bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Extremist Violence on Hateful Speech Online
Authors: Olteanu Alexandra , Castillo Carlos , Boy Jeremy , Varshney Kush
Published: November 2022

User-generated content online is shaped by many factors, including endogenous elements such as platform affordances and norms, as well as exogenous elements, in particular significant events. These impact what users say, how they say it, and when they say it. In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence. We anchor this study in a series of attacks involving Arabs and Muslims as perpetrators or victims, occurring in Western countries, that have been covered extensively by news media. These attacks have fueled intense policy debates around immigration in various fora, including online media, which have been marred by racist prejudice and hateful speech. The focus of our research is to model the effect of the attacks on the volume and type of hateful speech on two social media platforms, Twitter and Reddit. Among other findings, we observe that extremist violence tends to lead to an increase in online hate speech, particularly on messages directly advocating violence. Our research has implications for the way in which hate speech online is monitored and suggests ways in which it could be fought.
Collaboration in N-th Order Derivative Creation
Authors: Hironaka Shiori , Tsukuda Kosetsu , Hamasaki Masahiro , Goto Masataka
Published: November 2022

In derivative creation activity, where new content is created based on existing content, it has become popular for multiple creators to collaborate to create new derivative content. In this paper, we analyze the collaboration of music-related derivative videos on a video sharing service. Specifically, by using 83,496 collaborative videos created by 22,841 creators, we analyze the collaboration from the following two viewpoints: video popularity and creator activity. Our analysis results showed that collaborative videos tend to become more popular than non-collaborative ones, the collaboration is not a one-off activity but a continuous one, and creators who have collaboration experience are active for a longer time than inexperienced creators, etc.
The Gender Gap in Wikipedia Talk Pages
Authors: Cabrera Benjamin , Ross Björn , Dado Marielle , Heisel Maritta
Published: November 2022

Wikipedia is an important source of information in today's world. Yet, the lack of gender diversity in its community has been shown to affect the topics covered. Each Wikipedia article has a talk page that volunteer editors use to discuss proposed changes. Research on the gender bias has focused on article contribution and topic coverage, but not talk page activity. It has been suggested that the conflicts that take place in talk pages are especially intimidating for women, but this assertion has not been quantified yet. To fill this gap, we collected a dataset of all comments on Wikipedia talk pages, enriching it with gender information available from users who have chosen to disclose their gender on their user profiles or settings. Among the users active in talk pages, 49,387 indicated that they are male while only 5,996 indicated that they are female. The comments of these users make up for 4 million comments, approximately one quarter of all comments on Wikipedia. In addition, we observed that female participation varies by topic, reflecting traditional gender stereotypes: compared to Science, Technology, Engineering and Mathematics (STEM) topics, women were more active in categories such as Gender studies or Feminism. Results also indicate that a post on a talk page is 2.4\% less likely to be replied to if the author is female. Likewise, reply probability varies from topic to topic. These results provide quantitative support for a gender bias in Wikipedia talk pages, and serve as a basis for discussing why overall female participation is low.
Professional Gender Gaps Across US Cities
Authors: Haranko Karri , Zagheni Emilio , Garimella Kiran , Weber Ingmar
Published: November 2022

Gender imbalances in work environments have been a long-standing concern. Identifying the existence of such imbalances is key to designing policies to help overcome them. In this work, we study gender trends in employment across various dimensions in the United States. This is done by analyzing anonymous, aggregate statistics that were extracted from LinkedIn's advertising platform. The data contain the number of male and female LinkedIn users with respect to (i) location, (ii) age, (iii) industry and (iv) certain skills. We studied which of these categories correlate the most with high relative male or female presence on LinkedIn. In addition to examining the summary statistics of the LinkedIn data, we model the gender balance as a function of the different employee features using linear regression. Our results suggest that the gender gap, as measured using LinkedIn data, varies across all feature types, but the differences are most profound among industries and skills. A high correlation between gender ratios of people in our LinkedIn data set, and data provided by the US Bureau of Labor Statistics, serves as external validation for our results.
Discovering Political Slang in Readers' Comments
Authors: Hossain Nabil , Tran Thanh Thuy Trang , Kautz Henry
Published: November 2022

Slang is a continuously evolving phenomenon of language.The rise of social media has resulted in numerous slang terms circulating across the globe. In this paper, we aim to find novel and creative slang in the comments sections of online political news articles covering the 2016 US Presidential Election. First, we define creative political slang and partition it into sub-classes. Next, we extract a dataset of partisan news articles and comments ranging from the left wing to the right wing. Then, we develop PoliSlang, an unsupervised algorithm for detecting creative slang, evaluating its performance using expert human judgments. Finally, we use this algorithm to compare and contrast political slang usage by commenters across different news media.
Modeling and Visualizing Locus of Control with Facebook Language
Authors: Jaidka Kokil , Buffone Anneke , Eichstaedt Johannes , Rouhizadeh Masoud , Ungar Lyle
Published: November 2022

A body of literature has demonstrated that users' psychological traits such as personality can be predicted from their posts on social media. However, there is still a gap between the computational and descriptive analyses of the language features associated with different psychological traits, and their use by social scientists and psychologists to make deeper behavioral inferences. In this study, we aim to bridge this gap with a visualization that situates the language associated with one psychological trait in the context of other psychological dimensions. We predict Locus of Control (LoC), an individual's perception of personal control over events in their lives, from their Facebook language (F1=0.82). We then look at how language explains the relationship of LoC with consciousness and emotional stability.
Uncertainty During New Disease Outbreaks in Wikipedia
Authors: Al Tamime Reham , Hall Wendy , Giordano Richard
Published: November 2022

Wikipedia relies on a community of editors who construct articles related to various topics such as new disease outbreaks. New disease outbreaks are often characterized by emergent and changing information which, in turn, require Wikipedia editors to spend time and effort to retrieve and understand information that is sometimes ambiguous, complex, and contradictory. This makes a good case study for identifying different types of uncertainty, as well as delineating strategies for managing uncertainty. Despite recent research that explores how the general population expresses uncertainty during crisis events on social media platforms, limited work has focused explicitly on how Wikipedia editors express uncertainty while editing articles when knowledge or background information is either incomplete or unknown. Thus, the goals of this study are to identify types of uncertainty expressed by Wikipedia editors during new disease outbreaks, and examine different strategies deployed by Wikipedia editors to manage uncertainty. Our findings may be used to build a theoretically and empirically-driven framework to better understand and manage uncertainty on Wikipedia.
Hot Streaks on Social Media
Authors: Garimella Kiran , West Robert
Published: November 2022

Measuring the impact and success of human performance is common in various disciplines, including art, science, and sports. Quantifying impact also plays a key role on social media, where impact is usually defined as the reach of a user’s content as captured by metrics such as the number of views, likes, retweets, or shares. In this paper, we study entire careers of Twitter users to understand properties of impact. We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other. Second, users commonly have “hot streaks” of impact, i.e., extended periods of highimpact tweets. Third, impact tends to gradually build up before, and fall off after, a user’s most impactful tweet. We attempt to explain these characteristics using various properties measured on social media, including the user’s network, content, activity, and experience, and find that changes in impact are associated with significant changes in these properties. Our findings open interesting avenues for future research on virality and influence on social media.
Measuring Professional Skill Development in U.S. Cities Using Internet Search Queries
Authors: Jhaver Shagun , Cranshaw Justin , Counts Scott
Published: November 2022

Using a sample of 10 million skill development-related queries from a popular internet search engine, drawn from almost 400 U.S. cities and over a 5 year period (2012 - 2016), we characterize the skills people search for across these cities, and relate them to measures of economic output, including GDP and unemployment. Findings show that differences in the amount, type, and specialization of skill development searches distinguish economically thriving cities from those that are struggling, including explaining variance in future GDP growth after accounting for socioeconomic demographics such as income and poverty levels. Overall, internet-based skill development appears to contribute to rich cities getting richer, with initially well-off cities seeing more and more specialized skill-related searching that in turn aligns with future economic growth.
TOmCAT: Target-Oriented Crowd Review Attacks and Countermeasures
Authors: Kaghazgaran Parisa , Alfifi Majid , Caverlee James
Published: November 2022

Online platforms like Amazon, Yelp, and Regulations.gov give a voice to masses of users through reviews, comments, and ratings. However, this crowd-based feedback is susceptible to manipulation. To tackle this problem, most previous efforts have only indirectly sought to uncover targets of attacks by focusing on manipulation at the review or user level. Instead, this paper focuses on the challenge of countering target-oriented crowd attacks. We introduce a unique ground truth dataset of Amazon products that have been targeted for attack and identify two target-oriented attack patterns: (i) promotion attacks and (ii) restoration attacks. With these attacks in mind, we propose the TOmCAT detection framework based only on the timing and sequencing of product ratings. Although TOmCAT succeeds in uncovering targets of manipulation with high accuracy by addressing existing attacks, strategic attackers potentially can create hard-todetect behavioral patterns by undermining timing-based footprints. Hence, we further propose a complementary approach to TOmCAT called TOmCATSeq which is resistant against strategic manipulation.
A Social Media Study on the Effects of Psychiatric Medication Use
Authors: Saha Koustuv , Sugar Benjamin , Torous John , Abrahao Bruno , Kıcıman Emre , De Choudhury Munmun
Published: November 2022

Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual’s psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.
Environmental Changes and the Dynamics of Musical Identity
Authors: Way Samuel F. , Gil Santiago , Anderson Ian , Clauset Aaron
Published: November 2022

Musical tastes reflect our unique values and experiences, our relationships with others, and the places where we live. But as each of these things changes, do our tastes also change to reflect the present, or remain fixed, reflecting our past? Here, we investigate how where a person lives shapes their musical preferences, using geographic relocation to construct quasi-natural experiments that measure short- and long-term effects. Analyzing comprehensive data on over 16 million users on Spotify, we show that relocation within the United States has only a small impact on individuals’ tastes, which remain more similar to those of their past environments. We then show that the age gap between a person and the music they consume indicates that adolescence, and likely their environment during these years, shapes their lifelong musical tastes. Our results demonstrate the robustness of individuals’ musical identity, and shed new light on the development of preferences.
Understanding Cross-Cultural Visual Food Tastes with Online Recipe Platforms
Authors: Zhang Qing , Trattner Christoph , Ludwig Bernd , Elsweiler David
Published: November 2022

Traces of human behaviour with online recipe portals offer an opportunity to employ a data-driven approach to the study of food culture. Here, we focus on understanding visual aspects of food preference by analysing datasets from China, Germany, and US. Predictive modelling with low-level image features and Deep Neural Network image embeddings show differences in recipe images across datasets and between recipes with high and low appreciation within datasets. Our findings demonstrate the utility of the approach for studying visual aspects food culture.
“I Just Want to Feel Safe”: A Diary Study of Safety Perceptions on Social Media
Authors: Redmiles Elissa M. , Bodford Jessica , Blackwell Lindsay
Published: November 2022

Social media can increase social capital, provide entertainment, and enable meaningful discourse. However, threats to safety experienced on social media platforms can inhibt users’ ability to gain these benefits. Threats to safety—whether real or perceived—detract from the pleasure people get out of their online interactions and damage the quality of online social spaces. While prior work has individually explored specific threats to safety – privacy, security, harassment – in this work we more broadly capture and characterize the full breadth of day-to-day experiences that influence users’ overall perceptions of safety on social media. We explore these perceptions through a three-week diary study (n=39). We contribute a novel, multidimensional taxonomy of how social media users define ’safety’, centered around security, privacy, and community. We conclude with a discussion of how safety perceptions can be used as a metric for social media quality, and detail the potential for enhancing safety perception through communityenhancing affordances and algorithmic transparency.
Toward a Better Performance Evaluation Framework for Fake News Classification
Authors: Bozarth Lia , Budak Ceren
Published: November 2022

The rising prevalence of fake news and its alarming downstream impact have motivated both the industry and academia to build a substantial number of fake news classification models, each with its unique architecture. Yet, the research community currently lacks a comprehensive model evaluation framework that can provide multifaceted comparisons between these models beyond the simple evaluation metrics such as accuracy or f1 scores. In our work, we examine a representative subset of classifiers using a very simple set of performance evaluation and error analysis steps. We demonstrate that model performance varies considerably based on i) dataset, ii) evaluation archetype, and iii) performance metrics. Additionally, classifiers also demonstrate a potential bias against small and conservative-leaning credible news sites. Finally, models' performance varies based on external events and article topics. In sum, our results highlight the need to move toward systematic benchmarking.
Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision
Authors: Camacho-Collados Jose , Doval Yerai , Martínez-Cámara Eugenio , Espinosa-Anke Luis , Barbieri Francesco , Schockaert Steven
Published: November 2022

Cross-lingual embeddings represent the meaning of words from different languages in the same vector space. Recent work has shown that it is possible to construct such representations by aligning independently learned monolingual embedding spaces, and that accurate alignments can be obtained even without external bilingual data. In this paper we explore a research direction that has been surprisingly neglected in the literature: leveraging noisy user-generated text to learn cross-lingual embeddings particularly tailored towards social media applications. While the noisiness and informal nature of the social media genre poses additional challenges to cross-lingual embedding methods, we find that it also provides key opportunities due to the abundance of code-switching and the existence of a shared vocabulary of emoji and named entities. Our contribution consists of a very simple post-processing step that exploits these phenomena to significantly improve the performance of state-of-the-art alignment methods.
Generating Realistic Interest-Driven Information Cascades
Authors: Cinus Federico , Bonchi Francesco , Monti Corrado , Panisson André
Published: November 2022

We propose a model for the synthetic generation of information cascades in social media. In our model the information “memes” propagating in the social network are characterized by a probability distribution in a topic space, accompanied by a textual description, i.e., a bag of keywords coherent with the topic distribution. Similarly, every user of the social media is described by a vector of interests defined over the same topic space. Information cascades are governed by the topic of the meme, its level of virality, the interests of each user, community pressure, and social influence.The main technical challenge we face towards our goal is the generation of realistic interest vectors, given a known network structure and a tunable level of homophily. We tackle this problem by means of a method based on non-negative matrix factorization, which is shown experimentally to outperform non-trivial baselines based on label propagation and random-walk-based graph embedding.As we showcase in our experiments, our model offers a small set of simple and easily interpretable “knobs” which allow to study, in vitro, how each set of assumptions affects the resulting propagations. Finally, we show how to generate synthetic cascades that have similar macro-statistics to the real-world cascades for a dataset containing both the network and the cascades.
Learn2Link: Linking the Social and Academic Profiles of Researchers
Authors: Hadgu Asmelash Teka , Gundam Jayanth Kumar Reddy
Published: November 2022

People have presence across different information networks on the social web. The problem of user identity linking, is the task of establishing a connection between accounts of the same user across different networks. Solving this problem is useful for: personalized recommendations, cross platform data enrichment and verifying online information among others. In this paper, we propose a deep learning based approach that jointly models heterogeneous data: text content, network structure as well as profile names and images, in order to solve the user identity linking problem. We perform experiments on a real world problem of connecting the social profile (Twitter) and academic profile (DBLP) of researchers. Experimental results show that our joint model achieves a 97% F1 score outperforming state-of-the-art results that consider profile, content or network features only.
Towards Automated Sexual Violence Report Tracking
Authors: Hassan Naeemul , Poudel Amrit , Hale Jason , Hubacek Claire , Huq Khandaker Tasnim , Karmaker Santu Shubhra Kanti , Ahmed Syed Ishtiaque
Published: November 2022

Warning: This paper may contain trigger words that might be uncomfortable to some readers. Tracking sexual violence is a challenging task. In this paper, we present a supervised learning-based automated sexual violence report tracking model that is more scalable, and reliable than its crowdsource based counterparts. We define the sexual violence report tracking problem by considering victim, perpetrator contexts and the nature of the violence. We find that our model could identify sexual violence reports with a precision and recall of 80.4% and 83.4%, respectively. Moreover, we also applied the model during and after the #MeToo movement. Several interesting findings are discovered which are not easily identifiable from a shallow analysis.
Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections
Authors: Hua Yiqing , Ristenpart Thomas , Naaman Mor
Published: November 2022

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate. Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition.
Source Attribution: Recovering the Press Releases Behind Health Science News
Authors: MacLaughlin Ansel , Wihbey John , Bajak Aleszu , Smith David A.
Published: November 2022

We explore the task of intrinsic source attribution: inferring which portions of a derived document were adapted from an unobserved source document. Specifically, we model the relationship between news articles and their press release sources using a dataset of 64,784 health science news articles and 23,068 press releases. We approach the problem at the sentence level and work with science journalism professors to develop a four point Likert scale describing the extent to which a news article sentence is derived from the content in the corresponding press release. Because manual annotation of news article - press release pairs is time-consuming, we turn to a mix of expert, non-expert, and heuristic-based annotation to label our dataset. After a small pilot study, which found that humans, when only able to view the text of the news article, struggle to identify which content is derived or not, we compare four different sentence regression models on the task. We find that modeling a sentence's context in the entire document is important, with the best performing model, a sequence regression model with BERT token representations, achieving a spearman's ρ of 0.49 and NDCG@1 of 0.60 on the expert-labeled test set. Examining the model's predictions, we find that it successfully identifies copied or closely paraphrased sentences in articles with a mix of derived and original content, but struggles to differentiate between loosely paraphrased and original sentences in articles with mostly original writing.
“And We Will Fight for Our Race!” A Measurement Study of Genetic Testing Conversations on Reddit and 4chan
Authors: Mittos Alexandros , Zannettou Savvas , Blackburn Jeremy , De Cristofaro Emiliano
Published: November 2022

Progress in genomics has enabled the emergence of a booming market for “direct-to-consumer” genetic testing. Nowadays, companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. At the same time, alt- and far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic “purity.” In this paper, we present a measurement study shedding light on how genetic testing is being discussed on Web communities in Reddit and 4chan. We collect 1.3M comments posted over 27 months on the two platforms, using a set of 280 keywords related to genetic testing. We then use NLP and computer vision tools to identify trends, themes, and topics of discussion. Our analysis shows that genetic testing attracts a lot of attention on Reddit and 4chan, with discussions often including highly toxic language expressed through hateful, racist, and misogynistic comments. In particular, on 4chan's politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly antisemitic rhetoric, often conveyed through memes. Finally, we find that discussions build around user groups, from technology enthusiasts to communities promoting fringe political views.
“Trust Me, I Have a Ph.D.”: A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities
Authors: Park Kunwoo , Kwak Haewoon , Song Hyunho , Cha Meeyoung
Published: November 2022

Online communities adopt various reputation schemes to measure content quality. This study analyzes the effect of a new reputation scheme that exposes one's offline social status, such as an education degree, within an online community. We study two Reddit communities that adopted this scheme, whereby posts include tags identifying education status referred to as flairs, and we examine how the “transferred” social status affects the interactions among the users. We computed propensity scores to test whether flairs give ad-hoc authority to the adopters while minimizing the effects of confounding variables such as topics of content. The results show that exposing academic degrees is likely to lead to higher audience votes as well as larger discussion size, compared to the users without the disclosed identities, in a community that covers peer-reviewed scientific articles. In another community with a focus on casual science topics, exposing mere academic degrees did not obtain such benefits. Still, the users with the highest degree (e.g., Ph.D. or M.D.) were likely to receive more feedback from the audience. These findings suggest that reputation schemes that link the offline and online worlds could induce halo effects on feedback behaviors differently depending upon the community culture. We discuss the implications of this research for the design of future reputation mechanisms.
Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities
Authors: Saha Koustuv , Sharma Amit
Published: November 2022

Online mental health communities enable people to seek and provide support, and growing evidence shows the efficacy of community participation to cope with mental health distress. However, what factors of peer support lead to favorable psychosocial outcomes for individuals is less clear. Using a dataset of over 300K posts by ∼39K individuals on an online community TalkLife, we present a study to investigate the effect of several factors, such as adaptability, diversity, immediacy, and the nature of support. Unlike typical causal studies that focus on the effect of each treatment, we focus on the outcome and address the reverse causal question of identifying treatments that may have led to the outcome, drawing on case-control studies in epidemiology. Specifically, we define the outcome as an aggregate of affective, behavioral, and cognitive psychosocial change and identify Case (most improved) and Control (least improved) cohorts of individuals. Considering responses from peers as treatments, we evaluate the differences in the responses received by Case and Control, per matched clusters of similar individuals. We find that effective support includes complex language factors such as diversity, adaptability, and style, but simple indicators such as quantity and immediacy are not causally relevant. Our work bears methodological and design implications for online mental health platforms, and has the potential to guide suggestive interventions for peer supporters on these platforms.
Measuring Edge Sparsity on Large Social Networks
Authors: Smith J. David , Thai My T.
Published: November 2022

How strong are the connections between individuals? This is a fundamental question in the study of social networks. In this work, we take a topological view rooted in the idea of local sparsity to answer this question on large social networks to which we have only incomplete access. Prior approaches to measuring network structure are not applicable to this setting due to the strict limits on data availability. Therefore, we propose a new metric, the Edgecut Weight, for this task. This metric can be calculated efficiently in an online fashion, and we empirically show that it captures important elements of communities. Further, we demonstrate that the distribution of these weights characterizes connectivity on a network. Subsequently, we estimate the distribution of weights on Twitter and show both a lack of strong connections and a corresponding lack of community structure.
Diffusion of Scientific Articles across Online Platforms
Authors: Zakhlebin Igor , Horvát Emoke-Ágnes
Published: November 2022

Online platforms have become the primary source of information about scientific advances for the wider public. As online dissemination of scientific findings increasingly influences personal decision-making and government action, there is a growing necessity and interest in studying how people disseminate research findings online beyond one individual platform. In this paper, we study the simultaneous diffusion of scientific articles across major online platforms based on 63 million mentions of about 7.2 million articles spanning a 7-year period. First, we find commonalities between people sharing science and other content such as news articles and memes. Specifically, we find recurring bursts in the coverage of individual articles with initial bursts co-occurring in time across platforms. This allows for a ranking of individual platforms based on the speed at which they pick up scientific information. Second, we explore specifics of sharing science. We reconstruct the likely underlying structure of information diffusion and investigate the transfer of information about scientific articles within and across different platforms. In particular, we (i) study the role of different users in the dissemination of information to better understand who are the prime sharers of knowledge, (ii) explore the propagation of articles between platforms, and (iii) analyze the structural virality of individual information cascades to place science sharing on the spectrum between pure broadcasting and peer-to-peer diffusion. Our work provides the broadest study to date about the sharing of science online and builds the basis for an informed model of the dynamics of research coverage across platforms.
Identifying Misinformation from Website Screenshots
Authors: Abdali Sara , Gurav Rutuja , Menon Siddharth , Fonseca Daniel , Entezari Negin , Shah Neil , Papalexakis Evangelos E.
Published: November 2022

Can the look and the feel of a website give information about the trustworthiness of an article? In this paper, we propose to use a promising, yet neglected aspect in detecting the misinformativeness: the overall look of the domain web page. To capture this overall look, we take screenshots of news articles served by either misinformative or trustworthy web domains and leverage a tensor decomposition based semi-supervised classification technique.
The proposed approach i.e., VizFake is insensitive to a number of image transformations such as converting the image to grayscale, vectorizing the image and losing some parts of the screenshots. VizFake leverages a very small amount of known labels, mirroring realistic and practical scenarios, where labels (especially for known misinformative articles), are scarce and quickly become dated. The F1 score of VizFake on a dataset of 50k screenshots of news articles spanning more than 500 domains is roughly 85% using only 5% of ground truth labels. Furthermore, tensor representations of VizFake, obtained in an unsupervised manner, allow for exploratory analysis of the data that provides valuable insights into the problem.
Finally, we compare VizFake with deep transfer learning, since it is a very popular black-box approach for image classification and also well-known text based methods. VizFake achieves competitive accuracy with deep transfer learning models while being two orders of magnitude faster and not requiring laborious hyper-parameter tuning.
X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums
Authors: Guimarães Anna , Weikum Gerhard
Published: November 2022

Most online discussion forums capture user feedback in the form of "likes'' and other similar signals, but limit this to positive feedback. A few forums, most notably Reddit, offer both upvotes and downvotes. Reddit posts that received a large number of both upvotes and downvotes receive an explicit "controversiality'' marker, while heavily downvoted posts are hidden from the standard view of the discussion, and only shown upon explicit clicks.

This paper aims at understanding the nature and role of controversial posts in Reddit, considering four subreddits of very different natures: US politics, World politics, Relationships and Soccer. We design a feature space and devise a classifier to predict the occurrence of a controversial post given a prefix of a path in a discussion thread. Our findings include that these classifiers exhibit different behaviors in the four subreddits, and we identify key features for the respective cases. An in-depth analysis indicates that controversial posts do not arise as troll-like behavior, but are often due to a polarizing topic (mostly in US politics), off-topic content, or mentions of individual entities such as soccer players or clubs.
Political Depolarization of News Articles Using Attribute-Aware Word Embeddings
Authors: Liu Ruibo , Wang Lili , Jia Chenyan , Vosoughi Soroush
Published: November 2022

Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper, we focus on addressing one of the factors that contributes to this polarity, polarized media. We introduce a framework for depolarizing news articles. Given an article on a certain topic with a particular ideological slant (eg., liberal or conservative), the framework first detects polar language in the article and then generates a new article with the polar language replaced with neutral expressions. To detect polar words, we train a multi-attribute-aware word embedding model that is aware of ideology and topics on 360k full-length media articles. Then, for text generation, we propose a new algorithm called Text Annealing Depolarization Algorithm (TADA). TADA retrieves neutral expressions from the word embedding model that not only decrease ideological polarity but also preserve the original argument of the text while maintaining grammatical correctness. We evaluate our framework by comparing the depolarized output of our model in two modes, fully-automatic and semi-automatic, on 99 stories spanning 11 topics. Based on feedback from 161 human testers, our framework successfully depolarized 90.1% of paragraphs in semi-automatic mode and 78.3% of paragraphs in fully-automatic mode. Furthermore, 81.2% of the testers agree that the non-polar content information is well-preserved and 79% agree that depolarization does not harm semantic correctness when they compare the original text and the depolarized text. Our work shows that data-driven methods can help to locate political polarity and aid in the depolarization of articles.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Authors: Mohseni Sina , Yang Fan , Pentyala Shiva , Du Mengnan , Liu Yi , Lupfer Nic , Hu Xia , Ji Shuiwang , Ragan Eric
Published: November 2022

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust according to their perceptions of model limitations.
Exercise? I thought you said 'Extra Fries’: Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis
Authors: Pramanick Shraman , Akhtar Md Shad , Chakraborty Tanmoy
Published: November 2022

Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. According to a survey, 33% of social media users in age bracket [13-35] send memes every day, whereas more than 50% send every week. Some of these memes spread rapidly within a very short time-frame, and their virality depends on the novelty of their (textual and visual) content. A few of them convey positive messages, such as funny or motivational quotes; while others are meant to mock/hurt someone's feelings through sarcastic or offensive messages. Despite the appealing nature of memes and their rapid emergence on social media, effective analysis of memes has not been adequately attempted to the extent it deserves. Recently, in SemEval'20, a pioneering attempt has been made in this direction by organizing a shared task on `Memotion Analysis' (meme emotion analysis). As expected, the competition attracted more than 500 participants with the final submission of [23-32] systems across three sub-tasks. In this paper, we attempt to solve the same set of tasks suggested in the SemEval'20 - Memotion Analysis competition. We propose a multi-hop attention-based deep neural network framework, called MHA-Meme, whose prime objective is to leverage the spatial-domain correspondence between the visual modality (an image) and various textual segments to extract fine-grained feature representations for classification. We evaluate MHA-Meme on the `Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification. Our comparative study shows state-of-the-art performances of MHA-Meme for all three tasks compared to the top systems that participated in the competition. Unlike all the baselines which perform inconsistently across all three tasks, MHA-Meme outperforms baselines in all the tasks on average. Moreover, we validate the generalization of MHA-Meme on another set of manually annotated test samples and observe it to be consistent. Finally, we establish the interpretability of MHA-Meme.
Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey
Authors: Rashed Ammar , Kutlu Mucahid , Darwish Kareem , Elsayed Tamer , Bayrak Cansın
Published: November 2022

On June 24, 2018, Turkey conducted a highly consequential election in which the Turkish people elected their president and parliament in the first election under a new presidential system. During the election period, the Turkish people extensively shared their political opinions on Twitter. One aspect of polarization among the electorate was support for or opposition to the reelection of Recep Tayyip Erdoğan. In this paper, we present an unsupervised method for target-specific stance detection in a polarized setting, specifically Turkish politics, achieving 90% precision in identifying user stances, while maintaining more than 80% recall. The method involves representing users in an embedding space using Google's Convolutional Neural Network (CNN) based multilingual universal sentence encoder. The representations are then projected onto a lower dimensional space in a manner that reflects similarities and are consequently clustered. We show the effectiveness of our method in properly clustering users of divergent groups across multiple targets that include political figures, different groups, and parties. We perform our analysis on a large dataset of 108M Turkish election-related tweets along with the timeline tweets of 168k Turkish users, who authored 213M tweets. Given the resultant user stances, we are able to observe correlations between topics and compute topic polarization.
Computational Analysis of Bot Activity in the Asia-Pacific: A Comparative Study of Four National Elections
Authors: Uyheng Joshua , Carley Kathleen M.
Published: November 2022

Bot-driven electoral disinformation represents a major threat to democracies worldwide. Extant scholarship, however, tends to concentrate around Western contexts. This paper undertakes a comparative computational analysis of bot activity during four recent elections in the Asia-Pacific. Through a systematic, multi-level comparison of bot activity, we contribute novel insights about shared and distinct computational features of the disinformation landscapes within a significant yet understudied geopolitical region. Across case studies in Indonesia, the Philippines, Singapore, and Taiwan, we find non-negligible levels of bot activity: bots engage in higher levels of tweet production; interact with humans especially through mentions; tend to occupy denser and more isolated communities; use simpler and abusive language; and share partisan, irrelevant, or conspiratorial content. We conclude with implications for deepening and utilizing the analysis presented here as well as future directions for further cross-national work.
Imagine All the People: Characterizing Social Music Sharing on Reddit
Authors: Veselovsky Veniamin , Waller Isaac , Anderson Ashton
Published: November 2022

Music shapes our individual and collective identities, and in turn is shaped by the social and cultural contexts it occurs in. Qualitative approaches to the study of music have uncovered rich connections between music and social context but are limited in scale, while computational approaches process large amounts of musical data but lack information on the social contexts music is embedded in. In this work, we develop a set of neural embedding methods to understand the social contexts of online music sharing, and apply them to a novel dataset containing 1.3M instances of music sharing in Reddit communities. We find that the patterns of how people share music in public are related to, but often differ from, how they listen to music in private. We cluster artists into social genres that are based entirely on aggregate sharing patterns and reflect where artists are invoked. We also characterize the social and cultural contexts music is shared in by measuring associations with social dimensions such as age and political affiliation. Finally, we observe that a significant amount of sharing is attributable to extra-musical factors—additional meanings that people have associated with songs. We develop two methods to quantify the extra-musicality of music sharing. Our methodology is widely applicable to the study of online social contexts, and our results reveal novel cultural associations that contribute to a better understanding of the online music ecosystem.
A Large Open Dataset from the Parler Social Network
Authors: Aliapoulios Max , Bevensee Emmi , Blackburn Jeremy , Bradlyn Barry , De Cristofaro Emiliano , Stringhini Gianluca , Zannettou Savvas
Published: November 2022

Parler is as an ``alternative'' social network promoting itself as a service that allows to ``speak freely and express yourself openly, without fear of being deplatformed for your views.'' Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider.

This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.