bluuebunny/crossref_metadata_2025 huggingface dataset:
The Pushshift Reddit Dataset
Authors: Baumgartner Jason , Zannettou Savvas , Keegan Brian , Squire Megan , Blackburn Jeremy
Published: November 2022

Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves.Reddit, the so called “front page of the Internet,” in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically.In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.
Generally Curious: Thematically Distinct Datasets of General Threads on 4chan/pol/
Authors: Jokubauskaitė Emilija , Peeters Stijn
Published: November 2022

Over the second half of the 2010s, the /pol/ (‘politically incorrect’) forum on the 4chan image board has emerged as a space within which various extreme political ideologies are discussed and cultivated, occasionally informing off-site acts of political extremism. While previous research has often studied this space as a unified whole, it is relevant to more specifically demarcate different publics within 4chan’s /pol/ board, apart from studying it as an ‘amorphous blob’. This paper focuses specifically on ‘generals’ - recurring threads with a specific thematic focus identified by a particular vernacular phrase or tag. By identifying them it is possible to partition the board’s archive into multiple distinct datasets comprising discussions about a particular topic, such as Donald Trump, the Syria war, or British politics. We provide a dataset containing 58,841 opening posts and 13,697,738 replies to those, divided over 329 thematically distinct general thread collections. In this paper we outline our data collection and query protocol, the structure of the data and its rationale, as well as a number of suggested research uses for this new data.
P4KxSpotify: A Dataset of Pitchfork Music Reviews and Spotify Musical Features
Authors: Pinter Anthony T. , Paul Jacob M. , Smith Jessie , Brubaker Jed R.
Published: November 2022

Algorithmically driven curation and recommendation systems like those employed by Spotify have become more ubiquitous for surfacing content that people might want hear. However, expert reviews continue to have a measurable impact on what people choose to listen to and the subsequent commercial success and cultural staying power of those artists. One such site, Pitchfork, is particularly known in the music community for its ability to catapult an artist to stardom based on the review that an album receives. In this paper, we present P4KxSpotify: a dataset of Pitchfork album reviews with the corresponding Spotify audio features for those albums. We describe our data collection and dataset creation process, including the ethics of such a collection. We present basic information and descriptive statistics about the dataset. Finally, we offer several possible avenues for research that might utilize this new dataset.
Towards Using Word Embedding Vector Space for Better Cohort Analysis
Authors: Bahgat Mohamed , Wilson Steve , Magdy Walid
Published: November 2022

On websites like Reddit, users join communities where they discuss specific topics which cluster them into possible cohorts. The authors within these cohorts have the opportunity to post more openly under the blanket of anonymity, and such openness provides a more accurate signal on the real issues individuals are facing. Some communities contain discussions about mental health struggles such as depression and suicidal ideation. To better understand and analyse these individuals, we propose to exploit properties of word embeddings that group related concepts close to each other in the embeddings space. For the posts from each topically situated sub-community, we build a word embeddings model and use handcrafted lexicons to identify emotions, values and psycholinguistically relevant concepts. We then extract insights into ways users perceive these concepts by measuring distances between them and references made by users either to themselves, others or other things around them. We show how our proposed approach can extract meaningful signals that go beyond the kinds of analyses performed at the individual word level.
Empirical Evaluation of Three Common Assumptions in Building Political Media Bias Datasets
Authors: Ganguly Soumen , Kulshrestha Juhi , An Jisun , Kwak Haewoon
Published: November 2022

In this work, we empirically validate three common assumptions in building political media bias datasets, which are (i) labelers' political leanings do not affect labeling tasks, (ii) news articles follow their source outlet's political leaning, and (iii) political leaning of a news outlet is stable across different topics. We build a ground-truth dataset of manually annotated article-level political leaning and validate the three assumptions. Our findings warn that the three assumptions could be invalid even for a small dataset. We hope that our work calls attention to the (in)validity of common assumptions in building political media bias datasets.
A Framework for Political Portmanteau Decomposition
Authors: Hossain Nabil , Tran Minh , Kautz Henry
Published: November 2022

Portmanteaus are new words formed by combining the sounds and meanings of two words. Given their sticky nature, portmanteaus are often used to create political and personal attacks by combining a target entity with derogatory terms, which can then be spread online for promoting hate speech and defamation. In this paper, we present a framework to decompose political portmanteaus used online into their component words. Using our annotated dataset of political portmanteaus, we train a system that correctly decomposes 76.2% of the political portmanteaus into their component words. Furthermore, for 93.4% of the political portmanteaus, our system finds the correct component words in its top ten results, suggesting that using better ranking methods can lead to stronger results. This work provides a framework for both understanding an intriguing linguistic phenomena and for building hate-speech filters that could catch novel words that would bypass traditional hate speech detection approaches.
External Information Sharing on Health Forums: An Exploration
Authors: Nguyen Dana M. , Olteanu Alexandra , Kıcıman Emre
Published: November 2022

Online health forums are an important avenue for receiving support and learning about fellow patients' experiences with similar diagnoses. We seek to characterize the external information shared (via web links) on health forums as a proxy to participants' information needs. For this purpose, using a dataset of web links shared publicly on a lung cancer forum over a period of 16 years, we perform a comparative analysis with three different website typologies, uncovering a diverse ecosystem of websites. We also examine typological variations as this forum gains and then loses popularity over time.
Characterizing Variation in Toxic Language by Social Context
Authors: Radfar Bahar , Shivaram Karthik , Culotta Aron
Published: November 2022

How two people speak to one another depends heavily on the nature of their relationship. For example, the same phrase said to a friend in jest may be offensive to a stranger. In this paper, we apply this simple observation to study toxic comments in online social networks. We curate a collection of 6.7K tweets containing potentially toxic terms from users with different relationship types, as determined by the nature of their follower-friend connection. We find that such tweets between users with no connection are nearly three times as likely to be toxic as those between users who are mutual friends, and that taking into account this relationship type improves toxicity detection methods by about 5% on average. Furthermore, we provide a descriptive analysis of how toxic language varies by relationship type, finding for example that mildly offensive terms are used to express hostility more commonly between users with no social connection than users who are mutual friends.
Under the Spotlight: Web Tracking in Indian Partisan News Websites
Authors: Agarwal Vibhor , Vekaria Yash , Agarwal Pushkal , Mahapatra Sangeeta , Set Shounak , Muthiah Sakthi Balan , Sastry Nishanth , Kourtellis Nicolas
Published: November 2022

India is experiencing intense political partisanship and sectarian divisions. The paper performs, to the best of our knowledge, the first comprehensive analysis on the Indian online news media with respect to tracking and partisanship. We build a dataset of 103 online, mostly mainstream news websites. With the help of two experts, alongside data from the Media Ownership Monitor, we label these websites according to their partisanship (Left, Right, or Centre). We study and compare user tracking on these sites with different metrics: numbers of cookies, cookie synchronization, device fingerprinting, and invisible pixel-based tracking. We find that Left and Centre websites serve more cookies than Right-leaning websites. However, through cookie synchronization, more user IDs are synchronized in Left websites than Right or Centre. Canvas fingerprinting is used similarly by Left and Right, and less by Centre. Invisible pixel-based tracking is 50% more intense in Centre-leaning websites than Right, and 25% more than Left. Desktop versions of news websites deliver more cookies than their mobile counterparts. A handful of third-parties are tracking users in most websites in this study. This paper demonstrates the intensity of Web tracking happening in Indian news websites and discusses implications for research on overall privacy of users visiting partisan news websites in India.
On the Role of Micro-categories to Characterize Event Popularity in Meetup
Authors: Bhowmick Ayan Kumar , Pramanik Soumajit , Pathak Sayan , Mitra Bivas
Published: November 2022

Event-based social networking platforms such as Meetup have recently witnessed a huge growth. However, with the rise in the volume of groups and events, making individual events attractive has become increasingly challenging for its organizers. As a result, we find that events hosted by groups of same category at similar venues and similar times, also widely differ in their popularity. Data study reveals that the topics specified in textual descriptions of events may be key to their popularity. In this paper, we introduce a novel concept of topical micro-categories in the context of EBSNs for accurately characterizing events, such that events belonging to the same micro-category exhibit similar popularity profile. We develop a principled method to detect such micro-categories from the textual descriptions of individual events. Our experiments reveal the significance of the detected micro-categories in determining the popularity of associated Meetup events and groups. We also investigate the effectiveness of the micro-categories in a real-world application scenario by developing a recommendation model; this model recommends relevant micro-categories to a group for hosting its future events with enhanced popularity. Notably, our model achieves an average NDCG score of around 0.75 showing a straight 5% improvement over the best performing competing method.
Political Polarization in Online News Consumption
Authors: Garimella Kiran , Smith Tim , Weiss Rebecca , West Robert
Published: November 2022

Political polarization appears to be on the rise, as measured by voting behavior, general affect towards opposing partisans and their parties, and contents posted and consumed online.
Research over the years has focused on the role of the Web as a driver of polarization.
In order to further our understanding of the factors behind online polarization, in the present work we collect and analyze Web browsing histories of tens of thousands of users alongside careful measurements of the time spent browsing various news sources.
We show that online news consumption follows a polarized pattern, where users' visits to news sources aligned with their own political leaning are substantially longer than their visits to other news sources.
Next, we show that such preferences hold at the individual as well as the population level, as evidenced by the emergence of clear partisan communities of news domains from aggregated browsing patterns.
Finally, we tackle the important question of the role of user choices in polarization. Are users simply following the links proffered by their Web environment, or do they exacerbate partisan polarization by intentionally pursuing like-minded news sources?
To answer this question, we compare browsing patterns with the underlying hyperlink structure spanned by the considered news domains, finding strong evidence of polarization in partisan browsing habits beyond that which can be explained by the hyperlink structure of the Web.
Sudden Attention Shifts on Wikipedia During the COVID-19 Crisis
Authors: Horta Ribeiro Manoel , Gligorić Kristina , Peyrard Maxime , Lemmerich Florian , Strohmaier Markus , West Robert
Published: November 2022

We study how the COVID-19 pandemic, alongside the severe mobility restrictions that ensued, has impacted information access on Wikipedia, the world's largest online encyclopedia. A longitudinal analysis that combines pageview statistics for 12 Wikipedia language editions with mobility reports published by Apple and Google reveals massive shifts in the volume and the nature of information seeking patterns amidst the pandemic. Interestingly, while we observe a transient increase in Wikipedia's pageviews volume following mobility restrictions, the nature of what people seek for was impacted in the long run. These changes are most pronounced for language editions associated with countries in which the most severe mobility restrictions were implemented. We also find that articles belonging to different topics behaved differently, e.g., attention towards entertainment-related topics is lingering and even increasing, while the interest in health- and biology-related topics was either unsubstantial or transient. Our results highlight the utility of Wikipedia to study how the pandemic changed information seeking behavior regarding shifting needs, interests, and concerns.
Partisan Responses to Fact-Checking in Online News Platforms: Evidence from a Political Rumor about the North Korean Leader
Authors: Kang TaeYoung , Sim Jaeung
Published: November 2022

To correct misinformation and mitigate the social costs of political rumors and fake news, news providers, politicians, and researchers have exerted significant efforts on fact-checking and rumor debunking. This study examined how individuals will respond when a political rumor is debunked by large-scale fact-checking. To explore this question, we leveraged a quasi-experimental setting where the North Korean leader’s reappearance in the public event suddenly rebutted a political rumor about his death. Collecting 2.6 million comments from the largest online news portal in South Korea, we employed a difference-in-differences approach comparing differences in commenting behaviors between liberals and conservatives before and after this event. The results show that a political side empowered by the fact-checking coverage became more vocal and hostile. However, their explicit support level for the rumor did not change significantly compared to their partisan counterparts. Besides, we found that news outlets rebutted by fact-checking attracted more user comments than supported news outlets. Swearing comments of the supported political side mostly drove this difference, suggesting that partisans tend to utilize favorable fact-checking to empower their political side through blaming the other side. Our research stresses the importance of capturing the silence of partisans in considering the effectiveness of fact-checking and provides an alternative explanation on why fact-checking evokes hostile communication in online media.
Evaluating Audience Loyalty and Authenticity in Influencer Marketing via Multi-task Multi-relational Learning
Authors: Kim Seungbae , Chen Xiusi , Jiang Jyun-Yu , Han Jinyoung , Wang Wei
Published: November 2022

Since influencer marketing has become an essential marketing method, influencer fraud behavior such as buying fake followers and engagements to manipulate the popularity is under the spotlight. To address this issue, we propose a multi-task audience evaluation model that can assess both the loyalty and authenticity of influencers’ audiences. More specifically, the proposed model takes engagement information of an influencer’s audience, including likes and comments on social media posts, and predicts (i) the retention rate of the audience of the influencer and (ii) how the influencer is associated with fake audiences (or engagement bots). To learn the social interaction between influencers and their audiences, we build multi-relational networks based on the diverse engagement behavior such as commenting. Our model further utilizes the contextualized information captured in user comments to learn distinct engagement behavior of genuine and fake users. Based on the predicted loyalty and authenticity scores, we rank influencers to find those who are followed by loyal and authentic audiences. By using a large-scale Instagram influencer-audience dataset which contains 14,221 influencers, 9,290,895 audiences, and 65,848,717 engagements, we evaluate ranking performance, and show that the proposed framework outperforms other baseline methods.
You Don't Know How I Feel: Insider-Outsider Perspective Gaps in Cyberbullying Risk Detection
Authors: Kim Seunghyun , Razi Afsaneh , Stringhini Gianluca , Wisniewski Pamela J. , De Choudhury Munmun
Published: November 2022

Cyberbullying is a prevalent concern within social computing research that has led to the development of several supervised machine learning (ML) algorithms for automated risk detection. A critical aspect of ML algorithm development is how to establish ground truth that is representative of the phenomenon of interest in the real world. Often, ground truth is determined by third-party annotators (i.e., “outsiders”) who are removed from the situational context of the interaction; therefore, they cannot fully understand the perspective of the individuals involved (i.e., “insiders”). To understand the extent of this problem, we compare “outsider” versus “insider” perspectives when annotating 2,000 posts from an online peer-support platform. We interpolate this analysis to a corpus containing over 2.3 million posts on bullying and related topics, and reveal significant gaps in ML models that use third-party annotators to detect bullying incidents. Our results indicate that models based on the insiders’ perspectives yield a significantly higher recall in identifying bullying posts and are able to capture a range of explicit and implicit references and linguistic framings, including person-specific impressions of the incidents. Our study highlights the importance of incorporating the victim’s point of view in establishing effective tools for cyberbullying risk detection. As such, we advocate for the adoption of human-centered and value-sensitive approaches for algorithm development that bridge insider-outsider perspective gaps in a way that empowers the most vulnerable.
Uncovering Coordinated Networks on Social Media: Methods and Case Studies
Authors: Pacheco Diogo , Hui Pik-Mai , Torres-Lugo Christopher , Truong Bao Tran , Flammini Alessandro , Menczer Filippo
Published: November 2022

Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.
Assessing Media Bias in Cross-Linguistic and Cross-National Populations
Authors: Sales Allan , Zehe Albin , Marinho Leandro Balby , Veloso Adriano , Hotho Andreas , Omeliyanenko Janna
Published: November 2022

Media bias is a worldwide concern. Although automated methods exist for the analysis of various forms of media bias, language is still an important barrier toward spotting worldwide differences in reporting. In this paper, we propose a methodology based on word embeddings, lexicon translation, and document similarity to assess media bias in news articles published in different idioms. We model media bias under the perspective of subjective language use, i.e., the more subjective the content of a news article is, the more biased it is. Our core assumption is that news articles reporting the same events, but written in different languages, should have similar levels of subjectivity; otherwise, we may have spotted biased text. Our method consists of using translated versions of subjectivity lexicons that were originally constructed for measuring subjectivity in the Brazilian Portuguese language. We evaluate our approach on two labeled data sets to show that our method is valid and apply our methodology to analyze recent and largely resounded topics, such as the Venezuela crisis and Syrian war, on four distinct idioms: Portuguese, German, English, and Spanish.
Group Link Prediction Using Conditional Variational Autoencoder
Authors: Sha Hao , Al Hasan Mohammad , Mohler George
Published: November 2022

Though link prediction is a well-studied problem with a large body of solutions, existing methods do not handle the case where the predicted link is between an individual and a group. This limitation prevents link prediction models from being directly applicable to many real-life prediction problems where more than two individuals are involved. Examples of such problems include: finding missing members of a group, soliciting new members to join a group, or recommending a group for a person to join. The central aspect of all of the above problems is predicting linkage between a group and an individual, a task we refer to as group link prediction. In this work we propose an innovative method for solving group link prediction consisting of two complementary models: group-to-individual-link (member-recommendation) and individual-to-group-link (group-recommendation). Both of the proposed models use a conditional variational auto-encoder (CVAE) for solving the respective task. For a given individual (or group), the model learns a conditional probability distribution of a link for each candidate group (or individual) link. We compare our proposed model and a collection of competing models on various real-world datasets and show the superior performance of the proposed model in group link prediction.
Understanding the Use of Fauxtography on Social Media
Authors: Wang Yuping , Tahmasbi Fatemeh , Blackburn Jeremy , Bradlyn Barry , De Cristofaro Emiliano , Magerman David , Zannettou Savvas , Stringhini Gianluca
Published: November 2022

Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and identify over 61k instances of fauxtography discussed on Twitter, 4chan, and Reddit. Then, we study how posting fauxtography affects engagement of posts on social media, finding that posts containing it receive more interactions in the form of re-shares, likes, and comments. Finally, we show that fauxtography images are often turned into memes by Web communities. Our findings show that effective mitigation against disinformation need to take images into account, and highlight a number of challenges in dealing with image-based disinformation.
Political Bias and Factualness in News Sharing across more than 100,000 Online Communities
Authors: Weld Galen , Glenski Maria , Althoff Tim
Published: November 2022

As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.
"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter
Authors: Zannettou Savvas
Published: November 2022

Over the past few years, there is a heated debate and serious public concerns regarding online content moderation, censorship, and the principle of free speech on the Web. To ease these concerns, social media platforms like Twitter and Facebook refined their content moderation systems to support soft moderation interventions. Soft moderation interventions refer to warning labels attached to potentially questionable or harmful content to inform other users about the content and its nature while the content remains accessible, hence alleviating concerns related to censorship and free speech. In this work, we perform one of the first empirical studies on soft moderation interventions on Twitter. Using a mixed-methods approach, we study the users who share tweets with warning labels on Twitter and their political leaning, the engagement that these tweets receive, and how users interact with tweets that have warning labels. Among other things, we find that 72% of the tweets with warning labels are shared by Republicans, while only 11% are shared by Democrats.
By analyzing content engagement, we find that tweets with warning labels had more engagement compared to tweets without warning labels. Also, we qualitatively analyze how users interact with content that has warning labels finding that the most popular interactions are related to further debunking false claims, mocking the author or content of the disputed tweet, and further reinforcing or resharing false claims. Finally, we describe concrete examples of inconsistencies, such as warning labels that are incorrectly added or warning labels that are not added on tweets despite sharing questionable and potentially harmful information.
Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic
Authors: Zhang Jason Shuo , Keegan Brian , Lv Qin , Tan Chenhao
Published: November 2022

As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two “new” communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities.

In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, both in language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly “loyal”. To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence.
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
Authors: Alam Firoj , Dalvi Fahim , Shaar Shaden , Durrani Nadir , Mubarak Hamdy , Nikolov Alex , Da San Martino Giovanni , Abdelali Ahmed , Sajjad Hassan , Darwish Kareem , Nakov Preslav
Published: November 2022

With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories. Unfortunately, alongside all this useful information, there was also a new blending of medical and political misinformation and disinformation, which gave rise to the first global infodemic. While fighting this infodemic is typically thought of in terms of factuality, the problem is much broader as malicious content includes not only fake news, rumors, and conspiracy theories, but also promotion of fake cures, panic, racism, xenophobia, and mistrust in the authorities, among others. This is a complex problem that needs a holistic approach combining the perspectives of journalists, fact-checkers, policymakers, government entities, social media platforms, and society as a whole. With this in mind, we define an annotation schema and detailed annotation instructions that reflect these perspectives. We further deploy a multilingual annotation platform, and we issue a call to arms to the research community and beyond to join the fight by supporting our crowdsourcing annotation efforts. We perform initial annotations using the annotation schema, and our initial experiments demonstrated sizable improvements over the baselines.
COVID-19 Coverage By Cable and Broadcast Networks
Authors: Budak Ceren , Muddiman Ashley , Kim Yujin , Murray Caroline C. , Stroud Natalie J.
Published: November 2022

In this paper, we present a dataset of COVID-19 coverage by cable and broadcast news networks. Our dataset, which spans the time period between January 21, 2020 and June 12, 2020, includes 44,643 transcript paragraphs that are manually labeled according to their relevance to COVID-19 and 486,068 paragraphs that are further labeled using supervised classifiers. We further provide descriptive analysis that shows differences in the degree to which networks covered the pandemic and how the content of this coverage varied. Our distinctive phrase analysis also suggests that cable news networks, particularly Fox News and MSNBC, are politicizing COVID-19. This dataset can be leveraged to model and characterize the role cable and broadcast news networks play in shaping COVID-19 attitudes and behaviors, as well as how the coverage was related to external events (e.g. the number of COVID-19 cases), coverage in other media (e.g. newspapers), and COVID-19 conversations on social media (e.g. Twitter). The COVID-19 cable and broadcast news dataset is publicly available to the research community, and can be accessed at https://doi.org/10.7910/DVN/LWMYAD.
Tracking Knowledge Propagation Across Wikipedia Languages
Authors: Valentim Rodolfo Vieira , Comarela Giovanni , Park Souneil , Sáez-Trumper Diego
Published: November 2022

In this paper, we present a dataset of inter-language knowledge propagation in Wikipedia. Covering the entire 309 language editions and 33M articles, the dataset aims to track the full propagation history of Wikipedia concepts, and allow follow-up research on building predictive models of them. For this purpose, we align all the Wikipedia articles in a language-agnostic manner according to the concept they cover, which results in 13M propagation instances. To the best of our knowledge, this dataset is the first to explore the full inter-language propagation at a large scale. Together with the dataset, a holistic overview of the propagation and key insights about the underlying structural factors are provided to aid future research. For example, we find that although long cascades are unusual, the propagation tends to continue further once it reaches more than four language editions. We also find that the size of language editions is associated with the speed of propagation. We believe the dataset not only contributes to the prior literature on Wikipedia growth but also enables new use cases such as edit recommendation for addressing knowledge gaps, detection of disinformation, and cultural relationship analysis.
American Politicians Diverge Systematically, Indian Politicians do so Chaotically: Text Embeddings as a Window into Party Polarization
Authors: Budhiraja Amar , Sharma Ankur , Agrawal Rahul , Choudhury Monojit , Pal Joyojeet
Published: November 2022

Conversations on polarization are increasingly central to discussions of politics and society, but the schisms between parties and states can be hard to identify systematically in what politicians say. In this paper, we demonstrate the use of representation learning as a window into political dialogue on social media through the tweets authored by politicians on Twitter. We propose to embed politicians in a space such that their output embedding vectors represent the content similarity between the two politicians based on their tweets. We further propose a short-text based embedding technique to overcome some of the shortcomings of the previous methods. The learnt embeddings for politicians of India and the United States show two trends. First, that in the US case, we find a clear distinction between Democrats and Republicans, which is also reflected in the coalescing of the states that lean towards each party placing likewise in a graphical space. However, in the Indian case, the federal structure, multiparty system, and linguistic differences clearly manifest in the coalescing political discourse in the largely monolingual north and the scattered regional states. Our work shows ways in which machine learning methods can offer a window into thinking about how polarized political discourses manifest in what politicians say online.
Risk-aware Regularization for Opinion-based Portfolio Selection
Authors: Hsu Ting-Wei , Chen Chung-Chi , Huang Hen-Hsen , Chen Hsin-Hsi
Published: November 2022

Every investor faces the risk-return tradeoff when making investment decisions. Most of the investors construct a portfolio instead of putting all of their wealth on a certain stock. However, most of the previous works in the NLP community focus on predicting the movement of stocks' prices or volatilities, but do not consider the portfolio selection issue. On the other hand, few works consider unstructured data in the financial community when dealing with this issue. This paper introduces a novel opinion-based portfolio selection task, and proposes new objective functions presenting different risk appetites of investors. The empirical studies of the selecting portfolio are also discussed with both Sharpe ratio and volatility metrics.
Mining Points-of-Interest Data to Predict Urban Inequality: Evidence from Germany and France
Authors: Ganter Manuel , Toetzke Malte , Feuerriegel Stefan
Published: November 2022

Reducing inequality is a major goal of the Sustainable Development Goals. Inequality is many-sided and often appears across geographic boundaries. Urban inequality refers to inequality between urban neighborhoods. Despite close distances, it reveals considerable disparities in income level, unemployment rates, and other socio-economic indicators and is highly dangerous for democratic societies. However, little is known about determinants indicating urban inequality. Here, we propose to explain urban inequality based on point-of-interest (POI) data from the online platform Open Street Maps. For this, we leverage machine learning to predict three major indicators of urban inequality, namely, unemployment rate, income level, and foreign national rate. We evaluate our machine learning approach using POI data for neighborhoods in Paris, Lyon, Marseille, Berlin, Hamburg, and Bremen. We find: (1) POIs are highly predictive of intra-city inequality explaining up to 75% of out-of-sample variance of urban inequality. (2) POIs generalize across cities and, thereby, can help to explain urban inequality in other cities, where no socio-economic data is available. (3) Important POIs for the prediction model are, e.g., banks and playgrounds. To the best of our knowledge, our work is the first to show urban inequality through POIs. As such, POIs can be used to infer granular mappings of urban inequality and thereby provide cost-effective evidence for policy-makers.
No Calm in the Storm: Investigating QAnon Website Relationships
Authors: Hanley Hans W. A. , Kumar Deepak , Durumeric Zakir
Published: November 2022

QAnon is a far-right conspiracy theory whose followers largely organize online. In this work, we use web crawls seeded from two of the largest QAnon hotbeds on the Internet, Voat and 8kun, to build a QAnon-centered domain-based hyperlink graph. We use this graph to identify, understand, and learn about the set of websites that spread QAnon content online. Specifically, we curate the largest list of QAnon centered websites to date, from which we document the types of QAnon sites, their hosting providers, as well as their popularity. We further analyze QAnon websites' connection to mainstream news and misinformation online, highlighting the outsized role misinformation websites play in spreading the conspiracy. Finally, we leverage the observed relationship between QAnon and misinformation sites to build a highly accurate random forest classifier that distinguishes between misinformation and authentic news sites. Our results demonstrate new and effective ways to study the growing presence of conspiracy theories and misinformation on the Internet.
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators
Authors: Jones Keenan , Nurse Jason R.C. , Li Shujun
Published: November 2022

Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, there have been many studies focused on online authorship attribution (AA). That is, the use of trained models to identify the authors of online texts. Given the power of natural language models in generating convincing texts, this paper examines the degree to which these language models can generate texts capable of deceiving online AA models. Experimenting with both blog and Twitter data, we utilise GPT-2 language models to generate texts using the existing posts of online users. We then examine whether GPT-2-based text generators are capable of mimicking authorial style to such a degree that they can deceive typical AA models. From this, we find that current AI-based text generators are able to successfully mimic authorship, showing capabilities towards this on both datasets. Our findings, in turn, highlight the current capacity of powerful natural language models to generate original online posts capable of mimicking authorial style sufficiently to deceive popular AA methods. This is a key finding given the proposed role of AA in real-world applications such as spam-detection and the investigation of criminal activity online -- where deceptive texts could be automatically generated to mimic authorship in order to mislead these critical AA systems.
Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience
Authors: Joseph Kenneth , Horne Benjamin D. , Green Jon , Wihbey John P.
Published: November 2022

We present analyses from a real-time information monitoring system of online local news in the U.S. We study relationships among online local news coverage of COVID, cases and deaths in an area, and properties of local news outlets and their audiences. Our analysis relies on a unique dataset of the online content of over 300 local news outlets, encompassing over 750,000 articles over a period of 10 months spanning April 2020 to February 2021. We find that the rate of COVID coverage over time by local news outlets was primarily associated with death rates at the national level, but that this effect dissipated over the course of the pandemic as news about COVID was steadily displaced by sociopolitical events, like the 2020 U.S. elections. We also find that both the volume and content of COVID coverage differed depending on local politics, and outlet audience size, as well as evidence that more vulnerable populations received less pandemic-related news.
From an Authentication Question to a Public Social Event: Characterizing Birthday Sharing on Twitter
Authors: Keküllüoğlu Dilara , Magdy Walid , Vaniea Kami
Published: November 2022

Date of birth (DOB) has historically been considered as private information and safe to use for authentication, but recent years have seen a shift towards wide public sharing. In this work we characterize how modern social media users are approaching the sharing of birthday wishes publicly online. Over 45 days, we collected over 2.8M tweets wishing happy birthday to 724K Twitter accounts. For 50K accounts, their age was likely mentioned revealing their DOB, and 10% were protected accounts. Our findings show that the majority of both public and protected accounts seem to be accepting of their birthdays and DOB being revealed online by their friends even when they do not have it listed on their profiles. We further complemented our findings through a survey to measure awareness of DOB disclosure issues and how people think about sharing different types of birthday-related information. Our analysis shows that giving birthday wishes to others online is considered a celebration and many users are quite comfortable with it. This view matches the trend also seen in security where the use of DOB in authentication process is no longer considered best practice.
Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions
Authors: Kong Quyu , Booth Emily , Bailo Francesco , Johns Amelia , Rizoiu Marian-Andrei
Published: November 2022

Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and can be overly focused and narrowed to certain online groups. This work proposes a complete solution to accelerate the qualitative analysis of problematic online speech, focusing on opinions emerging from online communities by leveraging machine learning algorithms. First, we employ qualitative methods of deep observation for understanding problematic online speech. This initial qualitative study constructs an ontology of problematic speech, which contains social media postings annotated with their underlying opinions. The qualitative study dynamically constructs the set of opinions, simultaneous with labeling the postings. Next, we use keywords to collect a large dataset from three online social media platforms (Facebook, Twitter, and Youtube). Finally, we introduce an iterative data exploration procedure to augment the dataset. It alternates between a data sampler --- which balances exploration and exploitation of unlabeled data --- the automatic labeling of the sampled data, the manual inspection by the qualitative mapping team, and, finally, the retraining of the automatic opinion classifiers. We present both qualitative and quantitative results. First, we show that our human-in-the-loop method successfully augments the initial qualitatively labeled and narrowly focused dataset and constructs a more encompassing dataset. Next, we present detailed case studies of the dynamics of problematic speech in a far-right Facebook group, exemplifying its mutation from conservative to extreme. Finally, we examine the dynamics of opinion emergence and co-occurrence, and we hint at some pathways through which extreme opinions creep into the mainstream online discourse.
All That’s Happening behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Reddit
Authors: Li Hanlin , Hecht Brent , Chancellor Stevie
Published: November 2022

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms.