pwc-archive/papers-with-abstracts huggingface dataset:
A Corpus of Turkish Offensive Language on Social Media
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: text-classification, Text Classification

This paper introduces a corpus of Turkish offensive language. To our knowledge, this is the first corpus of offensive language for Turkish. The corpus consists of randomly sampled micro-blog posts from Twitter. The annotation guidelines are based on a careful review of the annotation practices of recent efforts for other languages. The corpus contains 36 232 tweets sampled randomly from the Twitter stream during a period of 18 months between Apr 2018 to Sept 2019. We found approximately 19 {\%} of the tweets in the data contain some type of offensive language, which is further subcategorized based on the target of the offense. We describe the annotation process, discuss some interesting aspects of the data, and present results of automatically classifying the corpus using state-of-the-art text classification methods. The classifiers achieve 77.3 {\%} F1 score on identifying offensive tweets, 77.9 {\%} F1 score on determining whether a given offensive document is targeted or not, and 53.0 {\%} F1 score on classifying the targeted offensive documents into three subcategories.
From Witch's Shot to Music Making Bones - Resources for Medical Laymen to Technical Language and Vice Versa
Authors: Laura Seiffe, Oliver Marten, Michael Mikhailov, Sven Schmeier, Sebastian M{\"o}ller, Rol Roller
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Descriptive

Many people share information in social media or forums, like food they eat, sports activities they do or events which have been visited. Information we share online unveil directly or indirectly information about our lifestyle and health situation. Particularly when text input is getting longer or multiple messages can be linked to each other. Those information can be then used to detect possible risk factors of diseases or adverse drug reactions of medications. However, as most people are not medical experts, language used might be more descriptive rather than the precise medical expression as medics do. To detect and use those relevant information, laymen language has to be translated and/or linked against the corresponding medical concept. This work presents baseline data sources in order to address this challenge for German language. We introduce a new dataset which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data.
I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language
Authors: Tommaso Caselli, Valerio Basile, Jelena Mitrovi{\'c}, Inga Kartoziya, Michael Granitzer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abusive Language

Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors (degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and interaction across different phenomena).
A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection
Authors: Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, Soon-gyo Jung, Bernard J Jansen, Joni Salminen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Access to social media often enables users to engage in conversation with limited accountability. This allows a user to share their opinions and ideology, especially regarding public content, occasionally adopting offensive language. This may encourage hate crimes or cause mental harm to targeted individuals or groups. Hence, it is important to detect offensive comments in social media platforms. Typically, most studies focus on offensive commenting in one platform only, even though the problem of offensive language is observed across multiple platforms. Therefore, in this paper, we introduce and make publicly available a new dialectal Arabic news comment dataset, collected from multiple social media platforms, including Twitter, Facebook, and YouTube. We follow two-step crowd-annotator selection criteria for low-representative language annotation task in a crowdsourcing platform. Furthermore, we analyze the distinctive lexical content along with the use of emojis in offensive comments. We train and evaluate the classifiers using the annotated multi-platform dataset along with other publicly available data. Our results highlight the importance of multiple platform dataset for (a) cross-platform, (b) cross-domain, and (c) cross-dialect generalization of classifier performance.
Twitter Trend Extraction: A Graph-based Approach for Tweet and Hashtag Ranking, Utilizing No-Hashtag Tweets
Authors: Zahra Majdabadi, Behnam Sabeti, Preni Golazizian, Seyed Arad Ashrafi Asli, Omid Momenzadeh, reza fahmi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Twitter has become a major platform for users to express their opinions on any topic and engage in debates. User debates and interactions usually lead to massive content regarding a specific topic which is called a Trend. Twitter trend extraction aims at finding these relevant groups of content that are generated in a short period. The most straightforward approach for this problem is using Hashtags, however, tweets without hashtags are not considered this way. In order to overcome this issue and extract trends using all tweets, we propose a graph-based approach where graph nodes represent tweets as well as words and hashtags. More specifically, we propose a modified version of RankClus algorithm to extract trends from the constructed tweets graph. The proposed approach is also capable of ranking tweets, words and hashtags in each trend with respect to their importance and relevance to the topic. The proposed algorithm is used to extract trends from several twitter datasets, where it produced consistent and coherent results.
A French Corpus for Event Detection on Twitter
Authors: B{\'e}atrice Mazoyer, Julia Cag{\'e}, Nicolas Herv{\'e}, C{\'e}line Hudelot
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Event Detection

We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweets manually annotated by three annotators as related or unrelated to a given event. The 243 events were selected both from press articles and from subjects trending on Twitter during the annotation period (July to August 2018). In total, more than 95,000 tweets were annotated as related to one of the selected events. We also provide the titles and URLs of 15,500 news articles automatically detected as related to these events. In addition to this corpus, we detail the results of our event detection experiments on both this dataset and another publicly available dataset of tweets in English. We ran extensive tests with different types of text embeddings and a standard Topic Detection and Tracking algorithm, and detail our evaluation method. We show that tf-idf vectors allow the best performance for this task on both corpora. These results are intended to serve as a baseline for researchers wishing to test their own event detection systems on our corpus.
Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling
Authors: Arindam Chatterjere, Vineeth Guptha, Parul Chopra, Amitava Das
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling

Code-Mixing (CM) or language mixing is a social norm in multilingual societies. CM is quite prevalent in social media conversations in multilingual regions like - India, Europe, Canada and Mexico. In this paper, we explore the problem of Language Modeling (LM) for code-mixed Hinglish text. In recent times, there have been several success stories with neural language modeling like Generative Pre-trained Transformer (GPT) (Radford et al., 2019), Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) etc.. Hence, neural language models have become the new holy grail of modern NLP, although LM for CM is an unexplored area altogether. To better understand the problem of LM for CM, we initially experimented with several statistical language modeling techniques and consequently experimented with contemporary neural language models. Analysis shows switching-points are the main challenge for the LMCM performance drop, therefore in this paper we introduce the idea of minority positive sampling to selectively induce more sample to achieve better performance. On the contrary, all neural language models demand a huge corpus to train on for better performance. Finally, we are reporting a perplexity of 139 for Hinglish (Hindi-English language pair) LMCM using statistical bi-directional techniques.
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media
Authors: Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute to the expression of hatred and to the abusive effect, causing harm and offense. However, swearing is multifaceted and is often used in casual contexts, also with positive social functions. In this study, we explore the phenomenon of swearing in Twitter conversations, taking the possibility of predicting the abusiveness of a swear word in a tweet context as the main investigation perspective. We developed the Twitter English corpus SWAD (Swear Words Abusiveness Dataset), where abusive swearing is manually annotated at the word level. Our collection consists of 1,511 unique swear words from 1,320 tweets. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We also present the results of a glass box ablation study in order to investigate which lexical, syntactic, and affective features are more informative towards the automatic prediction of the function of swearing.
Detecting Troll Tweets in a Bilingual Corpus
Authors: Lin Miao, Mark Last, Marina Litvak
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Authorship Verification, Feature Engineering, General Classification, Misinformation, Translation

During the past several years, a large amount of troll accounts has emerged with efforts to manipulate public opinion on social network sites. They are often involved in spreading misinformation, fake news, and propaganda with the intent of distracting and sowing discord. This paper aims to detect troll tweets in both English and Russian assuming that the tweets are generated by some {``}troll farm.{''} We reduce this task to the authorship verification problem of determining whether a single tweet is authored by a {``}troll farm{''} account or not. We evaluate a supervised classification approach with monolingual, cross-lingual, and bilingual training scenarios, using several machine learning algorithms, including deep learning. The best results are attained by the bilingual learning, showing the area under the ROC curve (AUC) of 0.875 and 0.828, for tweet classification in English and Russian test sets, respectively. It is noteworthy that these results are obtained using only raw text features, which do not require manual feature engineering efforts. In this paper, we introduce a resource of English and Russian troll tweets containing original tweets and translation from English to Russian, Russian to English. It is available for academic purposes.
Collecting Tweets to Investigate Regional Variation in Canadian English
Authors: Filip Miletic, Anne Przewozny-Desriaux, Ludovic Tanguy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present a 78.8-million-tweet, 1.3-billion-word corpus aimed at studying regional variation in Canadian English with a specific focus on the dialect regions of Toronto, Montreal, and Vancouver. Our data collection and filtering pipeline reflects complex design criteria, which aim to allow for both data-intensive modeling methods and user-level variationist sociolinguistic analysis. It specifically consists in identifying Twitter users from the three cities, crawling their entire timelines, filtering the collected data in terms of user location and tweet language, and automatically excluding near-duplicate content. The resulting corpus mirrors national and regional specificities of Canadian English, it provides sufficient aggregate and user-level data, and it maintains a reasonably balanced distribution of content across regions and users. The utility of this dataset is illustrated by two example applications: the detection of regional lexical and topical variation, and the identification of contact-induced semantic shifts using vector space models. In accordance with Twitter{'}s developer policy, the corpus will be publicly released in the form of tweet IDs.
DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter
Authors: Ines Abbes, Wajdi Zaghouani, Omaima El-Hardlo, Faten Ashour
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Identifying irony in user-generated social media content has a wide range of applications; however to date Arabic content has received limited attention. To bridge this gap, this study builds a new open domain Arabic corpus annotated for irony detection. We query Twitter using irony-related hashtags to collect ironic messages, which are then manually annotated by two linguists according to our working definition of irony. Challenges which we have encountered during the annotation process reflect the inherent limitations of Twitter messages interpretation, as well as the complexity of Arabic and its dialects. Once published, our corpus will be a valuable free resource for developing open domain systems for automatic irony recognition in Arabic language and its dialects in social media text.
Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing
Authors: Rob van der Goot, Alan Ramponi, Tommaso Caselli, Michele Cafagna, Lorenzo De Mattei
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Dependency Parsing, Lexical Normalization

Lexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data. In this paper, we discuss the creation of a lexical normalization dataset for Italian. After two rounds of annotation, a Cohen{'}s kappa score of 78.64 is obtained. During this process, we also analyze the inter-annotator agreement for this task, which is only rarely done on datasets for lexical normalization,and when it is reported, the analysis usually remains shallow. Furthermore, we utilize this dataset to train a lexical normalization model and show that it can be used to improve dependency parsing of social media data. All annotated data and the code to reproduce the results are available at: http://bitbucket.org/robvanderg/normit.
Small Town or Metropolis? Analyzing the Relationship between Population Size and Language
Authors: Amy Rechkemmer, Steven Wilson, Rada Mihalcea
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cultural Vocal Bursts Intensity Prediction

The variance in language used by different cultures has been a topic of study for researchers in linguistics and psychology, but often times, language is compared across multiple countries in order to show a difference in culture. As a geographically large country that is diverse in population in terms of the background and experiences of its citizens, the U.S. also contains cultural differences within its own borders. Using a set of over 2 million posts from distinct Twitter users around the country dating back as far as 2014, we ask the following question: is there a difference in how Americans express themselves online depending on whether they reside in an urban or rural area? We categorize Twitter users as either urban or rural and identify ideas and language that are more commonly expressed in tweets written by one population over the other. We take this further by analyzing how the language from specific cities of the U.S. compares to the language of other cities and by training predictive models to predict whether a user is from an urban or rural area. We publicly release the tweet and user IDs that can be used to reconstruct the dataset for future studies in this direction.
Inferring Social Media Users' Mental Health Status from Multimodal Information
Authors: Zhentao Xu, Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification

Worldwide, an increasing number of people are suffering from mental health disorders such as depression and anxiety. In the United States alone, one in every four adults suffers from a mental health condition, which makes mental health a pressing concern. In this paper, we explore the use of multimodal cues present in social media posts to predict users{'} mental health status. Specifically, we focus on identifying social media activity that either indicates a mental health condition or its onset. We collect posts from Flickr and apply a multimodal approach that consists of jointly analyzing language, visual, and metadata cues and their relation to mental health. We conduct several classification experiments aiming to discriminate between (1) healthy users and users affected by a mental health illness; and (2) healthy users and users prone to mental illness. Our experimental results indicate that using multiple modalities can improve the performance of this classification task as compared to the use of one modality at a time, and can provide important cues into a user{'}s mental status.
Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?
Authors: Kelly Dekker, Rob van der Goot
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Lexical Normalization, Sentence, Word Embeddings

Social media is a valuable data resource for various natural language processing (NLP) tasks. However, standard NLP tools were often designed with standard texts in mind, and their performance decreases heavily when applied to social media data. One solution to this problem is to adapt the input text to a more standard form, a task also referred to as normalization. Automatic approaches to normalization have shown that they can be used to improve performance on a variety of NLP tasks. However, all of these systems are supervised, thereby being heavily dependent on the availability of training data for the correct language and domain. In this work, we attempt to overcome this dependence by automatically generating training data for lexical normalization. Starting with raw tweets, we attempt two directions, to insert non-standardness (noise) and to automatically normalize in an unsupervised setting. Our best results are achieved by automatically inserting noise. We evaluate our approaches by using an existing lexical normalization system; our best scores are achieved by custom error generation system, which makes use of some manually created datasets. With this system, we score 94.29 accuracy on the test data, compared to 95.22 when it is trained on human-annotated data. Our best system which does not depend on any type of annotation is based on word embeddings and scores 92.04 accuracy. Finally, we perform an experiment in which we asked humans to predict whether a sentence was written by a human or generated by our best model. This experiment showed that in most cases it is hard for a human to detect automatically generated sentences.
A Corpus of German Reddit Exchanges (GeRedE)
Authors: Andreas Blombach, Natalie Dykes, Philipp Heinrich, Besim Kabashi, Thomas Proisl
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

GeRedE is a 270 million token German CMC corpus containing approximately 380,000 submissions and 6,800,000 comments posted on Reddit between 2010 and 2018. Reddit is a popular online platform combining social news aggregation, discussion and micro-blogging. Starting from a large, freely available data set, the paper describes our approach to filter out German data and further pre-processing steps, as well as which metadata and annotation layers have been included so far. We explore the Reddit sphere, what makes the German data linguistically peculiar, and how some of the communities within Reddit differ from one another. The CWB-indexed version of our final corpus is available via CQPweb, and all our processing scripts as well as all manual annotation and automatic language classification can be downloaded from GitHub.
French Tweet Corpus for Automatic Stance Detection
Authors: Marc Evrard, R{\'e}mi Uro, Nicolas Herv{\'e}, B{\'e}atrice Mazoyer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Fake News Detection, Stance Detection

The automatic stance detection task consists in determining the attitude expressed in a text toward a target (text, claim, or entity). This is a typical intermediate task for the fake news detection or analysis, which is a considerably widespread and a particularly difficult issue to overcome. This work aims at the creation of a human-annotated corpus for the automatic stance detection of tweets written in French. It exploits a corpus of tweets collected during July and August 2018. To the best of our knowledge, this is the first freely available stance annotated tweet corpus in the French language. The four classes broadly adopted by the community were chosen for the annotation: support, deny, query, and comment with the addition of the ignore class. This paper presents the corpus along with the tools used to build it, its construction, an analysis of the inter-rater reliability, as well as the challenges and questions that were raised during the building process.