pwc-archive/papers-with-abstracts huggingface dataset:
Mining Social Science Publications for Survey Variables
Authors: Andrea Zielinski, Peter Mutschke
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Paraphrase Identification, Question Answering, Survey, Variable Detection

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding a significant increase in performance over the random baseline.
Non-lexical Features Encode Political Affiliation on Twitter
Authors: Rachael Tatman, Leo Stewart, Am Paullada, alynne, Emma Spiro
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Previous work on classifying Twitter users{'} political alignment has mainly focused on lexical and social network features. This study provides evidence that political affiliation is also reflected in features which have been previously overlooked: users{'} discourse patterns (proportion of Tweets that are retweets or replies) and their rate of use of capitalization and punctuation. We find robust differences between politically left- and right-leaning communities with respect to these discourse and sub-lexical features, although they are not enough to train a high-accuracy classifier.
Modelling Participation in Small Group Social Sequences with Markov Rewards Analysis
Authors: Gabriel Murray
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

We explore a novel computational approach for analyzing member participation in small group social sequences. Using a complex state representation combining information about dialogue act types, sentiment expression, and participant roles, we explore which sequence states are associated with high levels of member participation. Using a Markov Rewards framework, we associate particular states with immediate positive and negative rewards, and employ a Value Iteration algorithm to calculate the expected value of all states. In our findings, we focus on discourse states belonging to team leaders and project managers which are either very likely or very unlikely to lead to participation from the rest of the group members.
Code-Switching as a Social Act: The Case of Arabic Wikipedia Talk Pages
Authors: Michael Yoder, Shruti Rijhwani, Carolyn Ros{\'e}, Lori Levin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Code-switching has been found to have social motivations in addition to syntactic constraints. In this work, we explore the social effect of code-switching in an online community. We present a task from the Arabic Wikipedia to capture language choice, in this case code-switching between Arabic and other languages, as a predictor of social influence in collaborative editing. We find that code-switching is positively associated with Wikipedia editor success, particularly borrowing technical language on pages with topics less directly related to Arabic-speaking regions.
How Does Twitter User Behavior Vary Across Demographic Groups?
Authors: Zach Wood-Doughty, Michael Smith, David Broniatowski, Mark Dredze
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Demographically-tagged social media messages are a common source of data for computational social science. While these messages can indicate differences in beliefs and behaviors between demographic groups, we do not have a clear understanding of how different demographic groups use platforms such as Twitter. This paper presents a preliminary analysis of how groups{'} differing behaviors may confound analyses of the groups themselves. We analyzed one million Twitter users by first inferring demographic attributes, and then measuring several indicators of Twitter behavior. We find differences in these indicators across demographic groups, suggesting that there may be underlying differences in how different demographic groups use Twitter.
Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter
Authors: Kristen Johnson, I-Ta Lee, Dan Goldwasser
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification

Politicians carefully word their statements in order to influence how others view an issue, a political strategy called framing. Simultaneously, these frames may also reveal the beliefs or positions on an issue of the politician. Simple language features such as unigrams, bigrams, and trigrams are important indicators for identifying the general frame of a text, for both longer congressional speeches and shorter tweets of politicians. However, tweets may contain multiple unigrams across different frames which limits the effectiveness of this approach. In this paper, we present a joint model which uses both linguistic features of tweets and ideological phrase indicators extracted from a state-of-the-art embedding-based model to predict the general frame of political tweets.
Dimensions of Abusive Language on Twitter
Authors: Isobelle Clarke, Jack Grieve
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Abusive Language

In this paper, we use a new categorical form of multidimensional register analysis to identify the main dimensions of functional linguistic variation in a corpus of abusive language, consisting of racist and sexist Tweets. By analysing the use of a wide variety of parts-of-speech and grammatical constructions, as well as various features related to Twitter and computer-mediated communication, we discover three dimensions of linguistic variation in this corpus, which we interpret as being related to the degree of interactive, antagonistic and attitudinal language exhibited by individual Tweets. We then demonstrate that there is a significant functional difference between racist and sexist Tweets, with sexists Tweets tending to be more interactive and attitudinal than racist Tweets.
Constructive Language in News Comments
Authors: Varada Kolhatkar, Maite Taboada
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59{\%} (baseline=49.44{\%}) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42{\%} of the non-constructive comments and 17.89{\%} of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments.
Rephrasing Profanity in Chinese Text
Authors: Hui-Po Su, Zhen-Jie Huang, Hao-Tsung Chang, Chuan-Jie Lin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Sentence

This paper proposes a system that can detect and rephrase profanity in Chinese text. Rather than just masking detected profanity, we want to revise the input sentence by using inoffensive words while keeping their original meanings. 29 of such rephrasing rules were invented after observing sentences on real-word social websites. The overall accuracy of the proposed system is 85.56{\%}
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words
Authors: Joan Serr{\`a}, Ilias Leontiadis, Dimitris Spathis, Gianluca Stringhini, Jeremy Blackburn, Athena Vakali
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Adversarial Attack, Hate Speech Detection, Text Categorization, text-classification, Text Classification, Word Embeddings

Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or non-blacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the {`}ability to describe{'} seen documents to the {`}ability to predict{'} unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4-11{\%}.
Legal Framework, Dataset and Annotation Schema for Socially Unacceptable Online Discourse Practices in Slovene
Authors: Darja Fi{\v{s}}er, Toma{\v{z}} Erjavec, Nikola Ljube{\v{s}}i{\'c}
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification

In this paper we present the legal framework, dataset and annotation schema of socially unacceptable discourse practices on social networking platforms in Slovenia. On this basis we aim to train an automatic identification and classification system with which we wish contribute towards an improved methodology, understanding and treatment of such practices in the contemporary, increasingly multicultural information society.
Abusive Language Detection on Arabic Social Media
Authors: Hamdy Mubarak, Kareem Darwish, Walid Magdy
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Abusive Language, General Classification

In this paper, we present our work on detecting abusive language on Arabic social media. We extract a list of obscene words and hashtags using common patterns used in offensive and rude communications. We also classify Twitter users according to whether they use any of these words or not in their tweets. We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean). We make this dataset freely available for research, in addition to the list of obscene words and hashtags. We are also publicly releasing a large corpus of classified user comments that were deleted from a popular Arabic news site due to violations the site{'}s rules and guidelines.
Vectors for Counterspeech on Twitter
Authors: Lucas Wright, Derek Ruths, Kelly P Dillon, Haji Mohammad Saleem, Susan Benesch
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

A study of conversations on Twitter found that some arguments between strangers led to favorable change in discourse and even in attitudes. The authors propose that such exchanges can be usefully distinguished according to whether individuals or groups take part on each side, since the opportunity for a constructive exchange of views seems to vary accordingly.
Detecting Nastiness in Social Media
Authors: Niloofar Safi Samghabadi, Suraj Maharjan, Alan Sprague, Raquel Diaz-Sprague, Thamar Solorio
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.
Technology Solutions to Combat Online Harassment
Authors: George Kennedy, Andrew McCollough, Edward Dixon, Alexei Bastidas, John Ryan, Chris Loo, Saurav Sahay
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams. Online harassment goes under-reported due to the reliance on humans to identify and report harassment, reporting that is further slowed by requirements to fill out forms providing context. In addition, the time for moderators to respond and apply human judgment can take days, but response times in terms of minutes are needed in the online context. Though some of the major social media companies have been doing proprietary work in automating the detection of harassment, there are few tools available for use by the public. In addition, the amount of labeled online harassment data and availability of cross-platform online harassment datasets is limited. We present the methodology used to create a harassment dataset and classifier and the dataset used to help the system learn what harassment looks like.
Using Convolutional Neural Networks to Classify Hate-Speech
Authors: Bj{\"o}rn Gamb{\"a}ck, Utpal Kumar Sikdar
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentiment Analysis, text-classification, Text Classification

The paper introduces a deep learning-based Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convolutional Neural Network models were trained on resp. character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams. The feature set was down-sized in the networks by max-pooling, and a softmax function used to classify tweets. Tested by 10-fold cross-validation, the model based on word2vec embeddings performed best, with higher precision than recall, and a 78.3{\%} F-score.
Illegal is not a Noun: Linguistic Form for Detection of Pejorative Nominalizations
Authors: Alexis Palmer, Melissa Robinson, Kristy K. Phillips
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Abusive Language, Form, POS

This paper focuses on a particular type of abusive language, targeting expressions in which typically neutral adjectives take on pejorative meaning when used as nouns - compare {`}gay people{'} to {`}the gays{'}. We first collect and analyze a corpus of hand-curated, expert-annotated pejorative nominalizations for four target adjectives: female, gay, illegal, and poor. We then collect a second corpus of automatically-extracted and POS-tagged, crowd-annotated tweets. For both corpora, we find support for the hypothesis that some adjectives, when nominalized, take on negative meaning. The targeted constructions are non-standard yet widely-used, and part-of-speech taggers mistag some nominal forms as adjectives. We implement a tool called NomCatcher to correct these mistaggings, and find that the same tool is effective for identifying new adjectives subject to transformation via nominalization into abusive language.
A Cross-modal Review of Indicators for Depression Detection Systems
Authors: Michelle Morales, Stefan Scherer, Rivka Levitan
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Depression Detection

Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.
In your wildest dreams: the language and psychological features of dreams
Authors: Kate Niederhoffer, Jonathan Schler, Patrick Crutchley, Kate Loveys, Glen Coppersmith
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Decision Making, General Classification, text-classification, Text Classification

In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.
A Corpus Analysis of Social Connections and Social Isolation in Adolescents Suffering from Depressive Disorders
Authors: Jia-Wen Guo, Danielle L. Mowery, Djin Lai, Katherine Sward, Mike Conway
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Social connection and social isolation are associated with depressive symptoms, particularly in adolescents and young adults, but how these concepts are documented in clinical notes is unknown. This pilot study aimed to identify the topics relevant to social connection and isolation by analyzing 145 clinical notes from patients with depression diagnosis. We found that providers, including physicians, nurses, social workers, and psychologists, document descriptions of both social connection and social isolation.
Monitoring Tweets for Depression to Detect At-risk Users
Authors: Zunaira Jamil, Diana Inkpen, Prasadith Buddhitha, Kenton White
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

We propose an automated system that can identify at-risk users from their public social media activity, more specifically, from Twitter. The data that we collected is from the {\#}BellLetsTalk campaign, which is a wide-reaching, multi-year program designed to break the silence around mental illness and support mental health across Canada. To achieve our goal, we trained a user-level classifier that can detect at-risk users that achieves a reasonable precision and recall. We also trained a tweet-level classifier that predicts if a tweet indicates depression. This task was much more difficult due to the imbalanced data. In the dataset that we labeled, we came across 5{\%} depression tweets and 95{\%} non-depression tweets. To handle this class imbalance, we used undersampling methods. The resulting classifier had high recall, but low precision. Therefore, we only use this classifier to compute the estimated percentage of depressed tweets and to add this value as a feature for the user-level classifier.
Investigating Patient Attitudes Towards the use of Social Media Data to Augment Depression Diagnosis and Treatment: a Qualitative Study
Authors: Jude Mikal, Samantha Hurst, Mike Conway
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

In this paper, we use qualitative research methods to investigate the attitudes of social media users towards the (opt-in) integration of social media data with routine mental health care and diagnosis. Our investigation was based on secondary analysis of a series of five focus groups with Twitter users, including three groups consisting of participants with a self-reported history of depression, and two groups consisting of participants without a self reported history of depression. Our results indicate that, overall, research participants were enthusiastic about the possibility of using social media (in conjunction with automated Natural Language Processing algorithms) for mood tracking under the supervision of a mental health practitioner. However, for at least some participants, there was skepticism related to how well social media represents the mental health of users, and hence its usefulness in the clinical context.
Natural-language Interactive Narratives in Imaginal Exposure Therapy for Obsessive-Compulsive Disorder
Authors: Melissa Roemmele, Paola Mardo, Andrew Gordon
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Obsessive-compulsive disorder (OCD) is an anxiety-based disorder that affects around 2.5{\%} of the population. A common treatment for OCD is exposure therapy, where the patient repeatedly confronts a feared experience, which has the long-term effect of decreasing their anxiety. Some exposures consist of reading and writing stories about an imagined anxiety-provoking scenario. In this paper, we present a technology that enables patients to interactively contribute to exposure stories by supplying natural language input (typed or spoken) that advances a scenario. This interactivity could potentially increase the patient{'}s sense of immersion in an exposure and contribute to its success. We introduce the NLP task behind processing inputs to predict new events in the scenario, and describe our initial approach. We then illustrate the future possibility of this work with an example of an exposure scenario authored with our application.
Detecting Anxiety through Reddit
Authors: Judy Hanwen Shen, Frank Rudzicz
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Language Modeling, Language Modelling, Word Embeddings

Previous investigations into detecting mental illnesses through social media have predominately focused on detecting depression through Twitter corpora. In this paper, we study anxiety disorders through personal narratives collected through the popular social media website, Reddit. We build a substantial data set of typical and anxiety-related posts, and we apply N-gram language modeling, vector embeddings, topic analysis, and emotional norms to generate features that accurately classify posts related to binary levels of anxiety. We achieve an accuracy of 91{\%} with vector-space word embeddings, and an accuracy of 98{\%} when combined with lexicon-based features.
Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language
Authors: Kate Loveys, Patrick Crutchley, Emily Wyatt, Glen Coppersmith
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.
Cost Weighting for Neural Machine Translation Domain Adaptation
Authors: Boxing Chen, Colin Cherry, George Foster, Samuel Larkin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Domain Adaptation, Machine Translation, Translation

In this paper, we propose a new domain adaptation technique for neural machine translation called cost weighting, which is appropriate for adaptation scenarios in which a small in-domain data set and a large general-domain data set are available. Cost weighting incorporates a domain classifier into the neural machine translation training algorithm, using features derived from the encoder representation in order to distinguish in-domain from out-of-domain data. Classifier probabilities are used to weight sentences according to their domain similarity when updating the parameters of the neural translation model. We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting. Experiments on two large-data tasks show that both the traditional techniques and our novel proposal lead to significant gains, with cost weighting outperforming the traditional methods.