bluuebunny/crossref_metadata_2025 huggingface dataset:
More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks
Authors: Choi Minje , Budak Ceren , Romero Daniel M. , Jurgens David
Published: November 2022

Topics in conversations depend in part on the type of interpersonal relationship between speakers, such as friendship, kinship, or romance. Identifying these relationships can provide a rich description of how individuals communicate and reveal how relationships influence the way people share information. Using a dataset of more than 9.6M dyads of Twitter users, we show how relationship types influence language use, topic diversity, communication frequencies, and diurnal patterns of conversations. These differences can be used to predict the relationship between two users, with the best predictive model achieving a macro F1 score of 0.70. We also demonstrate how relationship types influence communication dynamics through the task of predicting future retweets. Adding relationships as a feature to a strong baseline model increases the F1 and recall by 1% and 2%. The results of this study suggest relationship types have the potential to provide new insights into how communication and information diffusion occur in social networks.
User Identity Linkage for Different Behavioral Patterns across Domains
Authors: Kusano Genki , Oyamada Masafumi
Published: November 2022

As customers use and benefit from multiple services, a large amount of customer data are accumulating daily. Connecting a customer's identity on a service with her identity on a different service, known as user identity linkage (UIL), enables a comprehensive understanding of users in a variety of real-world applications. The difficulties of UIL tasks in marketing applications are mainly the lack of user demographics and diverse user behavioral patterns, which differs from UIL tasks in social networking services that previous UIL methods have mainly been used to tackle. In this paper, we propose a novel method for UIL for different behavioral patterns to determine whether two given behavioral histories come from the same user without using any user demographics. Our proposed method links users by using natural language processing to efficiently characterize user intrinsic features and bridging the gap between two different behavioral patterns of the same user. We conducted experiments to evaluate our proposed method for three real-world open source datasets and observed that it successfully linked users compared to conventional UIL methods.
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
Authors: Lim Derek , Benson Austin R.
Published: November 2022

Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are driven by diverse processes across differing platforms. Thus, models for one platform are not necessarily applicable to others. Here, we study the temporal dynamics of Genius, a platform mainly designed for user-contributed annotations of song lyrics. A unique aspect of Genius is that the annotations are extremely local --- an annotated lyric may just be a few lines of a song --- but also highly related, e.g., by song, album, artist, or genre.

We analyze several dynamical processes associated with lyric annotations and their edits, which differ substantially from models for other platforms. For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that offers one possible explanation for such behavior. We also find several traits appearing early in a user's lifespan of contributions that distinguish (eventual) experts from non-experts. Combining our findings, we develop a model for early prediction of user expertise.
Coordinated Behavior on Social Media in 2019 UK General Election
Authors: Nizzoli Leonardo , Tardelli Serena , Avvenuti Marco , Cresci Stefano , Tesconi Maurizio
Published: November 2022

Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable.
Here, we propose a new network-based framework for uncovering and studying coordinated behaviors on social media. Our research extends existing systems and goes beyond limiting binary classifications of coordinated and uncoordinated behaviors. It allows to expose different coordination patterns and to estimate the degree of coordination that characterizes diverse communities. We apply our framework to a dataset collected during the 2019 UK General Election, detecting and characterizing coordinated communities that participated in the electoral debate. Our work conveys both theoretical and practical implications and provides more nuanced and fine-grained results for studying online information manipulation.
The Media During the Rise of Trump: Identity Politics, Immigration,"Mexican" Demonization and Hate-Crime
Authors: Papakyriakopoulos Orestis , Zuckerman Ethan
Published: November 2022

In this study, we investigate the role of the US online media ecosystem in Donald Trump’s rise and consolidation to power (2013-2019). We analyze over 54 million articles from online U.S. media and locate a media narrative shift related to three issues that Trump focused on during his 2016 presidential campaign: immigration, Latinx people, and identity politics. Given this, we develop Natural Language Processing techniques based on word embeddings to quantify biased representations of minorities in the media across time. We locate an increase in biased speech that parallels Trump’s rise to power, and a clear partisan pattern to this bias. Comparing articles related to Latinx, African Americans, Asian Americans, and Jewish Americans, we show that the most biased representations in terms of stereotypes and prejudice are found when the media uses the term "Mexicans," which Trump used as a blanket term for a diverse Latin and Hispanic population. We develop econometric models to understand the narrative shift and to associate media reporting with real-world dynamics. We find that the media’s focus on the new narratives and the intensity of biased representations are statistically associated with hate-crime incidents at the state level. These results illustrate how media amenability to politicians' agenda-building can result in the discrimination of social groups, as well as how problematic media reporting is linked to real-world harms. Consequently, we reflect on the role of the media as gatekeepers of public discourse and discuss the conditions for a diverse and inclusive media ecosystem.
Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia
Authors: Park Chan Young , Yan Xinru , Field Anjalie , Tsvetkov Yulia
Published: November 2022

Specific lexical choices in narrative text reflect both the writer's attitudes towards people in the narrative and influence the audience's reactions. Prior work has examined descriptions of people in English using contextual affective analysis, a natural language processing (NLP) technique that seeks to analyze how people are portrayed along dimensions of power, agency, and sentiment. Our work presents an extension of this methodology to multilingual settings, which is enabled by a new corpus that we collect and a new multilingual model. We additionally show how word connotations differ across languages and cultures, highlighting the difficulty of generalizing existing English datasets and methods. We then demonstrate the usefulness of our method by analyzing Wikipedia biography pages of members of the LGBT community across three languages: English, Russian, and Spanish. Our results show systematic differences in how the LGBT community is portrayed across languages, surfacing cultural differences in narratives and signs of social biases. Practically, this model can be used to identify Wikipedia articles for further manual analysis---articles that might contain content gaps or an imbalanced representation of particular social groups.
How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement
Authors: Park Kunwoo , Kwak Haewoon , An Jisun , Chawla Sanjay
Published: November 2022

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those media edited tweets against original headlines and the effects of such changes. To estimate the effects of editing news headlines for social media sharing in audience engagement, we present a systematic analysis that incorporates a causal inference technique with deep learning; using propensity score matching, it allows for estimating potential (dis-)advantages of an editing style compared to counterfactual cases where a similar news article is shared with a different style. According to the analyses of various editing styles, we report common and differing effects of the styles across the outlets. To understand the effects of various editing styles, media outlets could apply our easy-to-use tool by themselves.
On Positive Moderation Decisions
Authors: Samory Mattia
Published: November 2022

A crucial role of moderators is to decide what content is allowed in their community. Though research has advanced its understanding of the content that moderators remove, such as spam and hateful messages, we know little about what moderators approve. This work analyzes moderator-approved content from 49 Reddit communities. It sheds light on the complexity of moderation by giving empirical evidence that the difference between approved and removed content is often subtle. In fact, approved content is more similar to removed content than it is to the remaining content in a community---i.e. content that has never been reviewed by a moderator---along dimensions of topicality, psycholinguistic categories, and toxicity. Building upon this observation, I quantify the implications for NLP systems aimed at supporting moderation decisions, which often conflate moderator-approved content with content that has potentially never been reviewed by a moderator. I show that these systems would remove over half of the content that moderators approved. I conclude with recommendations for building better tools for automated moderation, even when approved content is not available.
Variation in Situational Awareness Information due to Selection of Data Source, Summarization Method, and Method Implementation
Authors: Sarol M. Janina , Dinh Ly , Diesner Jana
Published: November 2022

The increasing amount of information available about crisis events calls for tools that help people to efficiently and reliably sift through and summarize this information. When using such tools, practitioners have to make choices that can affect not only the relevant information retrieved, but also their understanding or situational awareness of a crisis. We present a study that assesses the impact of some of these choices on the resulting situational awareness information. We focus on commonly used sources (news, tweets, and blogs), text mining methods (topic modeling and text summarization), and a disaster-related text mining task (needs detection). Our main application context is the 2010 Haiti earthquake, supplemented by data about 11 other events. Our results show that situational awareness information retrieved about the same crisis event can be different or even conflicting based on these choices. We also found that analyzing news data can be helpful in aiding response efforts as they contain unique situational awareness information not found in other considered sources, including first-responder accounts.
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Authors: Sharma Karishma , He Xinran , Seo Sungyong , Liu Yan
Published: November 2022

The dissemination of fake news intended to deceive people,
influence public opinion and manipulate social outcomes, has
become a pressing problem on social media. Moreover, information
sharing on social media facilitates diffusion of viral
information cascades. In this work, we focus on understanding
and leveraging diffusion dynamics of false and legitimate
contents in order to facilitate network interventions for
fake news mitigation. We analyze real-world Twitter datasets
comprising fake and true news cascades, to understand differences
in diffusion dynamics and user behaviours with regards
to fake and true contents. Based on the analysis, we model
the diffusion as a mixture of Independent Cascade models
(MIC) with parameters \theta_T , \theta_F over the social network graph;
and derive unsupervised inference techniques for parameter
estimation of the diffusion mixture model from observed, unlabeled
cascades. Users influential in the propagation of true
and fake contents are identified using the inferred diffusion
dynamics. Characteristics of the identified influential users
reveal positive correlation between influential users identified
for fake news and their relative appearance in fake news cascades.
Identified influential users tend to be related to topics
of more viral information cascades than less viral ones; and
identified fake news influential users have relatively fewer
counts of direct followers, compared to the true news influential
users. Intervention analysis on nodes and edges demonstrates
capacity of the inferred diffusion dynamics in supporting
network interventions for mitigation.
Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention
Authors: Solans David , Tauchmann Christopher , Farrell Aideen , Kappler Karolin , Huber Hans-Hendrik , Castillo Carlos , Kersting Kristian
Published: November 2022

Artificial Intelligence (AI) and its relation with societies has become an increasingly interesting subject of study for the social sciences. Nevertheless, there is still an important lack of interdisciplinary and empirical research applying social theories to the field of AI. We here aim to shed light on the interactions between humans and autonomous systems and analyse the moral conventions, which underly these interactions and cause moments of conflict and cooperation. For this purpose we employ the Economics of Convention (EC), originally developed in the context of economic processes of production and management involving humans, objects and machines. We create a dataset from three relevant text sources and perform a qualitative exploration of its content. Then, we train a combination of Machine Learning (ML) classifiers on this dataset, which achieve an average classification accuracy of 83.7%. A qualitative and quantitative evaluation of the predicted conventions reveals, inter alia, that the Industrial and Inspired conventions tend to co-exist in the AI domain.
This is the first time, ML classifiers are used to study the EC in different AI-related text types. Our analysis of a larger dataset is especially beneficial for the social sciences.
Online Communication Shifts in the Midst of the Covid-19 Pandemic: A Case Study on Snapchat
Authors: Yang Qi , Wang Weinan , Pierce Lucas , Vaish Rajan , Shi Xiaolin , Shah Neil
Published: November 2022

The Covid-19 pandemic has created large shifts in how people stay connected with each other in lieu of social distancing and isolation measures. More and more, individuals have turned to online communications as a necessary replacement for in-person interaction. Despite this, the research community has little understanding of how online communications have been influenced by the offline impacts of Covid-19. Our work touches upon this topic. Specifically, we study research questions around the impact of Covid-19 on online public and private sharing propensity, its influence on online communication homophily, and correlations between online communication and offline case severity in the United States. To do so, we study the usage patterns of 79 million US-based users on Snapchat, a large, leading mobile multimedia-driven social sharing platform. Our findings suggest that Covid-19 has increased propensity to privately communicate with friends, while decreasing propensity to publicly share content when users are out-and-about. Moreover, online communications have observed a marked decrease in baseline homophily across locations, ages and genders, with relative increases in cross-group communications. Finally, we observe that increased offline positive Covid-19 case severity in US states is associated with widening gaps between across-state and within-state communication increases after the onset of Covid-19, as well as marked declines in public sharing. We hope our findings drive further interest and work on online communication changes during pandemics and other extended times of crisis.
What's Your Value of Travel Time? Collecting Traveler-Centered Mobility Data via Crowdsourcing
Authors: Consonni Cristian , Basile Silvia , Manca Matteo , Boratto Ludovico , Freitas André , Kovacikova Tatiana , Pourhashem Ghadir , Cornet Yannick
Published: November 2022

Mobility and transport, by their nature, involve crowds and require the coordination of multiple stakeholders - such as policy-makers, planners, transport operators, and the travelers themselves. However, traditional approaches have been focused on time savings, proposing to users solutions that include the shortest or fastest paths. We argue that this approach towards travel time value is not centered on a traveler's perspective. To date, very few works have mined data from crowds of travelers to test the efficacy and efficiency of novel mobility paradigms. In this paper, we build upon a different paradigm of worthwhile time in which travelers can use their travel time for other activities; we present a new dataset, which contains data about travelers and their journeys, collected from a dedicated mobile application. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive and negative experience factors. To showcase this new dataset's potential, we also present a use case, which compares corresponding trip legs with different transport modes, studying experience factors that negatively impact users using cycling and public transport as alternatives to cars. We conclude by discussing other application domains and research opportunities enabled by the dataset.
A Dataset of Multidimensional and Multilingual Social Opinions for Malta’s Annual Government Budget
Authors: Cortis Keith , Davis Brian
Published: November 2022

This paper presents three high quality social opinion datasets in the socio-economic domain, specifically Malta's annual Government Budgets of 2018, 2019 and 2020. They contain over 6,000 online posts of user-generated content in English and/or Maltese, gathered from newswires and social networking services. These have been annotated for multiple opinion dimensions, namely subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining. Moreover, they can be used for policy formulation, policy-making, decision-making and decision-taking. This research can also support similar initiatives in other countries, studies in the socio-economic domain and applied in other areas, such as Politics, Finance, Marketing, Advertising, Sales and Education.
Check Mate: Prioritizing User Generated Multi-Media Content for Fact-Checking
Authors: Prabhakar Tarunima , Gupta Anushree , Nadig Kruttika , George Denny
Published: November 2022

Volume of content and misinformation on social media is rapidly increasing. There is a need for systems that can support fact checkers by prioritizing content that needs to be fact checked. Prior research on prioritizing content for fact-checking has focused on news media articles, predominantly in English language. Increasingly, misinformation is found in user-generated content. In this paper we present a novel dataset that can be used to prioritize check-worthy posts from multi-media content in Hindi. It is unique in its 1) focus on user generated content, 2) language and 3) accommodation of multi-modality in social media posts. In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation. The data is accessible on Zenodo (https://zenodo.org/record/4032629) under Creative Commons Attribution License (CC BY 4.0).
Media Cloud: Massive Open Source Collection of Global News on the Open Web
Authors: Roberts Hal , Bhargava Rahul , Valiukas Linas , Jen Dennis , Malik Momin M. , Bishop Cindy Sherman , Ndulue Emily B. , Dave Aashka , Clark Justin , Etling Bruce , Faris Robert , Shah Anushka , Rubinovitz Jasmin , Hope Alexis , D'Ignazio Catherine , Bermejo Fernando , Benkler Yochai , Zuckerman Ethan
Published: November 2022

We present the first full description of Media Cloud, an open source platform based on crawling hyperlink structure in operation for over 10 years, that for many uses will be the best way to collect data for studying the media ecosystem on the open web. We document the key choices behind what data Media Cloud collects and stores, how it processes and organizes these data, and its open API access as well as user-facing tools. We also highlight the strengths and limitations of the Media Cloud collection strategy compared to relevant alternatives. We give an overview two sample datasets generated using Media Cloud and discuss how researchers can use the platform to create their own datasets.
SIGNLENS: A Tool for Analyzing People's Polarization Social Relationship Based on Signed Graph Modeling
Authors: Huang Junjie , Shen Huawei , Cheng Xueqi
Published: November 2022

SIGNLENS is a tool that helps to analyze polarized social relationships based on signed graph modeling.
It could be used by political analysts, historical researchers, and social media community organizations to analyze the social network with negative links (i.e., conflicts or disagreement).
SIGNLENS can handle signed temporal graph by doing individual analysis and group analysis.
Individual analysis gives a Web dashboard to users to explore the nodes who users focus on.
Group analysis provides another perspective of analyzing the whole graph, which mainly analyzes the changes in the unbalance of the signed graph over time.
We give two examples to demonstrate how our tool works, which consist of The China Biographical Database and the United States Congress Vote.
More details about these two examples can be found in https://github.com/huangjunjie-cs/SIGNLENS.
Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate
Authors: Crupi Giuseppe , Mejova Yelena , Tizzani Michele , Paolotti Daniela , Panisson André
Published: November 2022

Italy was the first European country to be hit by COVID-19 in the early 2020, since then losing over 100,000 people to the disease. By the end of the vaccination campaign of 2021, 81% of the public received at least one dose. These dramatic developments were accompanied by a rigorous discussion around vaccination, both about its urgency and its possible negative effects. Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is thus imperative to understand the nature of this discourse, with a specific focus on the vaccination hesitant individuals, whose healthcare decisions may affect their communities and the country at large. In this study we ask, how has the Italian discussion around vaccination changed during the COVID-19 pandemic, and have the unprecedented events of 2020-2021 been able to break the echo chamber around this topic? We use a Twitter dataset spanning September 2019 - November 2021 to examine the state of polarization around vaccination. We propose a hierarchical clustering approach to find the largest communities in the endorsement networks of different time periods, and manually illustrate that it produces communities of users sharing a stance. Examining the structure of these networks, as well as textual content of their interactions, we find the stark division between supporters and hesitant individuals to continue throughout the vaccination campaign. However, we find an increasing commonality in the topical focus of the vaccine supporters and vaccine hesitant, pointing to a possible common set of facts the two sides may agree on. Still, we discover a series of concerns voiced by the hesitant community, ranging from unfounded conspiracies (microchips in vaccines) to public health policy discussion (vaccine passport limitations). We recommend an ongoing surveillance of this debate, especially to uncover concerns around vaccination before the public health decisions and official messaging are made public.
Social Media Reveals Urban-Rural Differences in Stress across China
Authors: Cui Jesse , Zhang Tingdan , Jaidka Kokil , Pang Dandan , Sherman Garrick , Jakhetiya Vinit , Ungar Lyle H. , Guntuku Sharath Chandra
Published: November 2022

Modelling differential stress expressions in urban and rural regions in China can provide a better understanding of the effects of urbanization on psychological well-being in a country that has rapidly grown economically in the last two decades. Using Weibo posts from over 65,000 users across 329 counties, we build hierarchical mixed-effects models to understand linguistic differences in the experiences and expressions of stress in urban-rural China. We analyze phrases, topical themes, and psycho-linguistic word choices in Weibo posts mentioning stress to better understand appraisal differences surrounding psychological stress in urban and rural communities in China; we then compare them with large-scale polls from Gallup. After controlling for socioeconomic and gender differences, we find that rural communities tend to express stress in emotional and personal themes such as relationships, health, and opportunity while users in urban areas express stress using relative, temporal, and external themes such as work, politics, and economics. We find corroborative trends in physical, financial, and social wellness with urbanization in Gallup polls.
Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario
Authors: Ebeling Régis , Saenz Carlos Abel Córdova , Nobre Jéferson Campos , Becker Karin
Published: November 2022

The outbreak of COVID‐19 had a huge global impact, and non-scientific beliefs and political polarization have significantly influenced the population's behavior. In this context, COVID vaccines were made available at an unprecedented time, but a high level of hesitance has been observed that can undermine community immunization. Traditionally, anti-vaccination attitudes are more related to conspiratorial thinking than political bias. In Brazil, a country with an exemplar tradition in large-scale vaccination programs, all COVID-related topics have also been discussed under a strong political bias. In this paper, we use a multi-dimensional analysis framework to understand if anti/pro-vaccination stances expressed by Brazilians in social media are influenced by political polarization. The analysis framework incorporates techniques to automatically infer from users their political orientation, topic modeling to discover their concerns, network analysis to characterize their social behavior, and the characterization of information sources and external influence. Our main findings confirm that anti/pro-stances are biased by political polarization, right and left, respectively. While a significant proportion of pro-vaxxers display haste for an immunization program and criticize the government's actions, the anti-vaxxers distrust a vaccine developed in a record time. Anti-vaccination stance is also related to prejudice against China (anti-sinovaxxers), revealing conspiratorial theories related to communism. All groups display an ``echo chamber" behavior, revealing they are not open to distinct views.
Characterizing Retweet Bots: The Case of Black Market Accounts
Authors: Elmas Tuğrulcan , Overdorf Rebekah , Aberer Karl
Published: November 2022

Malicious Twitter bots are detrimental to public discourse on social media. Past studies have looked at spammers, fake followers, and astroturfing bots, but retweet bots, which artificially inflate content, are not well understood. We present the first study focusing exclusively on retweet bots. We characterize retweet bots that have been uncovered by purchasing retweets from the black market. We detect whether they are fake or genuine accounts involved in inauthentic activities and what they do in order to appear legitimate. We also analyze their differences from human-controlled accounts. From our findings on the nature and life-cycle of retweet bots, we also point out several inconsistencies between the retweet bots used in this work and bots studied in prior works. Our findings challenge some of the fundamental assumptions related to bots and in particular how to detect them.
Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?
Authors: Epstein Ziv , Foppiani Nicolo , Hilgard Sophie , Sharma Sanjana , Glassman Elena , Rand David
Published: November 2022

Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study (N=1473 Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly.
Exposure Inequality in People Recommender Systems: The Long-Term Effects
Authors: Fabbri Francesco , Croci Maria Luisa , Bonchi Francesco , Castillo Carlos
Published: November 2022

People recommender systems may affect the exposure that users receive in social networking platforms, influencing attention dynamics and potentially strengthening pre-existing inequalities that disproportionately affect certain groups.
In this paper we introduce a model to simulate the feedback loop created by multiple rounds of interactions between users and a link recommender in a social network. This allows us to study the long-term consequences of those particular recommendation algorithms. Our model is equipped with several parameters to control: (i) the level of homophily in the network, (ii) the relative size of the groups, (iii) the choice among several state-of-the-art link recommenders, and (iv) the choice among three different stochastic user behavior models, that decide which recommendations are accepted or rejected.
Our extensive experimentation with the proposed model shows that a minority group, if homophilic enough, can get a disproportionate advantage in exposure from all link recommenders. Instead, when it is heterophilic, it gets underexposed. Moreover, while the homophily level of the minority affects the speed of the growth of the disparate exposure, the relative size of the minority affects the magnitude of the effect. Finally, link recommenders strengthen exposure inequalities at the individual level, exacerbating the“rich-get-richer” effect: this happens for both the minority and the majority class and independently of their level of homophily.
Improving Wikidata with Student-Generated Concept Maps
Authors: Freedman Hayden , Hoek André van der , Tomlinson Bill
Published: November 2022

Wikidata is a publicly available, crowdsourced knowledge base that contains interlinked concepts structured for use by intelligent systems. While Wikidata has experienced rapid growth, it is far from complete and faces challenges that prevent it from being used to its full potential. In this paper, we propose a novel method for improving Wikidata by engaging undergraduate students to contribute previously missing knowledge via concept mapping assignments. Rather than allow students to edit Wikidata directly, we describe a workflow in which knowledge is constructed by students and then reviewed by an expert. We present a case study in which we deployed a workflow in a large undergraduate course about sustainability, and find that it was able to contribute a substantial number of high quality statements that persisted in and contributed previously missing knowledge to Wikidata. This work provides a preliminary workflow for improving Wikidata based on classroom assignments, as well as recommendations for how future educational projects could continue to improve Wikidata or other public knowledge bases.
SAFER: Social Capital-Based Friend Recommendation to Defend against Phishing Attacks
Authors: Guo Zhen , Cho Jin-Hee , Chen Ing-Ray , Sengupta Srijan , Hong Michin , Mitra Tanushree
Published: November 2022

The tremendous growth of social media has been accompanied by highly advanced online social network (OSN) technologies. Such advanced technologies have been heavily utilized by perpetrators as convenient tools for deceiving people in online worlds. Social capital has been discussed as a powerful mechanism to leverage interpersonal relationships in social networks in order for an individual to achieve his/her goal. The beauty of social capital is the ability to materialize non-monetary, less costly, and non-economic resources into tools to solve social problems. In this paper, we aim to leverage social capital (SC) to minimize online users' vulnerabilities to online deception. In particular, we propose a Social cApital-based FriEnd Recommendation scheme, called SAFER, that can protect OSN users from phishing attacks. We quantify three dimensions of social capital, namely, structural, cognitive, and relational, based on user features obtained from real datasets and model a user's friending behavior based on their social capital. In addition, to model a user's behavior upon being attacked by a phishing attacker, we developed the so-called SER-SEIR (Susceptible, Exposed, Recovered-Susceptible, Exposed, Infected, and Recovered) model as a variant of the SEIR model. Via extensive simulation experiments based on two real datasets considering bot-based and human-based attackers performing phishing attacks, we demonstrate the performance of four SC-based friend recommendation schemes with three non-SC-based comparable counterparts in terms of the ratio of detecting attackers and the fraction of users in the states of S, E, I, and R. Based on the performance comparison, we analyze the overall trends of their performance in terms of the extent of resistance against phishing attacks by bot or human attackers.
Closing Pandora’s Box on Naver: Toward Ending Cyber Harassment
Authors: Kang Nam Gu , Kuo Tina , Grossklags Jens
Published: November 2022

In reaction to the high-profile celebrity suicides of Sulli and Goo Hara in 2019, attributed to cyberbullying, Korea's most popular internet platform Naver introduced a range of self-regulatory measures to curtail targeted hate comments online in 2020. These regulations include removing the entertainment comment section and pseudonymizing users by revealing comment history but maintaining user anonymity. To take a closer look at celebrity cyberbullying and analyze the effects of Naver's novel self-regulatory measures, we collected data about comments and emoticons from Naver News. In the celebrity space, we find that Sulli and Goo Hara indeed received negative comments and expressions using emoticons. However, our analyses of the self-regulatory measures demonstrate that while user interaction on Naver has decreased, the percentage of negative comments has also fallen, showing that Naver's regulations have had favorable effects.
Subreddit Links Drive Community Creation and User Engagement on Reddit
Authors: Krohn Rachel , Weninger Tim
Published: November 2022

On Reddit, individual subreddits are used to organize content and connect users. One mode of interaction is the subreddit link, which occurs when a user makes a direct reference to a subreddit in another community. Based on the ubiquity of these references, we have undertaken a study on subreddit links on Reddit, with the goal of understanding their impact on both the referenced subreddit, and on the subreddit landscape as a whole. By way of an extensive observational study along with several natural experiments using the entire history of Reddit, we were able to determine that (1) subreddit links are a significant driver of new subreddit creation; (2) subreddit links (2a) substantially drive activity in the referenced subreddit, and (2b) are frequently created in response to high levels of activity in the referenced subreddit; and (3) the graph of subreddit links has become less dense and more treelike over time. We conclude with a discussion of how these results confirm, add to, and in some cases conflict with existing theories on information-seeking behavior and self-organizing behavior in online social systems.
Identifying Hurricane Evacuation Intent on Twitter
Authors: Li Xintian , Hasan Samiul , Culotta Aron
Published: November 2022

Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.
Perspective-Taking to Reduce Affective Polarization on Social Media
Authors: Saveski Martin , Gillani Nabeel , Yuan Ann , Vijayaraghavan Prashanth , Roy Deb
Published: November 2022

The intensification of affective polarization worldwide has raised new questions about how social media platforms might be further fracturing an already-divided public sphere. As opposed to ideological polarization, affective polarization is defined less by divergent policy preferences and more by strong negative emotions towards opposing political groups, and thus arguably poses a formidable threat to rational democratic discourse. We explore if prompting perspective-taking on social media platforms can help enhance empathy between opposing groups as a first step towards reducing affective polarization. Specifically, we deploy a randomized field experiment through a browser extension to 1,611 participants on Twitter, which enables participants to randomly replace their feeds with those belonging to accounts whose political views either agree with or diverge from their own. We find that simply exposing participants to "outgroup" feeds enhances engagement, but not an understanding of why others hold their political views. On the other hand, framing the experience in familiar, empathic terms by prompting participants to recall a disagreement with a friend does not affect engagement, but does increase their ability to understand opposing views. Our findings illustrate how social media platforms might take simple steps that align with business objectives to reduce affective polarization.
If You Have a Reliable Source, Say Something: Effects of Correction Comments on COVID-19 Misinformation
Authors: Seo Haeseung , Xiong Aiping , Lee Sian , Lee Dongwon
Published: November 2022

In the post-truth era, particularly during the COVID-19 pandemic, an effective correction on misinformation is necessary to promote personal and public health. To better understand the effect of “correcting” misinformation, therefore, we investigated correction from different users on social media (e.g., individual users, fact-checking websites, and health organizations) and the frequency of correction (e.g., once vs. twice) in three online experiments. In each experiment, we evaluated participants’ perceived accuracy and willingness to share in terms of real and fake news of COVID-19, respectively. Across all experiments, a single correction from the health organizations effectively reduced participants’ perceived accuracy rating on the COVID-19 fake news. Experiments 2 and 3 revealed the effects of a single correction from individual users and fact-checking websites. Moreover, results of post-session questionnaires indicated that participants counted on the reliability of the sources in the correction. We did not obtain the consistent effects of frequent correction but verified the vulnerability of participants with high health anxiety to the COVID-19 fake news across all experiments. Overall, our study highlights the effects of user-initiated correction regardless of whether the user is an individual or an organization, as long as the correction contains a reliable source.
Characterizing Online Engagement with Disinformation and Conspiracies in the 2020 U.S. Presidential Election
Authors: Sharma Karishma , Ferrara Emilio , Liu Yan
Published: November 2022

Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world. Persistent manipulation of social media has resulted in increased concerns regarding the 2020 U.S. Presidential Election, due to its potential to influence individual opinions and social dynamics. In this work, we focus on the identification of distorted facts, in the form of unreliable and conspiratorial narratives in election-related tweets, to characterize discourse manipulation prior to the election. We apply a detection model to separate factual from unreliable (or conspiratorial) claims analyzing a dataset of 242 million election-related tweets. The identified claims are used to investigate targeted topics of disinformation, and conspiracy groups, most notably the far-right QAnon conspiracy group. Further, we characterize account engagements with unreliable and conspiracy tweets, and with the QAnon conspiracy group, by political leaning and tweet types. Finally, using a regression discontinuity design, we investigate whether Twitter's actions to curb QAnon activity on the platform were effective, and how QAnon accounts adapt to Twitter's restrictions.
Using Authorship Verification to Mitigate Abuse in Online Communities
Authors: Weerasinghe Janith , Singh Rhia , Greenstadt Rachel
Published: November 2022

Social media has become an important method for information sharing. This has also created opportunities for bad actors to easily spread disinformation and manipulate public opinion. This paper explores the possibility of applying Authorship Verification on online communities to mitigate abuse by analyzing the writing style of online accounts to identify accounts managed by the same person. We expand on our similarity-based authorship verification approach, previously applied on large fanfictions, and show that it works in open-world settings, shorter documents, and is largely topic-agnostic. Our expanded model can link Reddit accounts based on the writing style of only 40 comments with an AUC of 0.95, and the performance increases to 0.98 given more content. We apply this model on a set of suspicious Reddit accounts associated with the disinformation campaign surrounding the 2016 U.S. presidential election and show that the writing style of these accounts are inconsistent, indicating that each account was likely maintained by multiple individuals. We also apply this model to Reddit user accounts that commented on the WallStreetBets subreddit around the 2021 GameStop short squeeze and show that a number of account pairs share very similar writing styles. We also show that this approach can link accounts across Reddit and Twitter with an AUC of 0.91 even when training data is very limited.
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Authors: Weld Galen , West Peter , Glenski Maria , Arbour David , Rossi Ryan A. , Althoff Tim
Published: November 2022

Causal inference studies using textual social media data can provide actionable insights on human behavior. Making accurate causal inferences with text requires controlling for confounding which could otherwise impart bias. Recently, many different methods for adjusting for confounders have been proposed, and we show that these existing methods disagree with one another on two datasets inspired by previous social media studies. Evaluating causal methods is challenging, as ground truth counterfactuals are almost never available. Presently, no empirical evaluation framework for causal methods using text exists, and as such, practitioners must select their methods without guidance. We contribute the first such framework, which consists of five tasks drawn from real world studies. Our framework enables the evaluation of any casual inference method using text. Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements. We make all tasks, data, and models public to inform applications and encourage additional research.
#Emoji: A Study on the Association between Emojis and Hashtags on Twitter
Authors: Zhou Yuhang , Ai Wei
Published: November 2022

Prevalent on modern social media, both hashtags and emojis are text elements that function beyond plain text. While hashtags utilize free-formed strings and are highlighted by the platform, emojis are bonded by Unicode Standard and rendered by the platforms. Yet both are used to mark discussion topics, express sentiment, show identity, and highlight keywords. This paper analyzes and highlights the strong association between hashtags and emojis, not only in their usage frequency, but also in their semantics. We show that the association is strong enough for improving downstream tasks. To this end, we design a representation learning model that can learn emoji-based representations to improve hashtag prediction.