argumentation is communicated in different contexts  Second, we bridge the gap between argumentation theories and argumentation mining through selecting the argumenation model based on research into argumentation theories and related fields in communication studies or psychology  In particular, we adapt normative models from argumentation theory to perform empirical research in NLP and support our application of argumentation theories with an in-depth reliability study  Finally, we use state-of-the-art NLP techniques in order to build robust computational models for analyzing arguments that are capable of dealing with a variety of genres on the Web 
Our contributions
We create a new corpus which is, to the best of our knowledge, the largest corpus that has been annotated within the argumentation mining field to date  We choose several target domains from educational controversies, such as homeschooling, single-sex education, or mainstreaming  A novel aspect of the corpus is its coverage of different registers of user-generated Web content, such as comments to articles, discussion forum posts, blog posts, as well as professional newswire articles 
Since the data come from a variety of sources and no assumptions about its actual content with respect to argumentation can be drawn, we conduct two extensive annotation studies  In the first study, we tackle the problem of relatively high “noise” in the retrieved data  In particular, not all of the documents are related to the given topics in a way that makes them candidates for further deep analysis of argumentation (this study results into 990 annotated documents)  In the second study, we discuss the selection of an appropriate argumentation model based on evidence in argumentation research and propose a model that is suitable for analyzing micro-level argumention in user-generated Web content  Using this model, we annotate 340 documents (approx  90,000 tokens), reaching a substantial inter-annotator agreement  We provide a hand-analysis of all the phenomena typical to argumentation that are prevalent in our data  These findings may also serve as empirical evidence to issues that are on the spot of current argumentation research 
From the computational perspective, we experiment on the annotated data using various machine learning methods in order to extract argument structure from documents  We propose several novel feature sets and identify configurations that run best in in-domain and cross-domain scenarios  To foster research in the community, we provide the annotated data as well as all the experimental software under free license 
The rest of the article is structured as follows  First, we provide an essential background in argumentation theory in section SECREF2   Section SECREF3 surveys related work in several areas  Then we introduce the dataset and two annotation studies in section SECREF4   Section SECREF5 presents our experimental work and discusses the results and errors and section SECREF6 concludes this article 
Theoretical background
Let us first present some definitions of the term argumentation itself  [p  3]Ketcham 1917 defines argumentation as “the art of persuading others to think or act in a definite way  It includes all writing and speaking which is persuasive in form ” According to MacEwan 1898, “argumentation is the process of proving or disproving a proposition  Its purpose is to induce a new belief, to establish truth or combat error in the mind of another ” [p  2]Freeley Steinberg 2008 narrow the scope of argumentation to “reason giving in communicative situations by people whose purpose is the justification of acts, beliefs, attitudes, and values ” Although these definitions vary, the purpose of argumentation remains the same – to persuade others 
We would like to stress that our perception of argumentation goes beyond somehow limited giving reasons BIBREF17 , BIBREF18   Rather, we see the goal of argumentation as to persuade BIBREF19 , BIBREF20 , BIBREF21   Persuasion can be defined as a successful intentional effort at influencing another's mental state through communication in a circumstance in which the persuadee has some measure of freedom BIBREF22 , although, as OKeefe2011 points out, there is no correct or universally-endorsed definition of either `persuasion' or `argumentation'  However, broader understanding of argumentation as a means of persuasion allows us to take into account not only reasoned discourse, but also non-reasoned mechanisms of influence, such as emotional appeals BIBREF23  
Having an argument as a product within the argumentation process, we should now define it  One typical definition is that an argument is a claim supported by reasons BIBREF24   The term claim has been used since 1950's, introduced by Toulmin 1958, and in argumentation theory it is a synonym for standpoint or point of view  It refers to what is an issue in the sense what is being argued about  The presence of a standpoint is thus crucial for argumentation analysis  However, the claim as well as other parts of the argument might be implicit; this is known as enthymematic argumentation, which is rather usual in ordinary argumentative discourse BIBREF25  
One fundamental problem with the definition and formal description of arguments and argumentation is that there is no agreement even among argumentation theorists  As [p  29]vanEmeren et al 2014 admit in their very recent and exhaustive survey of the field, ”as yet, there is no unitary theory of argumentation that encompasses the logical, dialectical, and rhetorical dimensions of argumentation and is universally accepted  The current state of the art in argumentation theory is characterized by the coexistence of a variety of theoretical perspectives and approaches, which differ considerably from each other in conceptualization, scope, and theoretical refinement ”
Argumentation models
Despite the missing consensus on the ultimate argumentation theory, various argumentation models have been proposed that capture argumentation on different levels  Argumentation models abstract from the language level to a concept level that stresses the links between the different components of an argument or how arguments relate to each other BIBREF26   Bentahar et al 2010 propose a taxonomy of argumentation models, that is horizontally divided into three categories – micro-level models, macro-level models, and rhetorical models 
In this article, we deal with argumentation on the micro-level (also called argumentation as a product or monological models)  Micro-level argumentation focuses on the structure of a single argument  By contrast, macro-level models (also called dialogical models) and rhetorical models highlight the process of argumentation in a dialogue BIBREF27   In other words, we examine the structure of a single argument produced by a single author in term of its components, not the relations that can exist among arguments and their authors in time  A detailed discussion of these different perspectives can be found, e g , in BIBREF28 , BIBREF29 , BIBREF30 , BIBREF1 , BIBREF31 , BIBREF32  
Dimensions of argument
The above-mentioned models focus basically only on one dimension of the argument, namely the logos dimension  According to the classical Aristotle's theory BIBREF0 , argument can exist in three dimensions, which are logos, pathos, and ethos  Logos dimension represents a proof by reason, an attempt to persuade by establishing a logical argument  For example, syllogism belongs to this argumentation dimension BIBREF34 , BIBREF25   Pathos dimension makes use of appealing to emotions of the receiver and impacts its cognition BIBREF35   Ethos dimension of argument relies on the credibility of the arguer  This distinction will have practical impact later in section SECREF51 which deals with argumentation on the Web 
Original Toulmin's model
We conclude the theoretical section by presenting one (micro-level) argumentation model in detail – a widely used conceptual model of argumentation introduced by Toulmin 1958, which we will henceforth denote as the Toulmin's original model  This model will play an important role later in the annotation studies (section SECREF51 ) and experimental work (section SECREF108 )  The model consists of six parts, referred as argument components, where each component plays a distinct role 
is an assertion put forward publicly for general acceptance BIBREF38 or the conclusion we seek to establish by our arguments BIBREF17  
It is the evidence to establish the foundation of the claim BIBREF24 or, as simply put by Toulmin, “the data represent what we have to go on ” BIBREF37   The name of this concept was later changed to grounds in BIBREF38  
The role of warrant is to justify a logical inference from the grounds to the claim 
is a set of information that stands behind the warrant, it assures its trustworthiness 
limits the degree of certainty under which the argument should be accepted  It is the degree of force which the grounds confer on the claim in virtue of the warrant BIBREF37  
presents a situation in which the claim might be defeated 
A schema of the Toulmin's original model is shown in Figure FIGREF29   The lines and arrows symbolize implicit relations between the components  An example of an argument rendered using the Toulmin's scheme can be seen in Figure FIGREF30  
We believe that this theoretical overview should provide sufficient background for the argumentation mining research covered in this article; for further references, we recommend for example BIBREF15  
Related work in computational linguistics
We structure the related work into three sub-categories, namely argumentation mining, stance detection, and persuasion and on-line dialogs, as these areas are closest to this article's focus  For a recent overview of general discourse analysis see BIBREF39   Apart from these, research on computer-supported argumentation has been also very active; see, e g , BIBREF40 for a survey of various models and argumentation formalisms from the educational perspective or BIBREF41 which examines argumentation in the Semantic Web 
Argumentation Mining
The argumentation mining field has been evolving very rapidly in the recent years, resulting into several workshops co-located with major NLP conferences  We first present related works with a focus on annotations and then review experiments with classifying argument components, schemes, or relations 
One of the first papers dealing with annotating argumentative discourse was Argumentative Zoning for scientific publications BIBREF42   Later, Teufel et al 2009 extended the original 7 categories to 15 and annotated 39 articles from two domains, where each sentence is assigned a category  The obtained Fleiss' INLINEFORM0 was 0 71 and 0 65  In their approach, they tried to deliberately ignore the domain knowledge and rely only on general, rhetorical and logical aspect of the annotated texts  By contrast to our work, argumentative zoning is specific to scientific publications and has been developed solely for that task 
Reed Rowe 2004 presented Araucaria, a tool for argumentation diagramming which supports both convergent and linked arguments, missing premises (enthymemes), and refutations  They also released the AracuariaDB corpus which has later been used for experiments in the argumentation mining field  However, the creation of the dataset in terms of annotation guidelines and reliability is not reported – these limitations as well as its rather small size have been identified BIBREF10  
Biran Rambow 2011 identified justifications for subjective claims in blog threads and Wikipedia talk pages  The data were annotated with claims and their justifications reaching INLINEFORM0 0 69, but a detailed description of the annotation approach was missing 
[p  1078]Schneider et al 2013b annotated Wikipedia talk pages about deletion using 17 Walton's schemes BIBREF43 , reaching a moderate agreement (Cohen's INLINEFORM0 0 48) and concluded that their analysis technique can be reused, although “it is intensive and difficult to apply ”
Stab Gurevych 2014 annotated 90 argumentative essays (about 30k tokens), annotating claims, major claims, and premises and their relations (support, attack)  They reached Krippendorff's INLINEFORM0 0 72 for argument components and Krippendorff's INLINEFORM1 0 81 for relations between components 
Rosenthal2012 annotated sentences that are opinionated claims, in which the author expresses a belief that should be adopted by others  Two annotators labeled sentences as claims without any context and achieved Cohen's INLINEFORM0 0 50 (2,000 sentences from LiveJournal) and 0 56 (2,000 sentences from Wikipedia) 
Aharoni et al 2014 performed an annotation study in order to find context-dependent claims and three types of context-dependent evidence in Wikipedia, that were related to 33 controversial topics  The claim and evidence were annotated in 104 articles  The average Cohen's INLINEFORM0 between a group of 20 expert annotators was 0 40  Compared to our work, the linguistic properties of Wikipedia are qualitatively different from other user-generated content, such as blogs or user comments BIBREF44  
Wacholder et al 2014 annotated “argument discourse units” in blog posts and criticized the Krippendorff's INLINEFORM0 measure  They proposed a new inter-annotator metric by taking the most overlapping part of one annotation as the “core” and all annotations as a “cluster”  The data were extended by Ghosh2014, who annotated “targets” and “callouts” on the top of the units 
Park Cardie 2014 annotated about 10k sentences from 1,047 documents into four types of argument propositions with Cohen's INLINEFORM0 0 73 on 30% of the dataset  Only 7% of the sentences were found to be non-argumentative 
Faulkner2014 used Amazon Mechanical Turk to annotate 8,179 sentences from student essays  Three annotators decided whether the given sentence offered reasons for or against the main prompt of the essay (or no reason at all; 66% of the sentences were found to be neutral and easy to identify)  The achieved Cohen's INLINEFORM0 was 0 70 
The research has also been active on non-English datasets  Goudas et al 2014 focused on user-generated Greek texts  They selected 204 documents and manually annotated sentences that contained an argument (760 out of 16,000)  They distinguished claims and premises, but the claims were always implicit  However, the annotation agreement was not reported, neither was the number of annotators or the guidelines  A study on annotation of arguments was conducted by Peldszus Stede 2013, who evaluate agreement among 26 “naive" annotators (annotators with very little training)  They manually constructed 23 German short texts, each of them contains exactly one central claim, two premises, and one objection (rebuttal or undercut) and analyzed annotator agreement on this artificial data set  Peldszus 2014 later achieved higher inter-rater agreement with expert annotators on an extended version of the same data  Kluge 2014 built a corpus of argumentative German Web documents, containing 79 documents from 7 educational topics, which were annotated by 3 annotators according to the claim-premise argumentation model  The corpus comprises 70,000 tokens and the inter-annotator agreement was 0 40 (Krippendorff's INLINEFORM0 )  Houy et al 2013 targeted argumentation mining of German legal cases 
Table TABREF33 gives an overview of annotation studies with their respective argumentation model, domain, size, and agreement  It also contains other studies outside of computational linguistics and few proposals and position papers 
Arguments in the legal domain were targeted in BIBREF11   Using argumentation formalism inspired by Walton 2012, they employed multinomial Naive Bayes classifier and maximum entropy model for classifying argumentative sentences on the AraucariaDB corpus BIBREF45   The same test dataset was used by Feng Hirst 2011, who utilized the C4 5 decision classifier  Rooney et al 2012 investigated the use of convolution kernel methods for classifying whether a sentence belongs to an argumentative element or not using the same corpus 
Stab Gurevych 2014b classified sentences to four categories (none, major claim, claim, premise) using their previously annotated corpus BIBREF7 and reached 0 72 macro- INLINEFORM0 score  In contrast to our work, their documents are expected to comply with a certain structure of argumentative essays and are assumed to always contain argumentation 
Biran Rambow 2011 identified justifications on the sentence level using a naive Bayes classifier over a feature set based on statistics from the RST Treebank, namely n-grams which were manually processed by deleting n-grams that “seemed irrelevant, ambiguous or domain-specific ”
Llewellyn2014 experimented with classifying tweets into several argumentative categories, namely claims and counter-claims (with and without evidence) and verification inquiries previously annotated by Procter et al 2013  They used unigrams, punctuations, and POS as features in three classifiers 
Park Cardie 2014 classified propositions into three classes (unverifiable, verifiable non-experimental, and verifiable experimental) and ignored non-argumentative texts  Using multi-class SVM and a wide range of features (n-grams, POS, sentiment clue words, tense, person) they achieved Macro INLINEFORM0 0 69 
Peldszus 2014 experimented with a rather complex labeling schema of argument segments, but their data were artificially created for their task and manually cleaned, such as removing segments that did not meet the criteria or non-argumentative segments 
In the first step of their two-phase approach, Goudas et al 2014 sampled the dataset to be balanced and identified argumentative sentences with INLINEFORM0 0 77 using the maximum entropy classifier  For identifying premises, they used BIO encoding of tokens and achieved INLINEFORM1 score 0 42 using CRFs 
Saint-Dizier 2012 developed a Prolog engine using a lexicon of 1300 words and a set of 78 hand-crafted rules with the focus on a particular argument structure “reasons supporting conclusions” in French 
Taking the dialogical perspective, Cabrio Villata 2012 built upon an argumentation framework proposed by Dung 1995 which models arguments within a graph structure and provides a reasoning mechanism for resolving accepted arguments  For identifying support and attack, they relied on existing research on textual entailment BIBREF46 , namely using the off-the-shelf EDITS system  The test data were taken from a debate portal Debatepedia and covered 19 topics  Evaluation was performed in terms of measuring the acceptance of the “main argument" using the automatically recognized entailments, yielding INLINEFORM0 score about 0 75  By contrast to our work which deals with micro-level argumentation, the Dung's model is an abstract framework intended to model dialogical argumentation 
Finding a bridge between existing discourse research and argumentation has been targeted by several researchers  Peldszus2013a surveyed literature on argumentation and proposed utilization of Rhetorical Structure Theory (RST) BIBREF47   They claimed that RST is by its design well-suited for studying argumentative texts, but an empirical evidence has not yet been provided  Penn Discourse Tree Bank (PDTB) BIBREF48 relations have been under examination by argumentation mining researchers too  Cabrio2013b examined a connection between five Walton's schemes and discourse markers in PDTB, however an empirical evaluation is missing 
Stance detection
Research related to argumentation mining also involves stance detection  In this case, the whole document (discussion post, article) is assumed to represent the writer's standpoint to the discussed topic  Since the topic is stated as a controversial question, the author is either for or against it 
Somasundaran Wiebe 2009 built a computational model for recognizing stances in dual-topic debates about named entities in the electronic products domain by combining preferences learned from the Web data and discourse markers from PDTB BIBREF48   Hasan Ng 2013 determined stance in on-line ideological debates on four topics using data from createdebate com, employing supervised machine learning and features ranging from n-grams to semantic frames  Predicting stance of posts in Debatepedia as well as external articles using a probabilistic graphical model was presented in BIBREF49   This approach also employed sentiment lexicons and Named Entity Recognition as a preprocessing step and achieved accuracy about 0 80 in binary prediction of stances in debate posts 
Recent research has involved joint modeling, taking into account information about the users, the dialog sequences, and others  Hasan Ng 2012 proposed machine learning approach to debate stance classification by leveraging contextual information and author's stances towards the topic  Qiu et al 2013 introduced a computational debate side model to cluster posts or users by sides for general threaded discussions using a generative graphical model employing words from various subjectivity lexicons as well as all adjectives and adverbs in the posts  Qiu Jiang 2013 proposed a graphical model for viewpoint discovery in discussion threads  Burfoot et al 2011 exploited the informal citation structure in U S  Congressional floor-debate transcripts and use a collective classification which outperforms methods that consider documents in isolation 
Some works also utilize argumentation-motivated features  Park et al 2011 dealt with contentious issues in Korean newswire discourse  Although they annotate the documents with “argument frames”, the formalism remains unexplained and does not refer to any existing research in argumentation  Walker et al 2012b incorporated features with some limited aspects of the argument structure, such as cue words signaling rhetorical relations between posts, POS generalized dependencies, and a representation of the parent post (context) to improve stance classification over 14 topics from convinceme net 
Online persuasion
Another stream of research has been devoted to persuasion in online media, which we consider as a more general research topic than argumentation 
Schlosser 2011 investigated persuasiveness of online reviews and concluded that presenting two sides is not always more helpful and can even be less persuasive than presenting one side  Mohammadi et al 2013 explored persuasiveness of speakers in YouTube videos and concluded that people are perceived more persuasive in video than in audio and text  Miceli et al 2006 proposed a computational model that attempts to integrate emotional and non-emotional persuasion  In the study of Murphy 2001, persuasiveness was assigned to 21 articles (out of 100 manually preselected) and four of them are later analyzed in detail for comparing the perception of persuasion between expert and students  Bernard et al 2012 experimented with children's perception of discourse connectives (namely with “because”) to link statements in arguments and found out that 4- and 5-years-old and adults are sensitive to the connectives  Le 2004 presented a study of persuasive texts and argumentation in newspaper editorials in French 
A coarse-grained view on dialogs in social media was examined by Bracewell et al 2013, who proposed a set of 15 social acts (such as agreement, disagreement, or supportive behavior) to infer the social goals of dialog participants and presented a semi-supervised model for their classification  Their social act types were inspired by research in psychology and organizational behavior and were motivated by work in dialog understanding  They annotated a corpus in three languages using in-house annotators and achieved INLINEFORM0 in the range from 0 13 to 0 53 
Georgila et al 2011 focused on cross-cultural aspects of persuasion or argumentation dialogs  They developed a novel annotation scheme stemming from different literature sources on negotiation and argumentation as well as from their original analysis of the phenomena  The annotation scheme is claimed to cover three dimensions of an utterance, namely speech act, topic, and response or reference to a previous utterance  They annotated 21 dialogs and reached Krippendorff's INLINEFORM0 between 0 38 and 0 57 
Given the broad landscape of various approaches to argument analysis and persuasion studies presented in this section, we would like to stress some novel aspects of the current article  First, we aim at adapting a model of argument based on research by argumentation scholars, both theoretical and empirical  We pose several pragmatical constraints, such as register independence (generalization over several registers)  Second, our emphasis is put on reliable annotations and sufficient data size (about 90k tokens)  Third, we deal with fairly unrestricted Web-based sources, so additional steps of distinguishing whether the texts are argumentative are required  Argumentation mining has been a rapidly evolving field with several major venues in 2015  We encourage readers to consult an upcoming survey article by Lippi Torroni 2016 or the proceedings of the 2nd Argumentation Mining workshop BIBREF50 to keep up with recent developments  However, to the best of our knowledge, the main findings of this article have not yet been made obsolete by any related work 
Annotation studies and corpus creation
This section describes the process of data selection, annotation, curation, and evaluation with the goal of creating a new corpus suitable for argumentation mining research in the area of computational linguistics  As argumentation mining is an evolving discipline without established and widely-accepted annotation schemes, procedures, and evaluation, we want to keep this overview detailed to ensure full reproducibility of our approach  Given the wide range of perspectives on argumentation itself BIBREF15 , variety of argumentation models BIBREF27 , and high costs of discourse or pragmatic annotations BIBREF48 , creating a new, reliable corpus for argumentation mining represents a substantial effort 
A motivation for creating a new corpus stems from the various use-cases discussed in the introduction, as well as some research gaps pointed in section SECREF1 and further discussed in the survey in section SECREF31 (e g , domain restrictions, missing connection to argumentation theories, non-reported reliability or detailed schemes) 
Topics and registers
As a main field of interest in the current study, we chose controversies in education  One distinguishing feature of educational topics is their breadth of sub-topics and points of view, as they attract researchers, practitioners, parents, students, or policy-makers  We assume that this diversity leads to the linguistic variability of the education topics and thus represents a challenge for NLP  In a cooperation with researchers from the German Institute for International Educational Research we identified the following current controversial topics in education in English-speaking countries: (1) homeschooling, (2) public versus private schools, (3) redshirting — intentionally delaying the entry of an age-eligible child into kindergarten, allowing their child more time to mature emotionally and physically BIBREF51 , (4) prayer in schools — whether prayer in schools should be allowed and taken as a part of education or banned completely, (5) single-sex education — single-sex classes (males and females separate) versus mixed-sex classes (“co-ed”), and (6) mainstreaming — including children with special needs into regular classes 
Since we were also interested in whether argumentation differs across registers, we included four different registers — namely (1) user comments to newswire articles or to blog posts, (2) posts in discussion forums (forum posts), (3) blog posts, and (4) newswire articles  Throughout this work, we will refer to each article, blog post, comment, or forum posts as a document  This variety of sources covers mainly user-generated content except newswire articles which are written by professionals and undergo an editing procedure by the publisher  Since many publishers also host blog-like sections on their portals, we consider as blog posts all content that is hosted on personal blogs or clearly belong to a blog category within a newswire portal 
Raw corpus statistics
Given the six controversial topics and four different registers, we compiled a collection of plain-text documents, which we call the raw corpus  It contains 694,110 tokens in 5,444 documents  As a coarse-grained analysis of the data, we examined the lengths and the number of paragraphs (see Figure FIGREF43 )  Comments and forum posts follow a similar distribution, being shorter than 300 tokens on average  By contrast, articles and blogs are longer than 400 tokens and have 9 2 paragraphs on average  The process of compiling the raw corpus and its further statistics are described in detail in Appendix UID158  
Annotation study 1: Identifying persuasive documents in forums and comments
The goal of this study was to select documents suitable for a fine-grained analysis of arguments  In a preliminary study on annotating argumentation using a small sample (50 random documents) of forum posts and comments from the raw corpus, we found that many documents convey no argumentation at all, even in discussions about controversies  We observed that such contributions do not intend to persuade; these documents typically contain story-sharing, personal worries, user interaction (asking questions, expressing agreement), off-topic comments, and others  Such characteristics are typical to on-line discussions in general, but they have not been examined with respect to argumentation or persuasion  Indeed, we observed that there are (1) documents that are completely unrelated and (2) documents that are related to the topic, but do not contain any argumentation  This issue has been identified among argumentation theorist; for example as external relevance by Paglieri Castelfranchia 2014  Similar findings were also confirmed in related literature in argumentation mining, however never tackled empirically BIBREF53 , BIBREF8 These documents are thus not suitable for analyzing argumentation 
In order to filter documents that are suitable for argumentation annotation, we defined a binary document-level classification task  The distinction is made between either persuasive documents or non-persuasive (which includes all other sorts of texts, such as off-topic, story sharing, unrelated dialog acts, etc ) 
The two annotated categories were on-topic persuasive and non-persuasive  Three annotators with near-native English proficiency annotated a set of 990 documents (a random subset of comments and forum posts) reaching 0 59 Fleiss' INLINEFORM0   The final label was selected by majority voting  The annotation study took on average of 15 hours per annotator with approximately 55 annotated documents per hour  The resulting labels were derived by majority voting  Out of 990 documents, 524 (53%) were labeled as on-topic persuasive  We will refer to this corpus as gold data persuasive 
We examined all disagreements between annotators and discovered some typical problems, such as implicitness or topic relevance  First, the authors often express their stance towards the topic implicitly, so it must be inferred by the reader  To do so, certain common-ground knowledge is required  However, such knowledge heavily depends on many aspects, such as the reader's familiarity with the topic or her cultural background, as well as the context of the source website or the discussion forum thread  This also applies for sarcasm and irony  Second, the decision whether a particular topic is persuasive was always made with respect to the controversial topic under examination  Some authors shift the focus to a particular aspect of the given controversy or a related issue, making the document less relevant 
We achieved moderate agreement between the annotators, although the definition of persuasiveness annotation might seem a bit fuzzy  We found different amounts of persuasion in the specific topics  For instance, prayer in schools or private vs  public schools attract persuasive discourse, while other discussed controversies often contain non-persuasive discussions, represented by redshirting and mainstreaming  Although these two topics are also highly controversial, the participants of on-line discussions seem to not attempt to persuade but they rather exchange information, support others in their decisions, etc  This was also confirmed by socio-psychological researchers  Ammari et al 2014 show that parents of children with special needs rely on discussion sites for accessing information and social support and that, in particular, posts containing humor, achievement, or treatment suggestions are perceived to be more socially appropriate than posts containing judgment, violence, or social comparisons  According to Nicholson Leask 2012, in the online forum, parents of autistic children were seen to understand the issue because they had lived it  Assuming that participants in discussions related to young kids (e g , redshirting, or mainstreaming) are usually females (mothers), the gender can also play a role  In a study of online persuasion, Guadagno Cialdini 2002 conclude that women chose to bond rather than compete (women feel more comfortable cooperating, even in a competitive environment), whereas men are motivated to compete if necessary to achieve independence 
Annotation study 2: Annotating micro-structure of arguments
The goal of this study was to annotate documents on a detailed level with respect to an argumentation model  First, we will present the annotation scheme  Second, we will describe the annotation process  Finally, we will evaluate the agreement and draw some conclusions 
Given the theoretical background briefly introduced in section SECREF2 , we motivate our selection of the argumentation model by the following requirements  First, the scope of this work is to capture argumentation within a single document, thus focusing on micro-level models  Second, there should exist empirical evidence that such a model has been used for analyzing argumentation in previous works, so it is likely to be suitable for our purposes of argumentative discourse analysis in user-generated content  Regarding the first requirement, two typical examples of micro-level models are the Toulmin's model BIBREF36 and Walton's schemes BIBREF55   Let us now elaborate on the second requirement 
Walton's argumentation schemes are claimed to be general and domain independent  Nevertheless, evidence from the computational linguistics field shows that the schemes lack coverage for analyzing real argumentation in natural language texts  In examining real-world political argumentation from BIBREF56 , Walton 2012 found out that 37 1% of the arguments collected did not fit any of the fourteen schemes they chose so they created new schemes ad-hoc  Cabrio2013b selected five argumentation schemes from Walton and map these patterns to discourse relation categories in the Penn Discourse TreeBank (PDTB) BIBREF48 , but later they had to define two new argumentation schemes that they discovered in PDTB  Similarly, Song et al 2014 admitted that the schemes are ambiguous and hard to directly apply for annotation, therefore they modified the schemes and created new ones that matched the data 
Although Macagno Konstantinidou 2012 show several examples of two argumentation schemes applied to few selected arguments in classroom experiments, empirical evidence presented by Anthony Kim 2014 reveals many practical and theoretical difficulties of annotating dialogues with schemes in classroom deliberation, providing many details on the arbitrary selection of the sub-set of the schemes, the ambiguity of the scheme definitions, concluding that the presence of the authors during the experiment was essential for inferring and identifying the argument schemes BIBREF57  
Although this model (refer to section SECREF21 ) was designed to be applicable to real-life argumentation, there are numerous studies criticizing both the clarity of the model definition and the differentiation between elements of the model  Ball1994 claims that the model can be used only for the most simple arguments and fails on the complex ones  Also Freeman1991 and other argumentation theorists criticize the usefulness of Toulmin's framework for the description of real-life argumentative texts  However, others have advocated the model and claimed that it can be applied to the people's ordinary argumenation BIBREF58 , BIBREF59  
A number of studies (outside the field of computational linguistics) used Toulmin's model as their backbone argumentation framework  Chambliss1995 experimented with analyzing 20 written documents in a classroom setting in order to find the argument patterns and parts  Simosi2003 examined employees' argumentation to resolve conflicts  Voss2006 analyzed experts' protocols dealing with problem-solving 
The model has also been used in research on computer-supported collaborative learning  Erduran2004 adapt Toulmin's model for coding classroom argumentative discourse among teachers and students  Stegmann2011 builds on a simplified Toulmin's model for scripted construction of argument in computer-supported collaborative learning  Garcia-Mila2013 coded utterances into categories from Toulmin's model in persuasion and consensus-reaching among students  Weinberger Fischer 2006 analyze asynchronous discussion boards in which learners engage in an argumentative discourse with the goal to acquire knowledge  For coding the argument dimension, they created a set of argumentative moves based on Toulmin's model  Given this empirical evidence, we decided to build upon the Toulmin's model 
In this annotation task, a sequence of tokens (e g  a phrase, a sentence, or any arbitrary text span) is labeled with a corresponding argument component (such as the claim, the grounds, and others)  There are no explicit relations between these annotation spans as the relations are implicitly encoded in the pragmatic function of the components in the Toulmin's model 
In order to prove the suitability of the Toulmin's model, we analyzed 40 random documents from the gold data persuasive dataset using the original Toulmin's model as presented in section SECREF21   We took into account sever criteria for assessment, such as frequency of occurrence of the components or their importance for the task  We proposed some modifications of the model based on the following observations 
Authors do not state the degree of cogency (the probability of their claim, as proposed by Toulmin)  Thus we omitted qualifier from the model due to its absence in the data 
The warrant as a logical explanation why one should accept the claim given the evidence is almost never stated  As pointed out by BIBREF37 , “data are appealed to explicitly, warrants implicitly ” This observation has also been made by Voss2006  Also, according to [p  205]Eemeren et al 1987, the distinction of warrant is perfectly clear only in Toulmin’s examples, but the definitions fail in practice  We omitted warrant from the model 
Rebuttal is a statement that attacks the claim, thus playing a role of an opposing view  In reality, the authors often attack the presented rebuttals by another counter-rebuttal in order to keep the whole argument's position consistent  Thus we introduced a new component – refutation – which is used for attacking the rebuttal  Annotation of refutation was conditioned of explicit presence of rebuttal and enforced by the annotation guidelines  The chain rebuttal–refutation is also known as the procatalepsis figure in rhetoric, in which the speaker raises an objection to his own argument and then immediately answers it  By doing so, the speaker hopes to strengthen the argument by dealing with possible counter-arguments before the audience can raise them BIBREF43  
The claim of the argument should always reflect the main standpoint with respect to the discussed controversy  We observed that this standpoint is not always explicitly expressed, but remains implicit and must be inferred by the reader  Therefore, we allow the claim to be implicit  In such a case, the annotators must explicitly write down the (inferred) stance of the author 
By definition, the Toulmin's model is intended to model single argument, with the claim in its center  However, we observed in our data, that some authors elaborate on both sides of the controversy equally and put forward an argument for each side (by argument here we mean the claim and its premises, backings, etc )  Therefore we allow multiple arguments to be annotated in one document  At the same time, we restrained the annotators from creating complex argument hierarchies 
Toulmin's grounds have an equivalent role to a premise in the classical view on an argument BIBREF15 , BIBREF60 in terms that they offer the reasons why one should accept the standpoint expressed by the claim  As this terminology has been used in several related works in the argumentation mining field BIBREF7 , BIBREF61 , BIBREF62 , BIBREF11 , we will keep this convention and denote the grounds as premises 
One of the main critiques of the original Toulmin's model was the vague distinction between grounds, warrant, and backing BIBREF63 , BIBREF64 , BIBREF65   The role of backing is to give additional support to the warrant, but there is no warrant in our model anymore  However, what we observed during the analysis, was a presence of some additional evidence  Such evidence does not play the role of the grounds (premises) as it is not meant as a reason supporting the claim, but it also does not explain the reasoning, thus is not a warrant either  It usually supports the whole argument and is stated by the author as a certain fact  Therefore, we extended the scope of backing as an additional support to the whole argument 
The annotators were instructed to distinguish between premises and backing, so that premises should cover generally applicable reasons for the claim, whereas backing is a single personal experience or statements that give credibility or attribute certain expertise to the author  As a sanity check, the argument should still make sense after removing backing (would be only considered “weaker”) 
We call the model as a modified Toulmin's model  It contains five argument components, namely claim, premise, backing, rebuttal, and refutation  When annotating a document, any arbitrary token span can be labeled with an argument component; the components do not overlap  The spans are not known in advance and the annotator thus chooses the span and the component type at the same time  All components are optional (they do not have to be present in the argument) except the claim, which is either explicit or implicit (see above)  If a token span is not labeled by any argument component, it is not considered as a part of the argument and is later denoted as none (this category is not assigned by the annotators) 
An example analysis of a forum post is shown in Figure FIGREF65   Figure FIGREF66 then shows a diagram of the analysis from that example (the content of the argument components was shortened or rephrased) 
The annotation experiment was split into three phases  All documents were annotated by three independent annotators, who participated in two training sessions  During the first phase, 50 random comments and forum posts were annotated  Problematic cases were resolved after discussion and the guidelines were refined  In the second phase, we wanted to extend the range of annotated registers, so we selected 148 comments and forum posts as well as 41 blog posts  After the second phase, the annotation guidelines were final 
In the final phase, we extended the range of annotated registers and added newswire articles from the raw corpus in order to test whether the annotation guidelines (and inherently the model) is general enough  Therefore we selected 96 comments/forum posts, 8 blog posts, and 8 articles for this phase  A detailed inter-annotator agreement study on documents from this final phase will be reported in section UID75  
The annotations were very time-consuming  In total, each annotator spent 35 hours by annotating in the course of five weeks  Discussions and consolidation of the gold data took another 6 hours  Comments and forum posts required on average of 4 minutes per document to annotate, while blog posts and articles on average of 14 minutes per document  Examples of annotated documents from the gold data are listed in Appendix UID158  
We discarded 11 documents out of the total 351 annotated documents  Five forum posts, although annotated as persuasive in the first annotation study, were at a deeper look a mixture of two or more posts with missing quotations, therefore unsuitable for analyzing argumentation  Three blog posts and two articles were found not to be argumentative (the authors took no stance to the discussed controversy) and one article was an interview, which the current model cannot capture (a dialogical argumentation model would be required) 
For each of the 340 documents, the gold standard annotations were obtained using the majority vote  If simple majority voting was not possible (different boundaries of the argument component together with a different component label), the gold standard was set after discussion among the annotators  We will refer to this corpus as the gold standard Toulmin corpus  The distribution of topics and registers in this corpus in shown in Table TABREF71 , and Table TABREF72 presents some lexical statistics 
Based on pre-studies, we set the minimal unit for annotation as token  The documents were pre-segmented using the Stanford Core NLP sentence splitter BIBREF69 embedded in the DKPro Core framework BIBREF70   Annotators were asked to stick to the sentence level by default and label entire pre-segmented sentences  They should switch to annotations on the token level only if (a) a particular sentence contained more than one argument component, or (b) if the automatic sentence segmentation was wrong  Given the “noise” in user-generated Web data (wrong or missing punctuation, casing, etc ), this was often the case 
Annotators were also asked to rephrase (summarize) each annotated argument component into a simple statement when applicable, as shown in Figure FIGREF66   This was used as a first sanity checking step, as each argument component is expected to be a coherent discourse unit  For example, if a particular occurrence of a premise cannot be summarized/rephrased into one statement, this may require further splitting into two or more premises 
For the actual annotations, we developed a custom-made web-based application that allowed users to switch between different granularity of argument components (tokens or sentences), to annotate the same document in different argument “dimensions” (logos and pathos), and to write summary for each annotated argument component 
As a measure of annotation reliability, we rely on Krippendorff's unitized alpha ( INLINEFORM0 ) BIBREF71   To the best of our knowledge, this is the only agreement measure that is applicable when both labels and boundaries of segments are to be annotated 
Although the measure has been used in related annotation works BIBREF61 , BIBREF7 , BIBREF72 , there is one important detail that has not been properly communicated  The INLINEFORM0 is computed over a continuum of the smallest units, such as tokens  This continuum corresponds to a single document in the original Krippendorff's work  However, there are two possible extensions to multiple documents (a corpus), namely (a) to compute INLINEFORM1 for each document first and then report an average value, or (b) to concatenate all documents into one large continuum and compute INLINEFORM2 over it  The first approach with averaging yielded extremely high the standard deviation of INLINEFORM3 (i e , avg  = 0 253; std  dev  = 0 886; median = 0 476 for the claim)  This says that some documents are easy to annotate while others are harder, but interpretation of such averaged value has no evidence either in BIBREF71 or other papers based upon it  Thus we use the other methodology and treat the whole corpus as a single long continuum (which yields in the example of claim 0 541 INLINEFORM4 ) 
Table TABREF77 shows the inter-annotator agreement as measured on documents from the last annotation phase (see section UID67 )  The overall INLINEFORM0 for all register types, topics, and argument components is 0 48 in the logos dimension (annotated with the modified Toulmin's model)  Such agreement can be considered as moderate by the measures proposed by Landis Koch 1977, however, direct interpretation of the agreement value lacks consensus BIBREF54   Similar inter-annotator agreement numbers were achieved in the relevant works in argumentation mining (refer to Table TABREF33 in section SECREF31 ; although most of the numbers are not directly comparable, as different inter-annotator metrics were used on different tasks) 
There is a huge difference in INLINEFORM0 regarding the registers between comments + forums posts ( INLINEFORM1 0 60, Table TABREF77 a) and articles + blog posts ( INLINEFORM2 0 09, Table TABREF77 b) in the logos dimension  If we break down the value with respect to the individual argument components, the agreement on claim and premise is substantial in the case of comments and forum posts (0 59 and 0 69, respectively)  By contrast, these argument components were annotated only with a fair agreement in articles and blog posts (0 22 and 0 24, respectively) 
As can be also observed from Table TABREF77 , the annotation agreement in the logos dimension varies regarding the document topic  While it is substantial/moderate for prayer in schools (0 68) or private vs  public schools (0 44), for some topics it remains rather slight, such as in the case of redshirting (0 14) or mainstreaming (0 08) 
First, we examine the disagreement in annotations by posing the following research question: are there any measurable properties of the annotated documents that might systematically cause low inter-annotator agreement? We use Pearson's correlation coefficient between INLINEFORM0 on each document and the particular property under investigation  We investigated the following set of measures 
Full sentence coverage ratio represents a ratio of argument component boundaries that are aligned to sentence boundaries  The value is 1 0 if all annotations in the particular document are aligned to sentences and 0 0 if no annotations match the sentence boundaries  Our hypothesis was that automatic segmentation to sentences was often incorrect, therefore annotators had to switch to the token level annotations and this might have increased disagreement on boundaries of the argument components 
Document length, paragraph length and average sentence length  Our hypotheses was that the length of documents, paragraphs, or sentences negatively affects the agreement 
Readability measures  We tested four standard readability measures, namely Ari BIBREF73 , Coleman-Liau BIBREF74 , Flesch BIBREF75 , and Lix BIBREF76 to find out whether readability of the documents plays any role in annotation agreement 
Correlation results are listed in Table TABREF82   We observed the following statistically significant ( INLINEFORM0 ) correlations  First, document length negatively correlates with agreement in comments  The longer the comment was the lower the agreement was  Second, average paragraph length negatively correlates with agreement in blog posts  The longer the paragraphs in blogs were, the lower agreement was reached  Third, all readability scores negatively correlate with agreement in the public vs  private school domain, meaning that the more complicated the text in terms of readability is, the lower agreement was reached  We observed no significant correlation in sentence coverage and average sentence length measures  We cannot draw any general conclusion from these results, but we can state that some registers and topics, given their properties, are more challenging to annotate than others 
Another qualitative analysis of disagreements between annotators was performed by constructing a probabilistic confusion matrix BIBREF77 on the token level  The biggest disagreements, as can be seen in Table TABREF85 , is caused by rebuttal and refutation confused with none (0 27 and 0 40, respectively)  This is another sign that these two argument components were very hard to annotate  As shown in Table TABREF77 , the INLINEFORM5 was also low – 0 08 for rebuttal and 0 17 for refutation 
We analyzed the annotations and found the following phenomena that usually caused disagreements between annotators 
Each argument component (e g , premise or backing) should express one consistent and coherent piece of information, for example a single reason in case of the premise (see Section UID73 )  However, the decision whether a longer text should be kept as a single argument component or segmented into multiple components is subjective and highly text-specific 
While rhetorical questions have been researched extensively in linguistics BIBREF78 , BIBREF79 , BIBREF80 , BIBREF81 , their role in argumentation represents a substantial research question BIBREF82 , BIBREF83 , BIBREF84 , BIBREF85 , BIBREF86   Teninbaum 2011 provides a brief history of rhetorical questions in persuasion  In short, rhetorical questions should provoke the reader  From the perspective of our argumentation model, rhetorical questions might fall both into the logos dimension (and thus be labeled as, e g , claim, premise, etc ) or into the pathos dimension (refer to Section SECREF20 )  Again, the decision is usually not clear-cut 
As introduced in section UID55 , rebuttal attacks the claim by presenting an opponent's view  In most cases, the rebuttal is again attacked by the author using refutation  From the pragmatic perspective, refutation thus supports the author's stance expressed by the claim  Therefore, it can be easily confused with premises, as the function of both is to provide support for the claim  Refutation thus only takes place if it is meant as a reaction to the rebuttal  It follows the discussed matter and contradicts it  Such a discourse is usually expressed as:
[claim: My claim ] [rebuttal: On the other hand, some people claim XXX which makes my claim wrong ] [refutation: But this is not true, because of YYY ]
However, the author might also take the following defensible approach to formulate the argument:
[rebuttal: Some people claim XXX-1 which makes my claim wrong ] [refutation: But this is not true, because of YYY-1 ] [rebuttal: Some people claim XXX-2 which makes my claim wrong ] [refutation: But this is not true, because of YYY-2 ] [claim: Therefore my claim ]
If this argument is formulated without stating the rebuttals, it would be equivalent to the following:
[premise: YYY-1 ] [premise: YYY-2 ] [claim: Therefore my claim ]
This example shows that rebuttal and refutation represent a rhetorical device to produce arguments, but the distinction between refutation and premise is context-dependent and on the functional level both premise and refutation have very similar role – to support the author's standpoint  Although introducing dialogical moves into monological model and its practical consequences, as described above, can be seen as a shortcoming of our model, this rhetoric figure has been identified by argumentation researchers as procatalepsis BIBREF43   A broader view on incorporating opposing views (or lack thereof) is discussed under the term confirmation bias by BIBREF21 who claim that “[   ] people are trying to convince others  They are typically looking for arguments and evidence to confirm their own claim, and ignoring negative arguments and evidence unless they anticipate having to rebut them ” The dialectical attack of possible counter-arguments may thus strengthen one's own argument 
One possible solution would be to refrain from capturing this phenomena completely and to simplify the model to claims and premises, for instance  However, the following example would then miss an important piece of information, as the last two clauses would be left un-annotated  At the same time, annotating the last clause as premise would be misleading, because it does not support the claim (in fact, it supports it only indirectly by attacking the rebuttal; this can be seen as a support is considered as an admissible extension of abstract argument graph by BIBREF87 ) 
Doc#422 (forumpost, homeschooling) [claim: I try not to be anti-homeschooling, but    it's just hard for me ] [premise: I really haven't met any homeschoolers who turned out quite right, including myself ] I apologize if what I'm saying offends any of you - that's not my intention, [rebuttal: I know that there are many homeschooled children who do just fine,] but [refutation: that hasn't been my experience ]
To the best of our knowledge, these context-dependent dialogical properties of argument components using Toulmin's model have not been solved in the literature on argumentation theory and we suggest that these observations should be taken into account in the future research in monological argumentation 
Appeal to emotion, sarcasm, irony, or jokes are common in argumentation in user-generated Web content  We also observed documents in our data that were purely sarcastic (the pathos dimension), therefore logical analysis of the argument (the logos dimension) would make no sense  However, given the structure of such documents, some claims or premises might be also identified  Such an argument is a typical example of fallacious argumentation, which intentionally pretends to present a valid argument, but its persuasion is conveyed purely for example by appealing to emotions of the reader BIBREF88  
We present some statistics of the annotated data that are important from the argumentation research perspective  Regardless of the register, 48% of claims are implicit  This means that the authors assume that their standpoint towards the discussed controversy can be inferred by the reader and give only reasons for that standpoint  Also, explicit claims are mainly written just once, only in 3% of the documents the claim was rephrased and occurred multiple times 
In 6% of the documents, the reasons for an implicit claim are given only in the pathos dimension, making the argument purely persuasive without logical argumentation 
The “myside bias”, defined as a bias against information supporting another side of an argument BIBREF89 , BIBREF90 , can be observed by the presence of rebuttals to the author's claim or by formulating arguments for both sides when the overall stance is neutral  While 85% of the documents do not consider any opposing side, only 8% documents present a rebuttal, which is then attacked by refutation in 4% of the documents  Multiple rebuttals and refutations were found in 3% of the documents  Only 4% of the documents were overall neutral and presented arguments for both sides, mainly in blog posts 
We were also interested whether mitigating linguistic devices are employed in the annotated arguments, namely in their main stance-taking components, the claims  Such devices typically include parenthetical verbs, syntactic constructions, token agreements, hedges, challenge questions, discourse markers, and tag questions, among others BIBREF91   In particular, [p  1]Kaltenbock et al 2010 define hedging as a discourse strategy that reduces the force or truth of an utterance and thus reduces the risk a speaker runs when uttering a strong or firm assertion or other speech act  We manually examined the use of hedging in the annotated claims 
Our main observation is that hedging is used differently across topics  For instance, about 30-35% of claims in homeschooling and mainstreaming signal the lack of a full commitment to the expressed stance, in contrast to prayer in schools (15%) or public vs  private schools (about 10%)  Typical hedging cues include speculations and modality (“If I have kids, I will probably homeschool them ”), statements as neutral observations (“It's not wrong to hold the opinion that in general it's better for kids to go to school than to be homeschooled ”), or weasel phrases BIBREF92 (“In some cases, inclusion can work fantastically well ”, “For the majority of the children in the school, mainstream would not have been a suitable placement ”) 
On the other hand, most claims that are used for instance in the prayer in schools arguments are very direct, without trying to diminish its commitment to the conveyed belief (for example, “NO PRAYER IN SCHOOLS!    period ”, “Get it out of public schools”, “Pray at home ”, or “No organized prayers or services anywhere on public school board property - FOR ANYONE ”)  Moreover, some claims are clearly offensive, persuading by direct imperative clauses towards the opponents/audience (“TAKE YOUR KIDS PRIVATE IF YOU CARE AS I DID”, “Run, don't walk, to the nearest private school ”) or even accuse the opponents for taking a certain stance (“You are a bad person if you send your children to private school ”) 
These observations are consistent with the findings from the first annotation study on persuasion (see section UID48 ), namely that some topics attract heated argumentation where participant take very clear and reserved standpoints (such as prayer in schools or private vs  public schools), while discussions about other topics are rather milder  It has been shown that the choices a speaker makes to express a position are informed by their social and cultural background, as well as their ability to speak the language BIBREF93 , BIBREF94 , BIBREF91   However, given the uncontrolled settings of the user-generated Web content, we cannot infer any similar conclusions in this respect 
We investigated premises across all topics in order to find the type of support used in the argument  We followed the approach of Park Cardie 2014, who distinguished three types of propositions in their study, namely unverifiable, verifiable non-experiential, and verifiable experiential 
Verifiable non-experiential and verifiable experiential propositions, unlike unverifiable propositions, contain an objective assertion, where objective means “expressing or dealing with facts or conditions as perceived without distortion by personal feelings, prejudices, or interpretations ” Such assertions have truth values that can be proved or disproved with objective evidence; the correctness of the assertion or the availability of the objective evidence does not matter BIBREF8   A verifiable proposition can further be distinguished as experiential or not, depending on whether the proposition is about the writer's personal state or experience or something non-experiential  Verifiable experiential propositions are sometimes referred to as anectotal evidence, provide the novel knowledge that readers are seeking BIBREF8  
Table TABREF97 shows the distribution of the premise types with examples for each topic from the annotated corpus  As can be seen in the first row, arguments in prayer in schools contain majority (73%) of unverifiable premises  Closer examination reveals that their content vary from general vague propositions to obvious fallacies, such as a hasty generalization, straw men, or slippery slope  As Nieminen Mustonen 2014 found out, fallacies are very common in argumentation about religion-related issues  On the other side of the spectrum, arguments about redshirting rely mostly on anecdotal evidence (61% of verifiable experiential propositions)  We will discuss the phenomena of narratives in argumentation in more detail later in section UID98   All the topics except private vs  public schools exhibit similar amount of verifiable non-experiential premises (9%–22%), usually referring to expert studies or facts  However, this type of premises has usually the lowest frequency 
Manually analyzing argumentative discourse and reconstructing (annotating) the underlying argument structure and its components is difficult  As [p  267]Reed2006 point out, “the analysis of arguments is often hard, not only for students, but for experts too ” According to [p  81]Harrell 2011b, argumentation is a skill and “even for simple arguments, untrained college students can identify the conclusion but without prompting are poor at both identifying the premises and how the premises support the conclusion ” [p  81]Harrell 2011 further claims that “a wide literature supports the contention that the particular skills of understanding, evaluating, and producing arguments are generally poor in the population of people who have not had specific training and that specific training is what improves these skills ” Some studies, for example, show that students perform significantly better on reasoning tasks when they have learned to identify premises and conclusions BIBREF95 or have learned some standard argumentation norms BIBREF96  
One particular extra challenge in analyzing argumentation in Web user-generated discourse is that the authors produce their texts probably without any existing argumentation theory or model in mind  We assume that argumentation or persuasion is inherent when users discuss controversial topics, but the true reasons why people participate in on-line communities and what drives their behavior is another research question BIBREF97 , BIBREF98 , BIBREF99 , BIBREF100   When the analyzed texts have a clear intention to produce argumentative discourse, such as in argumentative essays BIBREF7 , the argumentation is much more explicit and a substantially higher inter-annotator agreement can be achieved 
The model seems to be suitable for short persuasive documents, such as comments and forum posts  Its applicability to longer documents, such as articles or blog posts, is problematic for several reasons 
The argument components of the (modified) Toulmin's model and their roles are not expressive enough to capture argumentation that not only conveys the logical structure (in terms of reasons put forward to support the claim), but also relies heavily on the rhetorical power  This involves various stylistic devices, pervading narratives, direct and indirect speech, or interviews  While in some cases the argument components are easily recognizable, the vast majority of the discourse in articles and blog posts does not correspond to any distinguishable argumentative function in the logos dimension  As the purpose of such discourse relates more to rhetoric than to argumentation, unambiguous analysis of such phenomena goes beyond capabilities of the current argumentation model  For a discussion about metaphors in Toulmin's model of argumentation see, e g , BIBREF102 , BIBREF103  
Articles without a clear standpoint towards the discussed controversy cannot be easily annotated with the model either  Although the matter is viewed from both sides and there might be reasons presented for either of them, the overall persuasive intention is missing and fitting such data to the argumentation framework causes disagreements  One solution might be to break the document down to paragraphs and annotate each paragraph separately, examining argumentation on a different level of granularity 
As introduced in section SECREF20 , there are several dimensions of an argument  The Toulmin's model focuses solely on the logos dimension  We decided to ignore the ethos dimension, because dealing with the author's credibility remains unclear, given the variety of the source web data  However, exploiting the pathos dimension of an argument is prevalent in the web data, for example as an appeal to emotions  Therefore we experimented with annotating appeal to emotions as a separate category independent of components in the logos dimension  We defined some features for the annotators how to distinguish appeal to emotions  Figurative language such as hyperbole, sarcasm, or obvious exaggerating to “spice up” the argument are the typical signs of pathos  In an extreme case, the whole argument might be purely emotional, as in the following example 
Doc#1698 (comment, prayer in schools) [app-to-emot: Prayer being removed from school is just the leading indicator of a nation that is ‘Falling Away’ from Jehovah  [   ] And the disasters we see today are simply God’s finger writing on the wall: Mene, mene, Tekel, Upharsin; that is, God has weighed America in the balances, and we’ve been found wanting  No wonder 50 million babies have been aborted since 1973  [   ]]
We kept annotations on the pathos dimension as simple as possible (with only one appeal to emotions label), but the resulting agreement was unsatisfying ( INLINEFORM0 0 30) even after several annotation iterations  Appeal to emotions is considered as a type of fallacy BIBREF104 , BIBREF18   Given the results, we assume that more carefully designed approach to fallacy annotation should be applied  To the best of our knowledge, there have been very few research works on modeling fallacies similarly to arguments on the discourse level BIBREF105   Therefore the question, in which detail and structure fallacies should be annotated, remains open  For the rest of the paper, we thus focus on the logos dimension solely 
Some of the educational topics under examination relate to young children (e g , redshirting or mainstreaming); therefore we assume that the majority of participants in discussions are their parents  We observed that many documents related to these topics contain narratives  Sometimes the story telling is meant as a support for the argument, but there are documents where the narrative has no intention to persuade and is simply a story sharing 
There is no widely accepted theory of the role of narratives among argumentation scholars  According to Fisher 1987, humans are storytellers by nature, and the “reason” in argumentation is therefore better understood in and through the narratives  He found that good reasons often take the form of narratives  Hoeken Fikkers 2014 investigated how integration of explicit argumentative content into narratives influences issue-relevant thinking and concluded that identifying with the character being in favor of the issue yielded a more positive attitude toward the issue  In a recent research, Bex 2011 proposes an argumentative-narrative model of reasoning with evidence, further elaborated in BIBREF106 ; also Niehaus et al 2012 proposes a computational model of narrative persuasion 
Stemming from another research field, LeytonEscobar2014 found that online community members who use and share narratives have higher participation levels and that narratives are useful tools to build cohesive cultures and increase participation  Betsch et al 2010 examined influencing vaccine intentions among parents and found that narratives carry more weight than statistics 
Summary of annotation studies
This section described two annotation studies that deal with argumentation in user-generated Web content on different levels of detail  In section SECREF44 , we argued for a need of document-level distinction of persuasiveness  We annotated 990 comments and forum posts, reaching moderate inter-annotator agreement (Fleiss' INLINEFORM0 0 59)  Section SECREF51 motivated the selection of a model for micro-level argument annotation, proposed its extension based on pre-study observations, and outlined the annotation set-up  This annotation study resulted into 340 documents annotated with the modified Toulmin's model and reached moderate inter-annotator agreement in the logos dimension (Krippendorff's INLINEFORM1 0 48)  These results make the annotated corpora suitable for training and evaluation computational models and each of these two annotation studies will have their experimental counterparts in the following section 
Experiments
This section presents experiments conducted on the annotated corpora introduced in section SECREF4   We put the main focus on identifying argument components in the discourse  To comply with the machine learning terminology, in this section we will use the term domain as an equivalent to a topic (remember that our dataset includes six different topics; see section SECREF38 ) 
We evaluate three different scenarios  First, we report ten-fold cross validation over a random ordering of the entire data set  Second, we deal with in-domain ten-fold cross validation for each of the six domains  Third, in order to evaluate the domain portability of our approach, we train the system on five domains and test on the remaining one for all six domains (which we report as cross-domain validation) 
Identification of argument components
In the following experiment, we focus on automatic identification of arguments in the discourse  Our approach is based on supervised and semi-supervised machine learning methods on the gold data Toulmin dataset introduced in section SECREF51  
An argument consists of different components (such as premises, backing, etc ) which are implicitly linked to the claim  In principle one document can contain multiple independent arguments  However, only 4% of the documents in our dataset contain arguments for both sides of the issue  Thus we simplify the task and assume there is only one argument per document 
Given the low inter-annotator agreement on the pathos dimension (Table TABREF77 ), we focus solely on recognizing the logical dimension of argument  The pathos dimension of argument remains an open problem for a proper modeling as well as its later recognition 
Since the smallest annotation unit is a token and the argument components do not overlap, we approach identification of argument components as a sequence labeling problem  We use the BIO encoding, so each token belongs to one of the following 11 classes: O (not a part of any argument component), Backing-B, Backing-I, Claim-B, Claim-I, Premise-B, Premise-I, Rebuttal-B, Rebuttal-I, Refutation-B, Refutation-I  This is the minimal encoding that is able to distinguish two adjacent argument components of the same type  In our data, 48% of all adjacent argument components of the same type are direct neighbors (there are no "O" tokens in between) 
We report Macro- INLINEFORM0 score and INLINEFORM1 scores for each of the 11 classes as the main evaluation metric  This evaluation is performed on the token level, and for each token the predicted label must exactly match the gold data label (classification of tokens into 11 classes) 
As instances for the sequence labeling model, we chose sentences rather than tokens  During our initial experiments, we observed that building a sequence labeling model for recognizing argument components as sequences of tokens is too fine-grained, as a single token does not convey enough information that could be encoded as features for a machine learner  However, as discussed in section UID73 , the annotations were performed on data pre-segmented to sentences and annotating tokens was necessary only when the sentence segmentation was wrong or one sentence contained multiple argument components  Our corpus consists of 3899 sentences, from which 2214 sentences (57%) contain no argument component  From the remaining ones, only 50 sentences (1%) have more than one argument component  Although in 19 cases (0 5%) the sentence contains a Claim-Premise pair which is an important distinction from the argumentation perspective, given the overall small number of such occurrences, we simplify the task by treating each sentence as if it has either one argument component or none 
The approximation with sentence-level units is explained in the example in Figure FIGREF112   In order to evaluate the expected performance loss using this approximation, we used an oracle that always predicts the correct label for the unit (sentence) and evaluated it against the true labels (recall that the evaluation against the true gold labels is done always on token level)  We lose only about 10% of macro INLINEFORM0 score (0 906) and only about 2% of accuracy (0 984)  This performance is still acceptable, while allowing to model sequences where the minimal unit is a sentence 
Table TABREF114 shows the distribution of the classes in the gold data Toulmin, where the labeling was already mapped to the sentences  The little presence of rebuttal and refutation (4 classes account only for 3 4% of the data) makes this dataset very unbalanced 
We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling  Each sentence ( INLINEFORM0 ) is represented as a vector of real-valued features 
We defined the following feature sets:
FS0: Baseline lexical features
word uni-, bi-, and tri-grams (binary)
FS1: Structural, morphological, and syntactic features
First and last 3 tokens  Motivation: these tokens may contain discourse markers or other indicators for argument components, such as “therefore” and “since” for premises or “think” and “believe” for claims 
Relative position in paragraph and relative position in document  Motivation: We expect that claims are more likely to appear at the beginning or at the end of the document 
Number of POS 1-3 grams, dependency tree depth, constituency tree production rules, and number of sub-clauses  Based on BIBREF113  
FS2: Topic and sentiment features
30 features taken from a vector representation of the sentence obtained by using Gibbs sampling on LDA model BIBREF114 , BIBREF115 with 30 topics trained on unlabeled data from the raw corpus  Motivation: Topic representation of a sentence might be valuable for detecting off-topic sentences, namely non-argument components 
Scores for five sentiment categories (from very negative to very positive) obtained from Stanford sentiment analyzer BIBREF116   Motivation: Claims usually express opinions and carry sentiment 
FS3: Semantic, coreference, and discourse features
Binary features from Clear NLP Semantic Role Labeler BIBREF117   Namely, we extract agent, predicate + agent, predicate + agent + patient + (optional) negation, argument type + argument value, and discourse marker which are based on PropBank semantic role labels  Motivation: Exploit the semantics of Capturing the semantics of the sentences 
Binary features from Stanford Coreference Chain Resolver BIBREF118 , e g , presence of the sentence in a chain, transition type (i e , nominal–pronominal), distance to previous/next sentences in the chain, or number of inter-sentence coreference links  Motivation: Presence of coreference chains indicates links outside the sentence and thus may be informative, for example, for classifying whether the sentence is a part of a larger argument component 
Results of a PTDB-style discourse parser BIBREF119 , namely the type of discourse relation (explicit, implicit), presence of discourse connectives, and attributions  Motivation: It has been claimed that discourse relations play a role in argumentation mining BIBREF120  
FS4: Embedding features
300 features from word embedding vectors using word embeddings trained on part of the Google News dataset BIBREF121   In particular, we sum up embedding vectors (dimensionality 300) of each word, resulting into a single vector for the entire sentence  This vector is then directly used as a feature vector  Motivation: Embeddings helped to achieve state-of-the-art results in various NLP tasks BIBREF116 , BIBREF122  
Except the baseline lexical features, all feature types are extracted not only for the current sentence INLINEFORM0 , but also for INLINEFORM1 preceding and subsequent sentences, namely INLINEFORM2 , INLINEFORM3 , INLINEFORM4 INLINEFORM5 , INLINEFORM6 , where INLINEFORM7 was empirically set to 4  Each feature is then represented with a prefix to determine its relative position to the current sequence unit 
Let us first discuss the upper bounds of the system  Performance of the three human annotators is shown in the first column of Table TABREF139 (results are obtained from a cumulative confusion matrix)  The overall Macro- INLINEFORM0 score is 0 602 (accuracy 0 754)  If we look closer at the different argument components, we observe that humans are good at predicting claims, premises, backing and non-argumentative text (about 0 60-0 80 INLINEFORM1 ), but on rebuttal and refutation they achieve rather low scores  Without these two components, the overall human Macro- INLINEFORM2 would be 0 707  This trend follows the inter-annotator agreement scores, as discussed in section UID75  
In our experiments, the feature sets were combined in the bottom-up manner, starting with the simple lexical features (FS0), adding structural and syntactic features (FS1), then adding topic and sentiment features (FS2), then features reflecting the discourse structure (FS3), and finally enriched with completely unsupervised latent vector space representation (FS4)  In addition, we were gradually removing the simple features (e g , without lexical features, without syntactic features, etc ) to test the system with more “abstract” feature sets (feature ablation)  The results are shown in Table TABREF139  
The overall best performance (Macro- INLINEFORM0 0 251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets  Classification of non-argumentative text (the "O" class) yields about 0 7 INLINEFORM1 score even in the baseline setting  The boundaries of claims (Cla-B), premises (Pre-B), and backing (Bac-B) reach in average lower scores then their respective inside tags (Cla-I, Pre-I, Bac-I)  It can be interpreted such that the system is able to classify that a certain sentence belongs to a certain argument component, but the distinction whether it is a beginning of the argument component is harder  The very low numbers for rebuttal and refutation have two reasons  First, these two argument components caused many disagreements in the annotations, as discussed in section UID86 , and were hard to recognize for the humans too  Second, these four classes have very few instances in the corpus (about 3 4%, see Table TABREF114 ), so the classifier suffers from the lack of training data 
The results for the in-domain cross validation scenario are shown in Table TABREF140   Similarly to the cross-validation scenario, the overall best results were achieved using the largest feature set (01234)  For mainstreaming and red-shirting, the best results were achieved using only the feature set 4 (embeddings)  These two domains contain also fewer documents, compared to other domains (refer to Table TABREF71 )  We suspect that embeddings-based features convey important information when not enough in-domain data are available  This observation will become apparent in the next experiment 
The cross-domain experiments yield rather poor results for most of the feature combinations (Table TABREF141 )  However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario  These results indicate that embedding features generalize well across domains in our task of argument component identification  We leave investigating better performing vector representations, such as paragraph vectors BIBREF123 , for future work 
Error analysis based on the probabilistic confusion matrix BIBREF124 shown in Table TABREF142 reveals further details  About a half of the instances for each class are misclassified as non-argumentative (the "O" prediction) 
Backing-B is often confused with Premise-B (12%) and Backing-I with Premise-I (23%)  Similarly, Premise-I is misclassified as Backing-I in 9%  This shows that distinguishing between backing and premises is not easy because these two components are similar such that they support the claim, as discussed in section UID86   We can also see that the misclassification is consistent among *-B and *-I tags 
Rebuttal is often misclassified as Premise (28% for Rebuttal-I and 18% for Rebuttal-B; notice again the consistency in *-B and *-I tags)  This is rather surprising, as one would expect that rebuttal would be confused with a claim, because its role is to provide an opposing view 
Refutation-B and Refutation-I is misclassified as Premise-I in 19% and 27%, respectively  This finding confirms the discussion in section UID86 , because the role of refutation is highly context-dependent  In a pragmatic perspective, it is put forward to indirectly support the claim by attacking the rebuttal, thus having a similar function to the premise 
We manually examined miss-classified examples produced the best-performing system to find out which phenomena pose biggest challenges  Properly detecting boundaries of argument components caused problems, as shown in Figure FIGREF146 (a)  This goes in line with the granularity annotation difficulties discussed in section UID86   The next example in Figure FIGREF146 (b) shows that even if boundaries of components were detected precisely, the distinction between premise and backing fails  The example also shows that in some cases, labeling on clause level is required (left-hand side claim and premise) but the approximation in the system cannot cope with this level of detail (as explained in section UID111 )  Confusing non-argumentative text and argument components by the system is sometimes plausible, as is the case of the last rhetorical question in Figure FIGREF146 (c)  On the other hand, the last example in Figure FIGREF146 (d) shows that some claims using figurative language were hard to be identified  The complete predictions along with the gold data are publicly available 
SVMhmm offers many hyper-parameters with suggested default values, from which three are of importance  Parameter INLINEFORM0 sets the order of dependencies of transitions in HMM, parameter INLINEFORM1 sets the order of dependencies of emissions in HMM, and parameter INLINEFORM2 represents a trading-off slack versus magnitude of the weight-vector  For all experiments, we set all the hyper-parameters to their default values ( INLINEFORM3 , INLINEFORM4 , INLINEFORM5 )  Using the best performing feature set from Table TABREF139 , we experimented with a grid search over different values ( INLINEFORM6 , INLINEFORM7 , INLINEFORM8 ) but the results did not outperform the system trained with default parameter values 
The INLINEFORM0 scores might seem very low at the first glance  One obvious reason is the actual performance of the system, which gives a plenty of room for improvement in the future  But the main cause of low INLINEFORM2 numbers is the evaluation measure — using 11 classes on the token level is very strict, as it penalizes a mismatch in argument component boundaries the same way as a wrongly predicted argument component type  Therefore we also report two another evaluation metrics that help to put our results into a context 
Krippendorff's INLINEFORM0 — It was also used for evaluating inter-annotator agreement (see section UID75 ) 
Boundary similarity BIBREF125 — Using this metric, the problem is treated solely as a segmentation task without recognizing the argument component types 
As shown in Table TABREF157 (the Macro- INLINEFORM0 scores are repeated from Table TABREF139 ), the best-performing system achieves 0 30 score using Krippendorf's INLINEFORM1 , which is in the middle between the baseline and the human performance (0 48) but is considered as poor from the inter-annotator agreement point of view BIBREF54   The boundary similarity metrics is not directly suitable for evaluating argument component classification, but reveals a sub-task of finding the component boundaries  The best system achieved 0 32 on this measure  Vovk2013MT used this measure to annotate argument spans and his annotators achieved 0 36 boundary similarity score  Human annotators in BIBREF125 reached 0 53 boundary similarity score 
The overall performance of the system is also affected by the accuracy of individual NPL tools used for extracting features  One particular problem is that the preprocessing models we rely on (POS, syntax, semantic roles, coreference, discourse; see section UID115 ) were trained on newswire corpora, so one has to expect performance drop when applied on user-generated content  This is however a well-known issue in NLP BIBREF126 , BIBREF127 , BIBREF128  
To get an impression of the actual performance of the system on the data, we also provide the complete output of our best performing system in one PDF document together with the gold annotations in the logos dimension side by side in the accompanying software package  We believe this will help the community to see the strengths of our model as well as possible limitations of our current approaches 
Conclusions
Let us begin with summarizing answers to the research questions stated in the introduction  First, as we showed in section UID55 , existing argumentation theories do offer models for capturing argumentation in user-generated content on the Web  We built upon the Toulmin's model and proposed some extensions 
Second, as compared to the negative experiences with annotating using Walton's schemes (see sections UID52 and SECREF31 ), our modified Toulmin's model offers a trade-off between its expressiveness and annotation reliability  However, we found that the capabilities of the model to capture argumentation depend on the register and topic, the length of the document, and inherently on the literary devices and structures used for expressing argumentation as these properties influenced the agreement among annotators 
Third, there are aspects of online argumentation that lack their established theoretical counterparts, such as rhetorical questions, figurative language, narratives, and fallacies in general  We tried to model some of them in the pathos dimension of argument (section UID103 ), but no satisfying agreement was reached  Furthermore, we dealt with a step that precedes argument analysis by filtering documents given their persuasiveness with respect to the controversy  Finally, we proposed a computational model based on machine learning for identifying argument components (section SECREF108 )  In this identification task, we experimented with a wide range of linguistically motivated features and found that (1) the largest feature set (including n-grams, structural features, syntactic features, topic distribution, sentiment distribution, semantic features, coreference feaures, discourse features, and features based on word embeddings) performs best in both in-domain and all-data cross validation, while (2) features based only on word embeddings yield best results in cross-domain evaluation 
Since there is no one-size-fits-all argumentation theory to be applied to actual data on the Web, the argumentation model and an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties  Its selection should be based on the data at hand and the desired application  Given the proposed use-case scenarios (section SECREF1 ) and the results of our annotation study (section SECREF51 ), we recommend a scheme based on Toulmin's model for short documents, such as comments or forum posts 
Context 2: Introduction
Over the past two decades, the emergence of social media has enabled the proliferation of traceable human behavior  The content posted by users can reflect who their friends are, what topics they are interested in, or which company they are working for  At the same time, users are listing a number of profile fields to define themselves to others  The utilization of such metadata has proven important in facilitating further developments of applications in advertising BIBREF0 , personalization BIBREF1 , and recommender systems BIBREF2   However, profile information can be limited, depending on the platform, or it is often deliberately omitted BIBREF3   To uncloak this information, a number of studies have utilized social media users' footprints to approximate their profiles 
This paper explores the potential of predicting a user's industry –the aggregate of enterprises in a particular field– by identifying industry indicative text in social media  The accurate prediction of users' industry can have a big impact on targeted advertising by minimizing wasted advertising BIBREF4 and improved personalized user experience  A number of studies in the social sciences have associated language use with social factors such as occupation, social class, education, and income BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8   An additional goal of this paper is to examine such findings, and in particular the link between language and occupational class, through a data-driven approach 
In addition, we explore how meaning changes depending on the occupational context  By leveraging word embeddings, we seek to quantify how, for example, cloud might mean a separate concept (e g , condensed water vapor) in the text written by users that work in environmental jobs while it might be used differently by users in technology occupations (e g , Internet-based computing) 
Specifically, this paper makes four main contributions  First, we build a large, industry-annotated dataset that contains over 20,000 blog users  In addition to their posted text, we also link a number of user metadata including their gender, location, occupation, introduction and interests 
Second, we build content-based classifiers for the industry prediction task and study the effect of incorporating textual features from the users' profile metadata using various meta-classification techniques, significantly improving both the overall accuracy and the average per industry accuracy 
Next, after examining which words are indicative for each industry, we build vector-space representations of word meanings and calculate one deviation for each industry, illustrating how meaning is differentiated based on the users' industries  We qualitatively examine the resulting industry-informed semantic representations of words by listing the words per industry that are most similar to job related and general interest terms 
Finally, we rank the different industries based on the normalized relative frequencies of emotionally charged words (positive and negative) and, in addition, discover that, for both genders, these frequencies do not statistically significantly correlate with an industry's gender dominance ratio 
After discussing related work in Section SECREF2 , we present the dataset used in this study in Section SECREF3   In Section SECREF4 we evaluate two feature selection methods and examine the industry inference problem using the text of the users' postings  We then augment our content-based classifier by building an ensemble that incorporates several metadata classifiers  We list the most industry indicative words and expose how each industrial semantic field varies with respect to a variety of terms in Section SECREF5   We explore how the frequencies of emotionally charged words in each gender correlate with the industries and their respective gender dominance ratio and, finally, conclude in Section SECREF6  
Related Work
Alongside the wide adoption of social media by the public, researchers have been leveraging the newly available data to create and refine models of users' behavior and profiling  There exists a myriad research that analyzes language in order to profile social media users  Some studies sought to characterize users' personality BIBREF9 , BIBREF10 , while others sequenced the expressed emotions BIBREF11 , studied mental disorders BIBREF12 , and the progression of health conditions BIBREF13   At the same time, a number of researchers sought to predict the social media users' age and/or gender BIBREF14 , BIBREF15 , BIBREF16 , while others targeted and analyzed the ethnicity, nationality, and race of the users BIBREF17 , BIBREF18 , BIBREF19   One of the profile fields that has drawn a great deal of attention is the location of a user  Among others, Hecht et al  Hecht11 predicted Twitter users' locations using machine learning on nationwide and state levels  Later, Han et al  Han14 identified location indicative words to predict the location of Twitter users down to the city level 
As a separate line of research, a number of studies have focused on discovering the political orientation of users BIBREF15 , BIBREF20 , BIBREF21   Finally, Li et al  Li14a proposed a way to model major life events such as getting married, moving to a new place, or graduating  In a subsequent study, BIBREF22 described a weakly supervised information extraction method that was used in conjunction with social network information to identify the name of a user's spouse, the college they attended, and the company where they are employed 
The line of work that is most closely related to our research is the one concerned with understanding the relation between people's language and their industry  Previous research from the fields of psychology and economics have explored the potential for predicting one's occupation from their ability to use math and verbal symbols BIBREF23 and the relationship between job-types and demographics BIBREF24   More recently, Huang et al  Huang15 used machine learning to classify Sina Weibo users to twelve different platform-defined occupational classes highlighting the effect of homophily in user interactions  This work examined only users that have been verified by the Sina Weibo platform, introducing a potential bias in the resulting dataset  Finally, Preotiuc-Pietro et al  Preoctiuc15 predicted the occupational class of Twitter users using the Standard Occupational Classification (SOC) system, which groups the different jobs based on skill requirements  In that work, the data collection process was limited to only users that specifically mentioned their occupation in their self-description in a way that could be directly mapped to a SOC occupational class  The mapping between a substring of their self-description and a SOC occupational class was done manually  Because of the manual annotation step, their method was not scalable; moreover, because they identified the occupation class inside a user self-description, only a very small fraction of the Twitter users could be included (in their case, 5,191 users) 
Both of these recent studies are based on micro-blogging platforms, which inherently restrict the number of characters that a post can have, and consequently the way that users can express themselves 
Moreover, both studies used off-the-shelf occupational taxonomies (rather than self-declared occupation categories), resulting in classes that are either too generic (e g , media, welfare and electronic are three of the twelve Sina Weibo categories), or too intermixed (e g , an assistant accountant is in a different class from an accountant in SOC)  To address these limitations, we investigate the industry prediction task in a large blog corpus consisting of over 20K American users, 40K web-blogs, and 560K blog posts 
Dataset
We compile our industry-annotated dataset by identifying blogger profiles located in the U S  on the profile finder on http://www blogger com, and scraping only those users that had the industry profile element completed 
For each of these bloggers, we retrieve all their blogs, and for each of these blogs we download the 21 most recent blog postings  We then clean these blog posts of HTML tags and tokenize them, and drop those bloggers whose cumulative textual content in their posts is less than 600 characters  Following these guidelines, we identified all the U S  bloggers with completed industry information 
Traditionally, standardized industry taxonomies organize economic activities into groups based on similar production processes, products or services, delivery systems or behavior in financial markets  Following such assumptions and regardless of their many similarities, a tomato farmer would be categorized into a distinct industry from a tobacco farmer  As demonstrated in Preotiuc-Pietro et al  Preoctiuc15 such groupings can cause unwarranted misclassifications 
The Blogger platform provides a total of 39 different industry options  Even though a completed industry value is an implicit text annotation, we acknowledge the same problem noted in previous studies: some categories are too broad, while others are very similar  To remedy this and following Guibert et al  Guibert71, who argued that the denominations used in a classification must reflect the purpose of the study, we group the different Blogger industries based on similar educational background and similar technical terminology  To do that, we exclude very general categories and merge conceptually similar ones  Examples of broad categories are the Education and the Student options: a teacher could be teaching in any concentration, while a student could be enrolled in any discipline  Examples of conceptually similar categories are the Investment Banking and the Banking options 
The final set of categories is shown in Table TABREF1 , along with the number of users in each category  The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts  Table TABREF2 presents additional statistics of our dataset 
Text-based Industry Modeling
After collecting our dataset, we split it into three sets: a train set, a development set, and a test set  The sizes of these sets are 17,880, 2,500, and 2,500 users, respectively, with users randomly assigned to these sets  In all the experiments that follow, we evaluate our classifiers by training them on the train set, configure the parameters and measure performance on the development set, and finally report the prediction accuracy and results on the test set  Note that all the experiments are performed at user level, i e , all the data for one user is compiled into one instance in our data sets 
To measure the performance of our classifiers, we use the prediction accuracy  However, as shown in Table TABREF1 , the available data is skewed across categories, which could lead to somewhat distorted accuracy numbers depending on how well a model learns to predict the most populous classes  Moreover, accuracy alone does not provide a great deal of insight into the individual performance per industry, which is one of the main objectives in this study  Therefore, in our results below, we report: (1) micro-accuracy ( INLINEFORM0 ), calculated as the percentage of correctly classified instances out of all the instances in the development (test) data; and (2) macro-accuracy ( INLINEFORM1 ), calculated as the average of the per-category accuracies, where the per-category accuracy is the percentage of correctly classified instances out of the instances belonging to one category in the development (test) data 
Leveraging Blog Content
In this section, we seek the effectiveness of using solely textual features obtained from the users' postings to predict their industry 
The industry prediction baseline Majority is set by discovering the most frequently featured class in our training set and picking that class in all predictions in the respective development or testing set 
After excluding all the words that are not used by at least three separate users in our training set, we build our AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier  As seen in Figure FIGREF3 , we can far exceed the Majority baseline performance by incorporating basic language signals into machine learning algorithms (173% INLINEFORM0 improvement) 
We additionally explore the potential of improving our text classification task by applying a number of feature ranking methods and selecting varying proportions of top ranked features in an attempt to exclude noisy features  We start by ranking the different features, w, according to their Information Gain Ratio score (IGR) with respect to every industry, i, and training our classifier using different proportions of the top features  INLINEFORM0 INLINEFORM1
Even though we find that using the top 95% of all the features already exceeds the performance of the All Words model on the development data, we further experiment with ranking our features with a more aggressive formula that heavily promotes the features that are tightly associated with any industry category  Therefore, for every word in our training set, we define our newly introduced ranking method, the Aggressive Feature Ranking (AFR), as: INLINEFORM0
In Figure FIGREF3 we illustrate the performance of all four methods in our industry prediction task on the development data  Note that for each method, we provide both the accuracy ( INLINEFORM0 ) and the average per-class accuracy ( INLINEFORM1 )  The Majority and All Words methods apply to all the features; therefore, they are represented as a straight line in the figure  The IGR and AFR methods are applied to varying subsets of the features using a 5% step 
Our experiments demonstrate that the word choice that the users make in their posts correlates with their industry  The first observation in Figure FIGREF3 is that the INLINEFORM0 is proportional to INLINEFORM1 ; as INLINEFORM2 increases, so does INLINEFORM3   Secondly, the best result on the development set is achieved by using the top 90% of the features using the AFR method  Lastly, the improvements of the IGR and AFR feature selections are not substantially better in comparison to All Words (at most 5% improvement between All Words and AFR), which suggest that only a few noisy features exist and most of the words play some role in shaping the “language" of an industry 
As a final evaluation, we apply on the test data the classifier found to work best on the development data (AFR feature selection, top 90% features), for an INLINEFORM0 of 0 534 and INLINEFORM1 of 0 477 
Leveraging User Metadata
Together with the industry information and the most recent postings of each blogger, we also download a number of accompanying profile elements  Using these additional elements, we explore the potential of incorporating users' metadata in our classifiers 
Table TABREF7 shows the different user metadata we consider together with their coverage percentage (not all users provide a value for all of the profile elements)  With the exception of the gender field, the remaining metadata elements shown in Table TABREF7 are completed by the users as a freely editable text field  This introduces a considerable amount of noise in the set of possible metadata values  Examples of noise in the occupation field include values such as “Retired”, “I work ”, or “momma” which are not necessarily informative for our industry prediction task 
To examine whether the metadata fields can help in the prediction of a user's industry, we build classifiers using the different metadata elements  For each metadata element that has a textual value, we use all the words in the training set for that field as features  The only two exceptions are the state field, which is encoded as one feature that can take one out of 50 different values representing the 50 U S  states; and the gender field, which is encoded as a feature with a distinct value for each user gender option: undefined, male, or female 
As shown in Table TABREF9 , we build four different classifiers using the multinomial NB algorithm: Occu (which uses the words found in the occupation profile element), Intro (introduction), Inter (interests), and Gloc (combined gender, city, state) 
In general, all the metadata classifiers perform better than our majority baseline ( INLINEFORM0 of 18 88%)  For the Gloc classifier, this result is in alignment with previous studies BIBREF24   However, the only metadata classifier that outperforms the content classifier is the Occu classifier, which despite missing and noisy occupation values exceeds the content classifier's performance by an absolute 3 2% 
To investigate the promise of combining the five different classifiers we have built so far, we calculate their inter-prediction agreement using Fleiss's Kappa BIBREF25 , as well as the lower prediction bounds using the double fault measure BIBREF26   The Kappa values, presented in the lower left side of Table TABREF10 , express the classification agreement for categorical items, in this case the users' industry  Lower values, especially values below 30%, mean smaller agreement  Since all five classifiers have better-than-baseline accuracy, this low agreement suggests that their predictions could potentially be combined to achieve a better accumulated result 
Moreover, the double fault measure values, which are presented in the top-right hand side of Table TABREF10 , express the proportion of test cases for which both of the two respective classifiers make false predictions, essentially providing the lowest error bound for the pairwise ensemble classifier performance  The lower those numbers are, the greater the accuracy potential of any meta-classification scheme that combines those classifiers  Once again, the low double fault measure values suggest potential gain from a combination of the base classifiers into an ensemble of models 
After establishing the promise of creating an ensemble of classifiers, we implement two meta-classification approaches  First, we combine our classifiers using features concatenation (or early fusion)  Starting with our content-based classifier (Text), we successively add the features derived from each metadata element  The results, both micro- and macro-accuracy, are presented in Table TABREF12   Even though all these four feature concatenation ensembles outperform the content-based classifier in the development set, they fail to outperform the Occu classifier 
Second, we explore the potential of using stacked generalization (or late fusion) BIBREF27   The base classifiers, referred to as L0 classifiers, are trained on different folds of the training set and used to predict the class of the remaining instances  Those predictions are then used together with the true label of the training instances to train a second classifier, referred to as the L1 classifier: this L1 is used to produce the final prediction on both the development data and the test data  Traditionally, stacking uses different machine learning algorithms on the same training data  However in our case, we use the same algorithm (multinomial NB) on heterogeneous data (i e , different types of data such as content, occupation, introduction, interests, gender, city and state) in order to exploit all available sources of information 
The ensemble learning results on the development set are shown in Table TABREF12   We notice a constant improvement for both metrics when adding more classifiers to our ensemble except for the Gloc classifier, which slightly reduces the performance  The best result is achieved using an ensemble of the Text, Occu, Intro, and Inter L0 classifiers; the respective performance on the test set is an INLINEFORM0 of 0 643 and an INLINEFORM1 of 0 564  Finally, we present in Figure FIGREF11 the prediction accuracy for the final classifier for each of the different industries in our test dataset  Evidently, some industries are easier to predict than others  For example, while the Real Estate and Religion industries achieve accuracy figures above 80%, other industries, such as the Banking industry, are predicted correctly in less than 17% of the time  Anecdotal evidence drawn from the examination of the confusion matrix does not encourage any strong association of the Banking class with any other  The misclassifications are roughly uniform across all other classes, suggesting that the users in the Banking industry use language in a non-distinguishing way 
Qualitative Analysis
In this section, we provide a qualitative analysis of the language of the different industries 
Top-Ranked Words
To conduct a qualitative exploration of which words indicate the industry of a user, Table TABREF14 shows the three top-ranking content words for the different industries using the AFR method 
Not surprisingly, the top ranked words align well with what we would intuitively expect for each industry  Even though most of these words are potentially used by many users regardless of their industry in our dataset, they are still distinguished by the AFR method because of the different frequencies of these words in the text of each industry 
Industry-specific Word Similarities
Next, we examine how the meaning of a word is shaped by the context in which it is uttered  In particular, we qualitatively investigate how the speakers' industry affects meaning by learning vector-space representations of words that take into account such contextual information  To achieve this, we apply the contextualized word embeddings proposed by Bamman et al  Bamman14, which are based on an extension of the “skip-gram" language model BIBREF28  
In addition to learning a global representation for each word, these contextualized embeddings compute one deviation from the common word embedding representation for each contextual variable, in this case, an industry option  These deviations capture the terms' meaning variations (shifts in the INLINEFORM0 -dimensional space of the representations, where INLINEFORM1 in our experiments) in the text of the different industries, however all the embeddings are in the same vector space to allow for comparisons to one another 
Using the word representations learned for each industry, we present in Table TABREF16 the terms in the Technology and the Tourism industries that have the highest cosine similarity with a job-related word, customers  Similarly, Table TABREF17 shows the words in the Environment and the Tourism industries that are closest in meaning to a general interest word, food  More examples are given in the Appendix SECREF8  
The terms that rank highest in each industry are noticeably different  For example, as seen in Table TABREF17 , while food in the Environment industry is similar to nutritionally and locally, in the Tourism industry the same word relates more to terms such as delicious and pastries  These results not only emphasize the existing differences in how people in different industries perceive certain terms, but they also demonstrate that those differences can effectively be captured in the resulting word embeddings 
Emotional Orientation per Industry and Gender
As a final analysis, we explore how words that are emotionally charged relate to different industries  To quantify the emotional orientation of a text, we use the Positive Emotion and Negative Emotion categories in the Linguistic Inquiry and Word Count (LIWC) dictionary BIBREF29   The LIWC dictionary contains lists of words that have been shown to correlate with the psychological states of people that use them; for example, the Positive Emotion category contains words such as “happy,” “pretty,” and “good ”
For the text of all the users in each industry we measure the frequencies of Positive Emotion and Negative Emotion words normalized by the text's length  Table TABREF20 presents the industries' ranking for both categories of words based on their relative frequencies in the text of each industry 
We further perform a breakdown per-gender, where we once again calculate the proportion of emotionally charged words in each industry, but separately for each gender  We find that the industry rankings of the relative frequencies INLINEFORM0 of emotionally charged words for the two genders are statistically significantly correlated, which suggests that regardless of their gender, users use positive (or negative) words with a relative frequency that correlates with their industry  (In other words, even if e g , Fashion has a larger number of women users, both men and women working in Fashion will tend to use more positive words than the corresponding gender in another industry with a larger number of men users such as Automotive )
Finally, motivated by previous findings of correlations between job satisfaction and gender dominance in the workplace BIBREF30 , we explore the relationship between the usage of Positive Emotion and Negative Emotion words and the gender dominance in an industry  Although we find that there are substantial gender imbalances in each industry (Appendix SECREF9 ), we did not find any statistically significant correlation between the gender dominance ratio in the different industries and the usage of positive (or negative) emotional words in either gender in our dataset 
Conclusion
In this paper, we examined the task of predicting a social media user's industry  We introduced an annotated dataset of over 20,000 blog users and applied a content-based classifier in conjunction with two feature selection methods for an overall accuracy of up to 0 534, which represents a large improvement over the majority class baseline of 0 188 
We also demonstrated how the user metadata can be incorporated in our classifiers  Although concatenation of features drawn both from blog content and profile elements did not yield any clear improvements over the best individual classifiers, we found that stacking improves the prediction accuracy to an overall accuracy of 0 643, as measured on our test dataset  A more in-depth analysis showed that not all industries are equally easy to predict: while industries such as Real Estate and Religion are clearly distinguishable with accuracy figures over 0 80, others such as Banking are much harder to predict 
Finally, we presented a qualitative analysis to provide some insights into the language of different industries, which highlighted differences in the top-ranked words in each industry, word semantic similarities, and the relative frequency of emotionally charged words 
Acknowledgments
This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503)  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation 
Additional Examples of Word Similarities
Context 3: Introduction and related work
In recent years there has been increasing interest on the issue of disinformation spreading on online social media  Global concern over false (or "fake") news as a threat to modern democracies has been frequently raised–ever since 2016 US Presidential elections–in correspondence of events of political relevance, where the proliferation of manipulated and low-credibility content attempts to drive and influence people opinions BIBREF0BIBREF1BIBREF2BIBREF3 
Researchers have highlighted several drivers for the diffusion of such malicious phenomenon, which include human factors (confirmation bias BIBREF4, naive realism BIBREF5), algorithmic biases (filter bubble effect BIBREF0), the presence of deceptive agents on social platforms (bots and trolls BIBREF6) and, lastly, the formation of echo chambers BIBREF7 where people polarize their opinions as they are insulated from contrary perspectives 
The problem of automatically detecting online disinformation news has been typically formulated as a binary classification task (i e  credible vs non-credible articles), and tackled with a variety of different techniques, based on traditional machine learning and/or deep learning, which mainly differ in the dataset and the features they employ to perform the classification  We may distinguish three approaches: those built on content-based features, those based on features extracted from the social context, and those which combine both aspects  A few main challenges hinder the task, namely the impossibility to manually verify all news items, the lack of gold-standard datasets and the adversarial setting in which malicious content is created BIBREF3BIBREF6 
In this work we follow the direction pointed out in a few recent contributions on the diffusion of disinformation compared to traditional and objective information  These have shown that false news spread faster and deeper than true news BIBREF8, and that social bots and echo chambers play an important role in the diffusion of malicious content BIBREF6, BIBREF7  Therefore we focus on the analysis of spreading patterns which naturally arise on social platforms as a consequence of multiple interactions between users, due to the increasing trend in online sharing of news BIBREF0 
A deep learning framework for detection of fake news cascades is provided in BIBREF9, where the authors refer to BIBREF8 in order to collect Twitter cascades pertaining to verified false and true rumors  They employ geometric deep learning, a novel paradigm for graph-based structures, to classify cascades based on four categories of features, such as user profile, user activity, network and spreading, and content  They also observe that a few hours of propagation are sufficient to distinguish false news from true news with high accuracy  Diffusion cascades on Weibo and Twitter are analyzed in BIBREF10, where authors focus on highlighting different topological properties, such as the number of hops from the source or the heterogeneity of the network, to show that fake news shape diffusion networks which are highly different from credible news, even at early stages of propagation 
In this work, we consider the results of BIBREF11 as our baseline  The authors use off-the-shelf machine learning classifiers to accurately classify news articles leveraging Twitter diffusion networks  To this aim, they consider a set of basic features which can be qualitatively interpreted w r t to the social behavior of users sharing credible vs non-credible information  Their methodology is overall in accordance with BIBREF12, where authors successfully detect Twitter astroturfing content, i e  political campaigns disguised as spontaneous grassroots, with a machine learning framework based on network features 
In this paper, we propose a classification framework based on a multi-layer formulation of Twitter diffusion networks  For each article we disentangle different social interactions on Twitter, namely tweets, retweets, mentions, replies and quotes, to accordingly build a diffusion network composed of multiple layers (on for each type of interaction), and we compute structural features separately for each layer  We pick a set of global network properties from the network science toolbox which can be qualitatively explained in terms of social dimensions and allow us to encode different networks with a tuple of features  These include traditional indicators, e g  network density, number of strong/weak connected components and diameter, and more elaborated ones such as main K-core number BIBREF13 and structural virality BIBREF14  Our main research question is whether the use of a multi-layer, disentangled network yields a significant advance in terms of classification accuracy over a conventional single-layer diffusion network  Additionally, we are interested in understanding which of the above features, and in which layer, are most effective in the classification task 
We perform classification experiments with an off-the-shelf Logistic Regression model on two different datasets of mainstream and disinformation news shared on Twitter respectively in the United States and in Italy during 2019  In the former case we also account for political biases inherent to different news sources, referring to the procedure proposed in BIBREF2 to label different outlets  Overall we show that we are able to classify credible vs non-credible diffusion networks (and consequently news articles) with high accuracy (AUROC up to 94%), even when accounting for the political bias of sources (and training only on left-biased or right-biased articles)  We observe that the layer of mentions alone conveys useful information for the classification, denoting a different usage of this functionality when sharing news belonging to the two news domains  We also show that most discriminative features, which are relative to the breadth and depth of largest cascades in different layers, are the same across the two countries 
The outline of this paper is the following: we first formulate the problem and describe data collection, network representation and structural properties employed for the classification; then we provide experimental results–classification performances, layer and feature importance analyses and a temporal classification evaluation–and finally we draw conclusions and future directions 
Methodology ::: Disinformation and mainstream news
In this work we formulate our classification problem as follows: given two classes of news articles, respectively $D$ (disinformation) and $M$ (mainstream), a set of news articles $A_i$ and associated class labels $C_i \in \lbrace D,M\rbrace $, and a set of tweets $\Pi _i=\lbrace T_i^1, T_i^2,    \rbrace $ each of which contains an Uniform Resource Locator (URL) pointing explicitly to article $A_i$, predict the class $C_i$ of each article $A_i$  There is huge debate and controversy on a proper taxonomy of malicious and deceptive information BIBREF1BIBREF2BIBREF15BIBREF16BIBREF17BIBREF3BIBREF11  In this work we prefer the term disinformation to the more specific fake news to refer to a variety of misleading and harmful information  Therefore, we follow a source-based approach, a consolidated strategy also adopted by BIBREF6BIBREF16BIBREF2BIBREF1, in order to obtain relevant data for our analysis  We collected:
Disinformation articles, published by websites which are well-known for producing low-credibility content, false and misleading news reports as well as extreme propaganda and hoaxes and flagged as such by reputable journalists and fact-checkers;
Mainstream news, referring to traditional news outlets which deliver factual and credible information 
We believe that this is currently the most reliable classification approach, but it entails obvious limitations, as disinformation outlets may also publish true stories and likewise misinformation is sometimes reported on mainstream media  Also, given the choice of news sources, we cannot test whether our methodology is able to classify disinformation vs factual but not mainstream news which are published on niche, non-disinformation outlets 
Methodology ::: US dataset
We collected tweets associated to a dozen US mainstream news websites, i e  most trusted sources described in BIBREF18, with the Streaming API, and we referred to Hoaxy API BIBREF16 for what concerns tweets containing links to 100+ US disinformation outlets  We filtered out articles associated to less than 50 tweets  The resulting dataset contains overall $\sim $1 7 million tweets for mainstream news, collected in a period of three weeks (February 25th, 2019-March 18th, 2019), which are associated to 6,978 news articles, and $\sim $1 6 million tweets for disinformation, collected in a period of three months (January 1st, 2019-March 18th, 2019) for sake of balance of the two classes, which hold 5,775 distinct articles  Diffusion censoring effects BIBREF14 were correctly taken into account in both collection procedures  We provide in Figure FIGREF4 the distribution of articles by source and political bias for both news domains 
As it is reported that conservatives and liberals exhibit different behaviors on online social platforms BIBREF19BIBREF20BIBREF21, we further assigned a political bias label to different US outlets (and therefore news articles) following the procedure described in BIBREF2  In order to assess the robustness of our method, we performed classification experiments by training only on left-biased (or right-biased) outlets of both disinformation and mainstream domains and testing on the entire set of sources, as well as excluding particular sources that outweigh the others in terms of samples to avoid over-fitting 
Methodology ::: Italian dataset
For what concerns the Italian scenario we first collected tweets with the Streaming API in a 3-week period (April 19th, 2019-May 5th, 2019), filtering those containing URLs pointing to Italian official newspapers websites as described in BIBREF22; these correspond to the list provided by the association for the verification of newspaper circulation in Italy (Accertamenti Diffusione Stampa)  We instead referred to the dataset provided by BIBREF23 to obtain a set of tweets, collected continuously since January 2019 using the same Twitter endpoint, which contain URLs to 60+ Italian disinformation websites  In order to get balanced classes (April 5th, 2019-May 5th, 2019), we retained data collected in a longer period w r

ground_truth: claim, premise, backing, rebuttal, and refutation

claim, premise, backing, rebuttal, refutation

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What model did they use for their system?
Context 3: t to mainstream news  In both cases we filtered out articles with less than 50 tweets; overall this dataset contains $\sim $160k mainstream tweets, corresponding to 227 news articles, and $\sim $100k disinformation tweets, corresponding to 237 news articles  We provide in Figure FIGREF5 the distribution of articles according to distinct sources for both news domains  As in the US dataset, we took into account censoring effects BIBREF14 by excluding tweets published before (left-censoring) or after two weeks (right-censoring) from the beginning of the collection process 
The different volumes of news shared on Twitter in the two countries are due both to the different population size of US and Italy (320 vs 60 millions) but also to the different usage of Twitter platform (and social media in general) for news consumption BIBREF24  Both datasets analyzed in this work are available from the authors on request 
A crucial aspect in our approach is the capability to fully capturing sharing cascades on Twitter associated to news articles  It has been reported BIBREF25 that the Twitter streaming endpoint filters out tweets matching a given query if they exceed 1% of the global daily volume of shared tweets, which nowadays is approximately $5\cdot 10^8$; however, as we always collected less than $10^6$ tweets per day, we did not incur in this issue and we thus gathered 100% of tweets matching our query 
Methodology ::: Building diffusion networks
We built Twitter diffusion networks following an approach widely adopted in the literature BIBREF6BIBREF17BIBREF2  We remark that there is an unavoidable limitation in Twitter Streaming API, which does not allow to retrieve true re-tweeting cascades because re-tweets always point to the original source and not to intermediate re-tweeting users BIBREF8BIBREF14; thus we adopt the only viable approach based on Twitter's public availability of data  Besides, by disentangling different interactions with multiple layers we potentially reduce the impact of this limitation on the global network properties compared to the single-layer approach used in our baseline 
Using the notation described in BIBREF26  we employ a multi-layer representation for Twitter diffusion networks  Sociologists have indeed recognized decades ago that it is crucial to study social systems by constructing multiple social networks where different types of ties among same individuals are used BIBREF27  Therefore, for each news article we built a multi-layer diffusion network composed of four different layers, one for each type of social interaction on Twitter platform, namely retweet (RT), reply (R), quote (Q) and mention (M), as shown in Figure FIGREF11  These networks are not necessarily node-aligned, i e  users might be missing in some layers  We do not insert "dummy" nodes to represent all users as it would have severe impact on the global network properties (e g  number of weakly connected components)  Alternatively one may look at each multi-layer diffusion network as an ensemble of individual graphs BIBREF26; since global network properties are computed separately for each layer, they are not affected by the presence of any inter-layer edges 
In our multi-layer representation, each layer is a directed graph where we add edges and nodes for each tweet of the layer type, e g  for the RT layer: whenever user $a$ retweets account $b$ we first add nodes $a$ and $b$ if not already present in the RT layer, then we build an edge that goes from $b$ to $a$ if it does not exists or we increment the weight by 1  Similarly for the other layers: for the R layer edges go from user $a$ (who replies) to user $b$, for the Q layer edges go from user $b$ (who is quoted by) to user $a$ and for the M layer edges go from user $a$ (who mentions) to user $b$ 
Note that, by construction, our layers do not include isolated nodes; they correspond to "pure tweets", i e  tweets which have not originated any interactions with other users  However, they are present in our dataset, and their number is exploited for classification, as described below 
Methodology ::: Global network properties
We used a set of global network indicators which allow us to encode each network layer by a tuple of features  Then we simply concatenated tuples as to represent each multi-layer network with a single feature vector  We used the following global network properties:
Number of Strongly Connected Components (SCC): a Strongly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $u,v$ there is a path in each direction ($u\rightarrow v$, $v\rightarrow u$) 
Size of the Largest Strongly Connected Component (LSCC): the number of nodes in the largest strongly connected component of a given graph 
Number of Weakly Connected Components (WCC): a Weakly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $(u, v)$ there is a path $u \leftrightarrow v$ ignoring edge directions 
Size of the Largest Weakly Connected Component (LWCC): the number of nodes in the largest weakly connected component of a given graph 
Diameter of the Largest Weakly Connected Component (DWCC): the largest distance (length of the shortest path) between two nodes in the (undirected version of) largest weakly connected component of a graph 
Average Clustering Coefficient (CC): the average of the local clustering coefficients of all nodes in a graph; the local clustering coefficient of a node quantifies how close its neighbours are to being a complete graph (or a clique)  It is computed according to BIBREF28 
Main K-core Number (KC): a K-core BIBREF13 of a graph is a maximal sub-graph that contains nodes of internal degree $k$ or more; the main K-core number is the highest value of $k$ (in directed graphs the total degree is considered) 
Density (d): the density for directed graphs is $d=\frac{|E|}{|V||V-1|}$, where $|E|$ is the number of edges and $|N|$ is the number of vertices in the graph; the density equals 0 for a graph without edges and 1 for a complete graph 
Structural virality of the largest weakly connected component (SV): this measure is defined in BIBREF14 as the average distance between all pairs of nodes in a cascade tree or, equivalently, as the average depth of nodes, averaged over all nodes in turn acting as a root; for $|V| > 1$ vertices, $SV=\frac{1}{|V||V-1|}\sum _i\sum _j d_{ij}$ where $d_{ij}$ denotes the length of the shortest path between nodes $i$ and $j$  This is equivalent to compute the Wiener's index BIBREF29 of the graph and multiply it by a factor $\frac{1}{|V||V-1|}$  In our case we computed it for the undirected equivalent graph of the largest weakly connected component, setting it to 0 whenever $V=1$ 
We used networkx Python package BIBREF30 to compute all features  Whenever a layer is empty  we simply set to 0 all its features  In addition to computing the above nine features for each layer, we added two indicators for encoding information about pure tweets, namely the number T of pure tweets (containing URLs to a given news article) and the number U of unique users authoring those tweets  Therefore, a single diffusion network is represented by a vector with $9\cdot 4+2=38$ entries 
Methodology ::: Interpretation of network features and layers
Aforementioned network properties can be qualitatively explained in terms of social footprints as follows: SCC correlates with the size of the diffusion network, as the propagation of news occurs in a broadcast manner most of the time, i e  re-tweets dominate on other interactions, while LSCC allows to distinguish cases where such mono-directionality is somehow broken  WCC equals (approximately) the number of distinct diffusion cascades pertaining to each news article, with exceptions corresponding to those cases where some cascades merge together via Twitter interactions such as mentions, quotes and replies, and accordingly LWCC and DWCC equals the size and the depth of the largest cascade  CC corresponds to the level of connectedness of neighboring users in a given diffusion network whereas KC identifies the set of most influential users in a network and describes the efficiency of information spreading BIBREF17  Finally, d describes the proportions of potential connections between users which are actually activated and SV indicates whether a news item has gained popularity with a single and large broadcast or in a more viral fashion through multiple generations 
For what concerns different Twitter actions, users primarily interact with each other using retweets and mentions BIBREF20 
The former are the main engagement activity and act as a form of endorsement, allowing users to rebroadcast content generated by other users BIBREF31  Besides, when node B retweets node A we have an implicit confirmation that information from A appeared in B's Twitter feed BIBREF12  Quotes are simply a special case of retweets with comments 
Mentions usually include personal conversations as they allow someone to address a specific user or to refer to an individual in the third person; in the first case they are located at the beginning of a tweet and they are known as replies, otherwise they are put in the body of a tweet BIBREF20  The network of mentions is usually seen as a stronger version of interactions between Twitter users, compared to the traditional graph of follower/following relationships BIBREF32 
Experiments ::: Setup
We performed classification experiments using a basic off-the-shelf classifier, namely Logistic Regression (LR) with L2 penalty; this also allows us to compare results with our baseline

ground_truth: AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How is the political bias of different sources included in the model?
Context 3:  We applied a standardization of the features and we used the default configuration for parameters as described in scikit-learn package BIBREF33  We also tested other classifiers (such as K-Nearest Neighbors, Support Vector Machines and Random Forest) but we omit results as they give comparable performances  We remark that our goal is to show that a very simple machine learning framework, with no parameter tuning and optimization, allows for accurate results with our network-based approach 
We used the following evaluation metrics to assess the performances of different classifiers (TP=true positives, FP=false positives, FN=false negatives):
Precision = $\frac{TP}{TP+FP}$, the ability of a classifier not to label as positive a negative sample 
Recall = $\frac{TP}{TP+FN}$, the ability of a classifier to retrieve all positive samples 
F1-score = $2 \frac{\mbox{Precision} \cdot \mbox{Recall}}{\mbox{Precision} + \mbox{Recall}}$, the harmonic average of Precision and Recall 
Area Under the Receiver Operating Characteristic curve (AUROC); the Receiver Operating Characteristic (ROC) curve BIBREF34, which plots the TP rate versus the FP rate, shows the ability of a classifier to discriminate positive samples from negative ones as its threshold is varied; the AUROC value is in the range $[0, 1]$, with the random baseline classifier holding AUROC$=0 5$ and the ideal perfect classifier AUROC$=1$; thus larger AUROC values (and steeper ROCs) correspond to better classifiers 
In particular we computed so-called macro average–simple unweighted mean–of these metrics evaluated considering both labels (disinformation and mainstream)  We employed stratified shuffle split cross validation (with 10 folds) to evaluate performances 
Finally, we partitioned networks according to the total number of unique users involved in the sharing, i e  the number of nodes in the aggregated network represented with a single-layer representation considering together all layers and also pure tweets  A breakdown of both datasets according to size class (and political biases for the US scenario) is provided in Table 1 and Table 2 
Experiments ::: Classification performances
In Table 3 we first provide classification performances on the US dataset for the LR classifier evaluated on the size class described in Table 1  We can observe that in all instances our methodology performs better than a random classifier (50% AUROC), with AUROC values above 85% in all cases 
For what concerns political biases, as the classes of mainstream and disinformation networks are not balanced (e g , 1,292 mainstream and 4,149 disinformation networks with right bias) we employ a Balanced Random Forest with default parameters (as provided in imblearn Python package BIBREF35)  In order to test the robustness of our methodology, we trained only on left-biased networks or right-biased networks and tested on the entire set of sources (relative to the US dataset); we provide a comparison of AUROC values for both biases in Figure 4  We can notice that our multi-layer approach still entails significant results, thus showing that it can accurately distinguish mainstream news from disinformation regardless of the political bias  We further corroborated this result with additional classification experiments, that show similar performances, in which we excluded from the training/test set two specific sources (one at a time and both at the same time) that outweigh the others in terms of data samples–respectively "breitbart com" for right-biased sources and "politicususa com" for left-biased ones 
We performed classification experiments on the Italian dataset using the LR classifier and different size classes (we excluded $[1000, +\infty )$ which is empty); we show results for different evaluation metrics in Table 3  We can see that despite the limited number of samples (one order of magnitude smaller than the US dataset) the performances are overall in accordance with the US scenario  As shown in Table 4, we obtain results which are much better than our baseline in all size classes (see Table 4):
In the US dataset our multi-layer methodology performs much better in all size classes except for large networks ($[1000, +\infty )$ size class), reaching up to 13% improvement on smaller networks ($[0, 100)$ size class);
In the IT dataset our multi-layer methodology outperforms the baseline in all size classes, with the maximum performance gain (20%) on medium networks ($[100, 1000)$ size class); the baseline generally reaches bad performances compared to the US scenario 
Overall, our performances are comparable with those achieved by two state-of-the-art deep learning models for "fake news" detection BIBREF9BIBREF36 
Experiments ::: Layer importance analysis
In order to understand the impact of each layer on the performances of classifiers, we performed additional experiments considering separately each layer (we ignored T and U features relative to pure tweets)  In Table 5 we show metrics for each layer and all size classes, computed with a 10-fold stratified shuffle split cross validation, evaluated on the US dataset; in Figure 5 we show AUROC values for each layer compared with the general multi-layer approach  We can notice that both Q and M layers alone capture adequately the discrepancies of the two distinct news domains in the United States as they obtain good results with AUROC values in the range 75%-86%; these are comparable with those of the multi-layer approach which, nevertheless, outperforms them across all size classes 
We obtained similar performances for the Italian dataset, as the M layer obtains comparable performances w r t multi-layer approach with AUROC values in the range 72%-82%  We do not show these results for sake of conciseness 
Experiments ::: Feature importance analysis
We further investigated the importance of each feature by performing a $\chi ^2$ test, with 10-fold stratified shuffle split cross validation, considering the entire range of network sizes $[0, +\infty )$  We show the Top-5 most discriminative features for each country in Table 6 
We can notice the exact same set of features (with different relative orderings in the Top-3) in both countries; these correspond to two global network propertie–LWCC, which indicates the size of the largest cascade in the layer, and SCC, which correlates with the size of the network–associated to the same set of layers (Quotes, Retweets and Mentions) 
We further performed a $\chi ^2$ test to highlight the most discriminative features in the M layer of both countries, which performed equally well in the classification task as previously highlighted; also in this case we focused on the entire range of network sizes $[0, +\infty )$  Interestingly, we discovered exactly the same set of Top-3 features in both countries, namely LWCC, SCC and DWCC (which indicates the depth of the largest cascade in the layer) 
An inspection of the distributions of all aforementioned features revealed that disinformation news exhibit on average larger values than mainstream news 
We can qualitatively sum up these results as follows:
Sharing patterns in the two news domains exhibit discrepancies which might be country-independent and due to the content that is being shared 
Interactions in disinformation sharing cascades tends to be broader and deeper than in mainstream news, as widely reported in the literature BIBREF8BIBREF2BIBREF7 
Users likely make a different usage of mentions when sharing news belonging to the two domains, consequently shaping different sharing patterns 
Experiments ::: Temporal analysis
Similar to BIBREF9, we carried out additional experiments to answer the following question: how long do we need to observe a news spreading on Twitter in order to accurately classify it as disinformation or mainstream?
With this goal, we built several versions of our original dataset of multi-layer networks by considering in turn the following lifetimes: 1 hour, 6 hours, 12 hours, 1 day, 2 days, 3 days and 7 days; for each case, we computed the global network properties of the corresponding network and evaluated the LR classifier with 10-fold cross validation, separately for each lifetime (and considering always the entire set of networks)  We show corresponding AUROC values for both US and IT datasets in Figure 6 
We can see that in both countries news diffusion networks can be accurately classified after just a few hours of spreading, with AUROC values which are larger than 80% after only 6 hours of diffusion  These results are very promising and suggest that articles pertaining to the two news domains exhibit discrepancies in their sharing patterns that can be timely exploited in order to rapidly detect misleading items from factual information 
Conclusions
In this work we tackled the problem of the automatic classification of news articles in two domains, namely mainstream and disinformation news, with a language-independent approach which is based solely on the diffusion of news items on Twitter social platform  We disentangled different types of interactions on Twitter to accordingly build a multi-layer representation of news diffusion networks, and we computed a set of global network properties–separately for each layer–in order to encode each network with a tuple of features  Our goal was to investigate whether a multi-layer representation performs better than one layer BIBREF11, and to understand which of the features, observed at given layers, are most effective in the classification task 
Experiments with an off-the-shelf classifier such as Logistic Regression on datasets pertaining to two different media landscapes (US and Italy) yield very accurate classification results (AUROC up to 94%), even when accounting for the different political bias of news sources, which are far better than our baseline BIBREF11 with improvements up to 20%  Classification performances using single layers show that the layer of mentions alone entails better performance w r t other layers in both countries 
We also highlighted the most discriminative features across different layers in both countries; the results suggest that differences between the two news domains might be country-independent but rather due only to the typology of content shared, and that disinformation news shape broader and deeper cascades 
Additional experiments involving the temporal evolution of Twitter diffusion networks show that our methodology can accurate classify mainstream and disinformation news after a few hours of propagation on the platform 
Overall, our results prove that the topological features of multi-layer diffusion networks might be effectively exploited to detect online disinformation  We do not deny the presence of deceptive efforts to orchestrate the regular spread of information on social media via content amplification and manipulation BIBREF37BIBREF38  On the contrary, we postulate that such hidden forces might play to accentuate the discrepancies between the diffusion patterns of disinformation and mainstream news (and thus to make our methodology effective) 
In the future we aim to further investigate three directions: (1) employ temporal networks to represent news diffusion and apply classification techniques that take into account the sequential aspect of data (e g  recurrent neural networks); (2) carry out an extensive comparison of the diffusion of disinformation and mainstream news across countries to investigate deeper the presence of differences and similarities in sharing patterns; (3) leverage our network-based features in addition to state-of-the-art text-based approaches for "fake news" dete ction in order to deliver a real-world system to detect misleading and harmful information spreading on social media 

ground_truth: By assigning a political bias label to each news article and training only on left-biased or right-biased outlets of both disinformation and mainstream domains

we also account for political biases inherent to different news sources, referring to the procedure proposed in BIBREF2 to label different outlets. Overall we show that we are able to classify credible vs non-credible diffusion networks (and consequently news articles) with high accuracy (AUROC up to 94%), even when accounting for the political bias of sources (and training only on left-biased or right-biased articles). We observe that the layer of mentions alone conveys useful information for the classification, denoting a different usage of this functionality when sharing news belonging to the two news domains. We also show that most discriminative features, which are relative to the breadth and depth of largest cascades in different layers, are the same across the two countries.