bluuebunny/crossref_metadata_2025 huggingface dataset:
CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines
Authors: DeVerna Matthew R. , Pierri Francesco , Truong Bao Tran , Bollenbacher John , Axelrod David , Loynes Niklas , Torres-Lugo Christopher , Yang Kai-Cheng , Menczer Filippo , Bryden John
Published: November 2022

With a substantial proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about vaccines spreading on social media. In this paper, we present the CoVaxxy dataset, a growing collection of English-language Twitter posts about COVID-19 vaccines. Using one week of data, we provide statistics regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also illustrate how these data might be utilized by performing an analysis of the prevalence over time of high- and low-credibility sources, topic groups of hashtags, and geographical distributions. Additionally, we develop and present the CoVaxxy dashboard, allowing people to visualize the relationship between COVID-19 vaccine adoption and U.S. geo-located posts in our dataset. This dataset can be used to study the impact of online information on COVID-19 health outcomes (e.g., vaccine uptake) and our dashboard can help with exploration of the data.
Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole
Authors: Haworth Ethan , Grover Ted , Langston Justin , Patel Ankush , West Joseph , Williams Alex C.
Published: November 2022

People regularly share retellings of their personal events through social media websites to elicit feedback about the reasonability of their actions in the event's context. In this paper, we explore how learning approaches can be used toward the goal of classifying reasonability in personal retellings of events shared on social media. We collect 13,748 community-labeled posts from /r/AmITheAsshole, a subreddit in which Reddit users share retellings of personal events which are voted upon by community members. We build and evaluate a total of 21 machine learning models across seven types of models and three distinct feature sets. We find that our best-performing model can predict the reasonability of a post with an F1 score of .76. Our findings suggest that features derived from the post and author metadata were more predictive than simple linguistic features like the post sentiment and types of words used. We conclude with a discussion on the implications of our findings as they relate to sharing retellings of personal events on social media and beyond.
Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls
Authors: Balasubramanian Siva K. , Bilgic Mustafa , Culotta Aron , Hemphill Libby , Nikolich Anita , Shapiro Matthew A.
Published: November 2022

The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord. In this paper we investigate the IRA's communication strategies by analyzing trending terms on Twitter to identify cases in which the IRA leads or follows other users. Our analysis focuses on over 38M tweets posted between 2016 and 2017 from IRA users (n=3,613), journalists (n=976), members of Congress (n=526), and politically engaged users from the general public (n=71,128). We find that the IRA tends to lead on topics related to the 2016 election, race, and entertainment, suggesting that these are areas both of strategic importance as well as those topics of highest potential impact. Furthermore, we identify topics where the IRA has been relatively ineffective, such as tweets on military, political scandals, and violent attacks, where, despite many tweets on the topic, the IRA rarely leads the conversation, and thus has little opportunity to influence it. We offer our proposed methodology as a way to track the strategic choices of future influence operations in real-time.
Visual Gender Biases in Wikipedia: A Systematic Evaluation across the Ten Most Spoken Languages
Authors: Beytía Pablo , Agarwal Pushkal , Redi Miriam , Singh Vivek K.
Published: November 2022

Wikipedia, a collaborative crowd-sourced platform, is the largest, most visited online encyclopedia. Since it spreads information freely in more than 300 languages, many users, tools, and dashboards rely on its content. Hence, there is a need to maintain its fairness and completeness. However, previous research has indicated the existence of a significant gender gap in Wikipedia biographical articles. We already know that a minimal proportion of those articles portray women and there are gender asymmetries in the textual content of these articles, but little has been reported about the visual aspects (e.g., image volume or image quality) of the gender gap. Here, we analyze all biographies available on Wikipedia across 300 occupations in the ten most widely spoken languages, and undertake quantitative and qualitative analysis of gender differences in the written and visual content. The cross-lingual results indicate that (1) much of the male bias in content arises when editors select which personalities should have a Wikipedia page, (2) the trends in written and visual content are quite dissimilar, (3) men biographies tend to have more images across languages, and (4) female biographies average better visual quality. A more granular analysis is performed on English Wikipedia, distinguishing trends in occupations and qualitatively analyzing science and technology biographies. The overall results shed light on the kinds of visual biases that emerge in the collaborative creation of Wikipedia and yield guidelines for future management of contributions on the platform.
The Effect of People Recommenders on Echo Chambers and Polarization
Authors: Cinus Federico , Minici Marco , Monti Corrado , Bonchi Francesco
Published: November 2022

The effects of online social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the information and the opinions users are exposed to.

In this paper, we propose a novel framework to assess the effect of people recommenders on the evolution of opinions. Our proposal is based on Monte Carlo simulations combining link recommendation and opinion-dynamics models. In order to control initial conditions, we define a random network model to generate graphs with opinions, with tunable amounts of modularity and homophily. Finally, we join these elements into a methodology able to study the causal relationship between the recommender system and the echo chamber effect. Our method can also assess if such relationships are statistically significant. We also show how such a framework can be used to measure, by means of simulations, the impact of different intervention strategies.

Our thorough experimentation shows that people recommenders can in fact lead to a significant increase in echo chambers. However, this happens only if there is considerable initial homophily in the network. Also, we find that if the network already contains echo chambers, the effect of the recommendation algorithm is negligible. Such findings are robust to two very different opinion dynamics models, a bounded confidence model and an epistemological model.
Weakening the Inner Strength: Spotting Core Collusive Users in YouTube Blackmarket Network
Authors: Dutta Hridoy Sankar , Diwan Nirav , Chakraborty Tanmoy
Published: November 2022

Social reputation (e.g., likes, comments, shares, etc.) on YouTube is the primary tenet to popularize channels/videos. However, the organic way to improve social reputation is tedious, which often provokes content creators to seek services of online blackmarkets for rapidly inflating content reputation. Such blackmarkets act underneath a thriving collusive ecosystem comprising core users and compromised accounts (together known as collusive users). Core users form the backbone of blackmarkets; thus, spotting and suspending them may help in destabilizing the entire collusive network. Although a few studies focused on collusive user detection on Twitter, Facebook, and YouTube, none of them differentiate between core users and compromised accounts.
We are the first to present a rigorous analysis of core users in YouTube blackmarkets. To this end, we collect a new dataset of collusive YouTube users. We study the core-periphery structure of the underlying collusive commenting network (CCN). We examine the topology of CCN to explore the behavioral dynamics of core and compromised users. We then introduce KORSE, a novel graph-based method to automatically detect core users based only on the topological structure of CCN. KORSE performs a weighted k-core decomposition using our proposed metric, called Weighted Internal Core Collusive Index (WICCI). However, KORSE is infeasible to adopt in practice as it requires complete interactions among collusive users to construct CCN. We, therefore, propose NURSE, a deep fusion framework that only leverages user timelines (without considering the underlying CCN) to detect core blackmarket users. Experimental results show that NURSE is quite close to KORSE in detecting core users and outperforms nine baselines.
Correcting Sociodemographic Selection Biases for Population Prediction from Social Media
Authors: Giorgi Salvatore , Lynn Veronica E. , Gupta Keshav , Ahmed Farhan , Matz Sandra , Ungar Lyle H. , Schwartz H. Andrew
Published: November 2022

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population --- a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. In this two-part study, we first evaluate standard, "out-of-the-box" restratification techniques, finding they provide no improvement and often even degraded prediction accuracies across four tasks of esimating U.S. county population health statistics from Twitter. The core reasons for degraded performance seem to be tied to their reliance on either sparse or shrunken estimates of each population's socio-demographics. In the second part of our study, we develop and evaluate Robust Poststratification, which consists of three methods to address these problems: (1) estimator redistribution to account for shrinking, as well as (2) adaptive binning and (3) informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods leads to significant improvement in prediction accuracies over the standard restratification approaches. Taken together, Robust Poststratification enables state-of-the-art prediction accuracies, yielding a 53.0% increase in variance explained (R^2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.
Exploring the Magnitude and Effects of Media Influence on Reddit Moderation
Authors: Habib Hussam , Nithyanand Rishab
Published: November 2022

Most platforms, including Reddit, face a dilemma when applying interventions such as subreddit bans to toxic communities — do they risk angering their user base by proactively enforcing stricter controls on discourse or do they defer interventions at the risk of eventually triggering negative media reactions which might impact their advertising revenue? In this paper, we analyze Reddit’s previous administrative interventions to understand one aspect of this dilemma: the relationship between the media and administrative interventions. More specifically, we make two primary contributions. First, using a mediation analysis framework, we find evidence that Reddit’s interventions for violating their content policy for toxic content occur because of media pressure. Second, using interrupted time series analysis, we show that media attention on communities with toxic content only increases the problematic behavior associated with that community (both within the community itself and across the platform). However, we find no significant difference in the impact of administrative interventions on subreddits with and without media pressure. Taken all together, this study provides evidence of a media-driven moderation strategy at Reddit and also suggests that such a strategy may not have a significantly different impact than a more proactive strategy.
Rules and Rule-Making in the Five Largest Wikipedias
Authors: Hwang Sohyeon , Shaw Aaron
Published: November 2022

The governance of many online communities relies on rules created by participants. However, prior work provides limited evidence about how these self-governance efforts compare and relate to one another across communities. Studies tend either to analyze communities as discrete entities or consider communities that coexist within a hierarchically-managed platform. In this paper, we investigate both comparative and relational dimensions of self-governance in similar communities. We use exhaustive trace data from the five largest language editions of Wikipedia over almost 20 years since their founding, and consider both patterns in rule-making and overlaps in rule sets. We find similar rule-making activity across the five communities that replicates and extends prior work on English language Wikipedia alone. However, we also find that these Wikipedias have increasingly unique rule sets, even as editing activity concentrates on rules shared between them. Self-governing communities aligned in key ways may share a common core of rules and rule-making practices as they develop and sustain institutional variations.
Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19
Authors: Jiang Yueyi , Jiang Yunfan , Leqi Liu , Winkielman Piotr
Published: November 2022

Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for early screening and targeted interventions to reduce loneliness, particularly among vulnerable groups such as young adults. To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group. We provided annotations by trained human annotators for binary and fine-grained loneliness classifications of the posts. Trained on FIG-Loneliness, two BERT-based models were used to understand loneliness forms and authors’ coping strategies in these forums. Our binary loneliness classification achieved an accuracy above 97%, and fine-grained loneliness category classification reached an average accuracy of 77% across all labeled categories. With FIG-Loneliness and model predictions, we found that loneliness expressions in the young adult related forums were distinct from other forums. Those in young adult-focused forums were more likely to express concerns pertaining to peer relationship, and were potentially more sensitive to geographical isolation impacted by the COVID-19 pandemic lockdown. Also, we showed that different forms of loneliness have differential use in coping strategies.
Supporting Human Memory by Reconstructing Personal Episodic Narratives from Digital Traces
Authors: Kalokyri Varvara , Borgida Alexander , Marian Amelie
Published: November 2022

Numerous applications capture in digital form aspects of people’s lives. The resulting data, which we call Personal Digital Traces - PDTs, can be used to help reconstruct people’s episodic memories and connect to their past personal events. This may have several applications, from helping the recall of patients with neurodegenerative diseases to gathering clues from multiple sources to identify recent contacts and places visited – a critical new application for the recent health crisis. This paper takes steps towards integrating, connecting and summarizing the heterogeneous collection of data into episodic narratives using scripts – prototypical plans for everyday activities. Specifically, we propose a matching algorithm that groups PDTs from many different sources into script instances (episodes), and we provide a technique for ranking the likelihood of candidate episodes.
We report on the results of a study based on the personal data of real users, which gives evidence that our episode reconstruction 1) integrates well PDTs from different sources into coherent episodes, and 2) augments users’ memory of their past actions.
Reconsidering Tweets: Intervening during Tweet Creation Decreases Offensive Content
Authors: Katsaros Matthew , Yang Kathy , Fratamico Lauren
Published: November 2022

The proliferation of harmful and offensive content is a problem that many online platforms face today. One of the most common approaches for moderating offensive content online is via the identification and removal after it has been posted, increasingly assisted by machine learning algorithms. More recently, platforms have begun employing moderation approaches which seek to intervene prior to offensive content being posted. In this paper, we conduct an online randomized controlled experiment on Twitter to evaluate a new intervention that aims to encourage participants to reconsider their offensive content and, ultimately, seeks to reduce the amount of offensive content on the platform. The intervention prompts users who are about to post harmful content with an opportunity to pause and reconsider their Tweet. We find that users in our treatment prompted with this intervention posted 6% fewer offensive Tweets than non-prompted users in our control. This decrease in the creation of offensive content can be attributed not just to the deletion and revision of prompted Tweets - we also observed a decrease in both the number of offensive Tweets that prompted users create in the future and the number of offensive replies to prompted Tweets. We conclude that interventions allowing users to reconsider their comments can be an effective mechanism for reducing offensive content online.
Unraveling Social Perceptions & Behaviors towards Migrants on Twitter
Authors: Khatua Aparup , Nejdl Wolfgang
Published: November 2022

We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards migrants. Our theoretical anchoring helped us in identifying two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. We have employed unsupervised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study offers a nuanced understanding of migrant-related Twitter deliberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outperformed other models. Additionally, we argue that tweets conveying antipathy or animosity can be broadly considered hate speech towards migrants, but they are not the same. Thus, our approach has fine-tuned the binary hate speech detection task by highlighting the granular differences between perceptual and behavioral aspects of hate speeches.
Measuring the Monetary Value of Online Volunteer Work
Authors: Li Hanlin , Hecht Brent , Chancellor Stevie
Published: November 2022

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.
FairLP: Towards Fair Link Prediction on Social Network Graphs
Authors: Li Yanying , Wang Xiuling , Ning Yue , Wang Hui
Published: November 2022

Link prediction has been widely applied in social network analysis. Despite its importance, link prediction algorithms can be biased by disfavoring the links between individuals in particular demographic groups. In this paper, we study one particular type of bias, namely, the bias in predicting inter-group links (i.e., links across different demographic groups). First, we formalize the definition of bias in link prediction by providing quantitative measurements of accuracy disparity, which measures the difference in prediction accuracy of inter-group and intra-group links. Second, we unveil the existence of bias in six existing state-of-the-art link prediction algorithms through extensive empirical studies over real world datasets. Third, we identify the imbalanced density across intra-group and inter-group links in training graphs as one of the underlying causes of bias in link prediction. Based on the identified cause, fourth, we design a pre-processing bias mitigation method named FairLP to modify the training graph, aiming to balance the distribution of intra-group and inter-group links while preserving the network characteristics of the graph. FairLP is model-agnostic and thus is compatible with any existing link prediction algorithm. Our experimental results on real-world social network graphs demonstrate that FairLP achieves better trade-off between fairness and prediction accuracy than the existing fairness-enhancing link prediction methods.
Cross-Platform Multimodal Misinformation: Taxonomy, Characteristics and Detection for Textual Posts and Videos
Authors: Micallef Nicholas , Sandoval-Castañeda Marcelo , Cohen Adi , Ahamad Mustaque , Kumar Srijan , Memon Nasir
Published: November 2022

Social media posts that direct users to YouTube videos are one of the most effective techniques for spreading misinformation. However, it has been observed that such posts rarely get deleted or flagged.
Since multi-modal misinformation that leads to compelling videos has more impact than using just textual content, it is important to characterize and detect such textual post and video pairs to prevent users from becoming victims of misinformation. To address this gap, we build a taxonomy of how links to YouTube videos are used on social media platforms. We then use pairs of posts and videos annotated with this taxonomy to test several classification models built using cross-platform features. Our work reveals several characteristics of post-video pairs, in terms of how posts and videos are related to each other, the type of content they share, and their collective outcome.
In addition, we find that traditional approaches to misinformation detection that rely only on text from posts miss a significant number of post-video pairs that contain misinformation.
More importantly, we find that to reduce the spread of misinformation via post-video pairs, classifiers would be more effective if they are designed to use data and features from multiple diverse platforms.
“Rough Day … Need a Hug”: Learning Challenges and Experiences of the Alzheimer’s Disease and Related Dementia Caregivers on Reddit
Authors: Ni Congning , Malin Bradley , Song Lijun , Jefferson Angela , Commiskey Patricia , Yin Zhijun
Published: November 2022

Alzheimer’s disease and related dementia (ADRD) is a collection of disorders involving mental deterioration, which is often quite distressing for the individuals and those caring for them. Online social media platforms have become popular environments for people to share their ADRD caring challenges and experiences. Despite encouraging findings in the literature regarding online support for ADRD caregivers, studies to date have focused only on a single online community about ADRD, which leads to an incomplete picture of the needs of ADRD caregivers. Additionally, the large volume of data from online communities makes it challenging for both researchers and caregivers to efficiently discover discussions about ADRD care. In this paper, we focus on Reddit, an online rating and discussion platform that consists of many communities, or subreddits, and aim to analyze the topic difference regarding ADRD care between ADRD and non-ADRD subreddits. To do so, we first develop a two-stage classification framework to extract posts about ADRD care from the entire Reddit. Then, we apply structured topic modeling to investigate what has been discussed on ADRD care and how such discussions are prevalent in different types of subreddits. Our results show that non-ADRD subreddits contribute 68.5% of submissions of ADRD care, more than twice as many as ADRD subreddits. Moreover, non-ADRD subreddits are more likely to disclose legal and financial issues, negative relationships and mental health, while ADRD subreddits are more likely to talk about memory loss, sleeping and diet issues, the disease and clinical visits. Our findings suggest that research in this area should look into discussions beyond ADRD communities to gain a comprehensive understanding of ADRD caring experiences and challenges.
The Gospel according to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information
Authors: Papasavva Antonis , Aliapoulios Max , Ballard Cameron , Cristofaro Emiliano De , Stringhini Gianluca , Zannettou Savvas , Blackburn Jeremy
Published: November 2022

The QAnon conspiracy theory claims that a cabal of (literally) blood-thirsty politicians and media personalities are engaged in a war to destroy society. By interpreting cryptic “drops” of information from an anonymous insider calling themself Q, adherents of the conspiracy theory believe that Donald Trump is leading them in an active fight against this cabal. QAnon has been covered extensively by the media, as its adherents have been involved in multiple violent acts, including the January 6th, 2021 seditious storming of the US Capitol building. Nevertheless, we still have relatively little understanding of how the theory evolved and spread on the Web, and the role played in that by multiple platforms.

To address this gap, we study QAnon from the perspective of “Q” themself. We build a dataset of 4,949 canonical Q drops collected from six “aggregation sites,” which curate and archive them from their original posting to anonymous and ephemeral image boards. We expose that these sites have a relatively low (overall) agreement, and thus at least some Q drops should probably be considered apocryphal. We then analyze the Q drops’ contents to identify topics of discussion and find statistically significant indications that drops were not authored by a single individual. Finally, we look at how posts on Reddit are used to disseminate Q drops to wider audiences. We find that dissemination was (initially) limited to a few sub-communities and that, while heavy-handed moderation decisions have reduced the overall issue, the “gospel” of Q persists on the Web.
Measuring Alignment of Online Grassroots Political Communities with Political Campaigns
Authors: Raymond Cameron , Waller Isaac , Anderson Ashton
Published: November 2022

Social media reduces barriers for the formation of large, self-organizing grassroots communities. For political campaigns this poses significant opportunities to address declining party membership, but also reputational risks and potential loss of campaign coherence. While balancing these factors is often done informally, we adopt a behavioural approach by using neural community embeddings to evaluate online communities along cultural, political, and demographic dimensions. We apply this technique to the 2020 U.S. Democratic presidential primaries and the website Reddit, providing novel insights into the important tension between campaigns and third-party actors. Using two benchmark comparison classes, we demonstrate that our embedding dimensions mirror their offline analogues, but more so the views of a candidate's supporters than the candidate's themselves. Finally, we introduce temporal aspects to our community embedding to evaluate the stability of political communities and their interrelations. These analyses serve as an exploration and application of our novel embedding methodology, and give insight into the relationship between online communities and the movements they support.
Developing Self-Advocacy Skills through Machine Learning Education: The Case of Ad Recommendation on Facebook
Authors: Register Yim , Spiro Emma S.
Published: November 2022

Facebook users interact with algorithms every day. These algorithms can perpetuate harm via incongruent targeted ads, echo chambers, or "rabbit hole" recommendations. Education around the machine learning (ML) behind Facebook (FB) can help users to point out algorithmic bias and harm, and advocate for themselves effectively when things go wrong. One algorithm that FB users interact with regularly is User-Based Collaborative Filtering (UB-CF) which provides the basis for ad recommendation. We contribute a novel research approach for teaching users about a commonly used algorithm in machine learning in real-world context -- an instructive web application using real examples built from the user's own FB data on ad interests. The instruction also prompts users to reflect on their interactions with ML systems, specifically Facebook. In a between-subjects design, we tested both Data Science Novices and Experts on the efficacy of the UB-CF instruction. Taking care to highlight the voices of marginalized users, we use the application as a prompt for surfacing potential harms perpetuated by FB ad recommendations, and qualitatively analyze themes of harm and proposed solutions provided by users themselves. The instruction increased comprehension of UB-CF for both groups, and we show that comprehension is associated with mentioning the mechanisms of the algorithm more in advocacy statements, a crucial component of a successful argument. We provide recommendations for increased algorithmic transparency on social media and for including marginalized voices in the conversation of algorithmic harm that are of interest both to social media researchers and ML educators.
Influence of Repetition through Limited Recall
Authors: Sassine Jad , Rahimian M. Amin , Eckles Dean
Published: November 2022

Decision makers who receive many signals are subject to imperfect recall. This is especially important when learning from feeds that aggregate messages from many senders on social media platforms. In this paper, we study a stylized model of learning from feeds and highlight the inefficiencies that arise due to imperfect recall. In our model, failure to recall a specific message comes from the accumulation of messages which creates interference. We characterize the influence of each sender according to the rate at which she sends messages and to the strength of interference. Our analysis indicates that imperfect recall not only leads to double-counting and extreme opinions in finite populations, but also impedes the ability of the receiver to learn the true state as the population of the senders increases. We estimate the strength of interference in an online experiment where participants are exposed to (non-informative) repeated messages and they need to estimate the opinion of others. Results show that interference plays a significant role and is weaker among participants who disagree with each other. Our work has implication for the diffusion of information in networks, especially when it is false because it is shared and repeated more than true information.
Engaging Politically Diverse Audiences on Social Media
Authors: Saveski Martin , Beeferman Doug , McClure David , Roy Deb
Published: November 2022

We study how political polarization is reflected in the social media posts used by media outlets to promote their content online. In particular, we track the Twitter posts of several media outlets over the course of more than three years (566K tweets), and the engagement with these tweets from other users (104M retweets), modeling the relationship between the tweet text and the political diversity of the audience. We build a tool that integrates our model and helps journalists craft tweets that are engaging to a politically diverse audience, guided by the model predictions. To test the real-world impact of the tool, we partner with the PBS documentary series Frontline and run a series of advertising experiments on Twitter. We find that in seven out of the ten experiments, the tweets selected by our model were indeed engaging to a more politically diverse audience, reducing the gap in engagement between left- and right-leaning users by 20.3%, on average, and illustrating the effectiveness of our approach.
A Tale of Two Subreddits: Measuring the Impacts of Quarantines on Political Engagement on Reddit
Authors: Shen Qinlan , Rosé Carolyn P.
Published: November 2022

Concerns over the spread of abusive content in social media have led online platforms to explore large scale community-level forms of moderation.
As an alternative to full bans, Reddit has implemented quarantines of subreddits, limiting access to controversial communities without explicit removal.
Previous evaluations of the effectiveness of quarantines have focused on their impact on toxicity in affected communities.
In this work, however, we focus on examining the impacts of quarantines of political subreddits on issues of political engagement in the broader Reddit community.
We present a case study of two ideologically distinct political subreddits, r/The_Donald and r/ChapoTrapHouse, and examine the impact of quarantines on activity, visibility, and political discussion.
We find that while quarantines had a homogenizing effect on participation in political subreddits, they had limited impact on the visibility of community-internal issues and political language, due to the entrenchment of subreddit norms.
Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study
Authors: Shevtsov Alexander , Tzagkarakis Christos , Antonakaki Despoina , Ioannidis Sotiris
Published: November 2022

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and
an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious
handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination and communication
purposes, the goal is to manipulate the public opinion and the electorate towards a certain direction, specific ideology, or political party. This paper focuses on the design of a novel system for identifying
Twitter bots based on labeled Twitter data. To this end, a supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm, where the hyper-parameters are tuned via cross-validation.

Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions by calculating feature importance, using the game theoretic-based Shapley values.
Experimental evaluation on distinct Twitter datasets demonstrate the superiority of our approach, in terms of bot detection accuracy, when compared against a recent state-of-the-art Twitter bot detection method.
Online Social Support via Avatar Communication Buffers Harmful Effects of Offline Bullying Victimization
Authors: Takano Masanori , Yokotani Kenji
Published: November 2022

Online social support via avatar communication is a powerful tool for bullying victims because they often lack offline social resources. Additionally, avatar communication allows users rich nonverbal interactions (e.g., emotional expressions) while maintaining online anonymity. This study investigates the role of online social support via avatars for victims and how to facilitate such support. Accordingly, we conducted an online questionnaire survey twice on an avatar communication application, Pigg Party, regarding mental health, offline and online social support, and offline bullying victimization (participants: 3,288 (1st wave) and 758 (2nd wave)). We found that online social support via avatars supplemented insufficient offline social resources, particularly when there was a high risk of offline bullying victimization. Furthermore, we investigated how online social support is improved by ego networks using social network data from Pigg Party. We demonstrated that belonging to large and closely connected communities can enhance online social support. Our findings suggest that avatar communication applications can improve players' mental health through online social support, reinforced by facilitating ego networks.
What Makes Online Communities ‘Better’? Measuring Values, Consensus, and Conflict across Thousands of Subreddits
Authors: Weld Galen , Zhang Amy X. , Althoff Tim
Published: November 2022

Making online social communities ‘better’ is a challenging undertaking, as online communities are extraordinarily varied in their size, topical focus, and governance. As such, what is valued by one community may not be valued by another.
However, community values are challenging to measure as they are rarely explicitly stated.
In this work, we measure community values through the first large-scale survey of community values, including 2,769 reddit users in 2,151 unique subreddits. Through a combination of survey responses and a quantitative analysis of publicly available reddit data, we characterize how these values vary within and across communities.
Amongst other findings, we show that community members disagree about how safe their communities are, that longstanding communities place 30.1% more importance on trustworthiness than newer communities, and that community moderators want their communities to be 56.7% less democratic than non-moderator community members.
These findings have important implications, including suggesting that care must be taken to protect vulnerable community members, and that participatory governance strategies may be difficult to implement.
Accurate and scalable modeling of community values enables research and governance which is tuned to each community's different values. To this end, we demonstrate that a small number of automatically quantifiable features capture a significant yet limited amount of the variation in values between communities with a ROC AUC of 0.667 on a binary classification task.
However, substantial variation remains, and modeling community values remains an important topic for future work.
We make our models and data public to inform community design and governance.
VaccinEU: COVID-19 Vaccine Conversations on Twitter in French, German and Italian
Authors: Giovanni Marco Di , Pierri Francesco , Torres-Lugo Christopher , Brambilla Marco
Published: November 2022

Despite the increasing limitations for unvaccinated people, in many European countries there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. We study the role of online social media in influencing individuals' opinion towards getting vaccinated by designing a large-scale collection of Twitter messages in three different languages -- French, German and Italian -- and providing public access to the data collected. Focusing on the European context, our VaccinEU dataset aims to help researchers to better understand the impact of online (mis)information about vaccines and design more accurate communication strategies to maximize vaccination coverage. Data can be fully accessed in a Dataverse repository and a GitHub repository.
Exploring Data Literacy Levels in the Crowd – the Case of COVID-19
Authors: Fan Shaoyang , Han Lei , Demartini Gianluca , Sadiq Shazia
Published: November 2022

During global health crises, the use of data becomes critical to control the spread of infections, to inform the general public and to foster safe behaviors. The ability of people to read and understand data (i.e., data literacy) has the potential to affect human behaviors. In this paper, we study non-expert human subjects' ability to make accurate interpretations of complex pandemic data visualizations designed for general public consumption. We present them with popular plots and graphs that have been shown by traditional and social media, and ask them to answer questions to assess their data literacy at three levels: extracting information, finding relationships among data, and expanding or predicting information. Our results show the presence of variance in interpretations and reveal insights into how messages communicated through data may be perceived differently by different people. We also highlight the importance of designing communication strategies that ensure the spread of the right message through data.
Supporting Online Toxicity Detection with Knowledge Graphs
Authors: Lobo Paula Reyero , Daga Enrico , Alani Harith
Published: November 2022

Due to the rise in toxic speech on social media and other online platforms, there is a growing need for systems that could automatically flag or filter such content. Various supervised machine learning approaches have been proposed, trained from manually-annotated toxic speech corpora. However, annotators sometimes struggle to judge or to agree on which text is toxic and which group is being targeted in a given text. This could be due to bias, subjectivity, or unfamiliarity
with used terminology (e.g. domain language, slang). In this paper, we propose the use of a knowledge graph to help in better understanding such toxic speech annotation issues. Our empirical results show that 3% in a sample of 19k texts mention terms associated with frequently attacked gender and sexual orientation groups that were not correctly identified by the annotators.
A Large-Scale Study of MySpace: Observations and Implications for Online Social Networks
Authors: Caverlee James
Published: November 2022

We study the characteristics of large online social networks through an extensive analysis of over 1.9 million MySpace profiles in an effort to understand who is using these networks and how they are being used. We study MySpace through a comparative study over three different, but related, facets: (i) the sociability of users in MySpace based on relationship, messaging, and group participation; (ii) the demographic characteristics of MySpace users in terms of age, gender, and location, and a study of how these factors correlate with their privacy preferences; and (iii) the text artifacts of MySpace users, which can be used to construct language models that distinguish MySpace users not just by who they say they are but also by the language model they employ. We find a number of surprising results and conjecture several potential research directions based on our observations
The Politics of Sourcing: A Study of Journalistic Practices in the Blogosphere
Authors: Walejko Gina , Ksiazek Thomas
Published: November 2022

Digital media lowers barriers to entry and has the ability to renegotiate traditional news-making power structures. However, it remains to be seen whether or not the people that use tools like blogs actually challenge those frameworks. Offline reporters predominantly use government officials as sources while online journalists and newspaper institutions typically cite their own pages and posts. In order to understand whether or not journalistic norms are challenged in the blogosphere, we look at the sourcing practices of a diverse group of 40 bloggers. Specifically, we sample 400 blogposts that discussed global warming between 2004 and May of 2007. Operationalizing hyperlinks as sources, we then code the 3264 hyperlinks in these posts. Results indicate that government sources are linked to the least. Instead, bloggers tend to link to the online versions of traditional news media as well as to other blogs. However, we find that bloggers also link to miscellaneous and civic sources such as academics and non-profit organizations about one-quarter of the time, somewhat challenging the dominance of government and traditional media. We conclude that blogging on non-traditional topics may subvert who gets heard in an online world, but further research is needed on this topic.
Recovering Implicit Thread Structure in Newsgroup Style Conversations
Authors: Wang Yi-Chia , Mahesh Joshi Mahesh Joshi , Cohen William
Published: November 2022

On-line discussions are composed of multiple inter-woven threads, regardless of whether that threaded structure is made explicit in the representation and presentation of the conversational data. Recovering the thread structure is valuable since it makes it possible to isolate discussion related to specific subtopics or related to particular conversational goals. In prior work, thread structure has been reconstructed using explicit meta-data features such as posted by and reply to relationships. The contribution of this paper is a novel approach to recovering thread structure in discussion forums where this explicit meta-data is missing. This approach uses a graph-based representation of a collection of messages where connections between messages are postulated based on inter-message similarity. We evaluate three variations of this simple baseline approach that exploit in different ways the temporal relationships between messages. The results show that the three proposed approaches outperform the simple threshold-cut baseline.
Social Access Control for Social Media Using Shared Knowledge Questions
Authors: Toomim Michael , Zhang Xianhang , Fogarty James , Morris Nathan
Published: November 2022

Managing privacy of online content is difficult. We present a simple social access control where sharers specify test questions of shared knowledge, such as "what is our school mascot," instead of creating authenticated accounts and specifying explicit access control rules for all potential accessors. This demo will let attendees interact with our Facebook prototype. We will also explain prior studies that elucidate the context of photo sharing security, gauge the difficulty of creating shared knowledge questions, measure their resilience to adversarial attack, and evaluate users' abilities to understand and predict this resilience.
Discovering Relationships among Tags and Geotags
Authors: Lee Sang , Won Dongwoo , McLeod Dennis
Published: November 2022

This paper presents an analysis of the correlation of annotated information unit (textual) tags and geographical identification metadata geotags. In this paper, to make it possible for geotagging to be used in analysis with tagging, we prove that there is a strong correlation between tagging and geotagging information. Our approach uses tag similarity and newly employed geographical distribution similarity to determine inter-relationships among tags and geotags. From our initial experiments, we show that the power law is established between tag similarity and geographical distribution similarity; they are strongly correlated and the correlation can be used to find more relevant tags in the tag space. The power law, which is any polynomial relationship that exhibits the property of scale invariance, confirms that there is the relationship between tagging and geotagging and the relationship is scalable in size of tags and geotags.