bluuebunny/crossref_metadata_2025 huggingface dataset:
Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media
Authors: Manikonda Lydia , Meduri Venkata Vamsikrishna , Kambhampati Subbarao
Published: November 2022

Understanding the usage of multiple Online Social Networks (OSNs) is of significant research interest as it helps in identifying the distinguishing traits of each social media platform that contribute to its continued existence. A comparison between two OSNs is particularly useful when it is done on the representative set of users holding active accounts on both the platforms. In this research, we collected a set of users holding accounts on both Twitter and Instagram. An extensive textual and visual analysis on the media content posted by these users reveals that these platforms are indeed perceived differently at a fundamental level with Instagram engaging more of the users' heart and Twitter capturing more of their mind. These differences get reflected in the linguistic, topical and visual aspects of the user posts.
In Wikipedia We Trust: A Case Study — Extended Abstract
Authors: Merchant Arpit , Shah Darshit , Singh Navjyoti
Published: November 2022

Wikipedia follows an open editing model that allows anyone to edit any entry. This has led to questions about the credibility and quality of information on it. Yet, it remains one of the most widely visited online encyclopedias. In this paper, we present a discussion of the various factors that influence the trust that users have on Wikipedia through a framework consisting of personal, social and functional elements. We further argue that digital signals and non-verbal cues also play an important role in determining trust on the various agents of the system.
To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter
Authors: Wang Yu , Luo Jiebo , Niemi Richard , Li Yuncheng
Published: November 2022

In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his ‘schlonged' remark.
Different News for Different Views: Political News-Sharing Communities on Social Media Through the UK General Election in 2015
Authors: Williams Matthew , Cioroianu Iulia , Williams Hywel
Published: November 2022

Media exposure is a central concept in understanding the dynamics of public opinion and political change. Traditional models of media exposure have been severely challenged by the shift to online news consumption and news-sharing on social media. Here we use network analysis and automated content analysis to examine the interaction between news media and social media around the UK General Election in 2015. We study a large corpus of UK newspaper articles and Twitter content, finding significant temporal correlations between newspaper topic coverage and the content discussed on Twitter. We also identify news-sharing communities around groups of news sources that are ideologically clustered. Analysis of topics covered within each group shows that different communities are exposed to different news content during the election. Our results confirm that ideological bias and selective news-sharing affect patterns of online media exposure in social media.
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Authors: Varol Onur , Ferrara Emilio , Davis Clayton , Menczer Filippo , Flammini Alessandro
Published: November 2022

Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9% and 15% of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.
Analysing Timelines of National Histories Across Wikipedia Editions: A Comparative Computational Approach
Authors: Samoilenko Anna , Lemmerich Florian , Weller Katrin , Zens Maria , Strohmaier Markus
Published: November 2022

Portrayals of history are never complete, and each description inherently exhibits a specific viewpoint and emphasis. In this paper, we aim to automatically identify such differences by computing timelines and detecting temporal focal points of written history across languages on Wikipedia. In particular, we study articles related to the history of all UN member states and compare them in 30 language editions. We develop a computational approach that allows to identify focal points quantitatively, and find that Wikipedia narratives about national histories (i) are skewed towards more recent events (recency bias) and (ii) are distributed unevenly across the continents with significant focus on the history of European countries (Eurocentric bias). We also establish that national historical timelines vary across language editions, although average interlingual consensus is rather high. We hope that this paper provides a starting point for a broader computational analysis of written history on Wikipedia and elsewhere.
The Substantial Interdependence of Wikipedia and Google: A Case Study on the Relationship Between Peer Production Communities and Information Technologies
Authors: McMahon Connor , Johnson Isaac , Hecht Brent
Published: November 2022

While Wikipedia is a subject of great interest in the computing literature, very little work has considered Wikipedia’s important relationships with other information technologies like search engines. In this paper, we report the results of two deception studies whose goal was to better understand the critical relationship between Wikipedia and Google. These studies silently removed Wikipedia content from Google search results and examined the effect of doing so on participants’ interactions with both websites. Our findings demonstrate and characterize an extensive interdependence between Wikipedia and Google. Google becomes a worse search engine for many queries when it cannot surface Wikipedia content (for example, click-through rates on results pages drop significantly) and the importance of Wikipedia content is likely greater than many improvements to search algorithms. Our results also highlight Google’s critical role in providing readership to Wikipedia. However, we also found evidence that this mutually beneficial relationship is in jeopardy: changes Google has made to its search results that involve directly surfacing Wikipedia content are significantly reducing traffic to Wikipedia. Overall, our findings argue that researchers and practitioners should give deeper consideration to the interdependence between peer production communities and the information technologies that use and surface their content.
Why Do Men Get More Attention? Exploring Factors Behind Success in An Online Design Community
Authors: Wachs Johannes , Hannak Aniko , Vörös András , Daróczy Bálint
Published: November 2022

Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (for example: recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users' position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.
Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan’s Politically Incorrect Forum and Its Effects on the Web
Authors: Hine Gabriel , Onaolapo Jeremiah , De Cristofaro Emiliano , Kourtellis Nicolas , Leontiadis Ilias , Samaras Riginos , Stringhini Gianluca , Blackburn Jeremy
Published: November 2022

The discussion-board site 4chan has been part of the Internet's dark underbelly since its inception, and recent political events have put it increasingly in the spotlight. In particular, /pol/, the “Politically Incorrect'” board, has been a central figure in the outlandish 2016 US election season, as it has often been linked to the alt-right movement and its rhetoric of hate and racism. However, 4chan remains relatively unstudied by the scientific community: little is known about its user base, the content it generates, and how it affects other parts of the Web. In this paper, we start addressing this gap by analyzing /pol/ along several axes, using a dataset of over 8M posts we collected over two and a half months. First, we perform a general characterization, showing that /pol/ users are well distributed around the world and that 4chan's unique features encourage fresh discussions. We also analyze content, finding, for instance, that YouTube links and hate speech are predominant on /pol/. Overall, our analysis not only provides the first measurement study of /pol/, but also insight into online harassment and hate speech trends in social media.
Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram
Authors: Deeb-Swihart Julia , Polack Christopher , Gilbert Eric , Essa Irfan
Published: November 2022

Carefully managing the presentation of self via technology is a core practice on all modern social media platforms. Recently, selfies have emerged as a new, pervasive genre of identity performance. In many ways unique, selfies bring us full-circle to Goffman — blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram — an online community comprising over 400M people. Applying computer vision and network analysis techniques to 2.5M selfies, we present a typology of emergent selfie categories which represent emphasized identity statements. To the best of our knowledge, this is the first large-scale, empirical research on selfies. We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life.
The Evolution and Consequences of Peer Producing Wikipedia's Rules
Authors: Keegan Brian , Fiesler Casey
Published: November 2022

Wikipedia's policies, guidelines, and other rules can be revised edited by anyone at any time, creating a rule environment that has changed substantially over its 15-year history. Wikipedia provides a remarkable benchmark for understanding the potential and pitfalls of self-governance in a knowledge commons and empirically informing theories of networked governance and institutional analysis. Using a corpus of 725,000 revisions made to 2,012 pages about rules and rule discussions since 2001, we explore the dynamics of English Wikipedia's rule-making and maintenance over time. Our analysis reveals a policy environment marked by on-going rule-making and deliberation across multiple regulatory levels more than a decade after its creation. This dynamism is however balanced by strong biases in the attention and length towards older rules coupled with a diminishing flexibility to change these rules, declining revision activity over time, and a strong shift toward deliberation.
Understanding Emoji Ambiguity in Context: The Role of Text in Emoji-Related Miscommunication
Authors: Miller Hannah , Kluver Daniel , Thebault-Spieker Jacob , Terveen Loren , Hecht Brent
Published: November 2022

Recent studies have found that people interpret emoji characters inconsistently, creating significant potential for miscommunication. However, this research examined emoji in isolation, without consideration of any surrounding text. Prior work has hypothesized that examining emoji in their natural textual contexts would substantially reduce potential for miscommunication. To investigate this hypothesis, we carried out a controlled study with 2,482 participants who interpreted emoji both in isolation and in multiple textual contexts. After comparing the variability of emoji interpretation in each condition, we found that our results do not support the hypothesis in prior work: when emoji are interpreted in textual contexts, the potential for miscommunication appears to be roughly the same. We also identify directions for future research to better understand the interplay between emoji and textual context.
Self-Disclosure and Channel Difference in Online Health Support Groups
Authors: Yang Diyi , Yao Zheng , Kraut Robert
Published: November 2022

Online health support groups are places for people to compare themselves with others and obtain informational and emotional support about their disease. To do so, they generally need to reveal private information about themselves and in many support sites, they can do this in public or private channels. However, we know little about how the publicness of the channels in health support groups influence the amount of self-disclosure people provide. Our work examines the extent members self-disclose in the private and public channels of an online cancer support group. We first built machine learning models to automatically identify the amount of positive and negative self-disclosure in messages exchanged in this community, with adequate validity r>0.70. In contrast to findings from non-health-related sites, our results show that people generally self-disclose more in the public channel than the private one and are especially likely to reveal their negative thoughts and feelings publicly. We discuss theoretical and practical implications of our work.
'Just the Facts:' Exploring the Relationship Between Emotional Language and Member Satisfaction in Enterprise Online Communities
Authors: Compton Ryan , Chen Jilin , Haber Eben , Badenes Hernan , Whittaker Steve
Published: November 2022

Online enterprise communities often have clear informational goals, for example, for experts to answer novices’ factual questions. However, emotional support is also critical for developing online social relationships. But what is the optimal balance between informational and emotional communication for such communities? To address this, we develop and validate a model to assess the relative prevalence of emotional versus factual communication. We next test hypotheses about how this prevalence relates to community member satisfaction in enterprise communities. Overall, factual, not emotional, posts predict perceived member satisfaction. This relationship also depends on community subtype. Although prior work argues that Communities of Practice (CoPs) rely on frequent emotional communication, instead we found CoPs showed less satisfaction when members focused on emotional concerns. We discuss implications for both community tool design and the practices of community leaders.
Automated Hate Speech Detection and the Problem of Offensive Language
Authors: Davidson Thomas , Warmsley Dana , Macy Michael , Weber Ingmar
Published: November 2022

A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.
25 Tweets to Know You: A New Model to Predict Personality with Social Media
Authors: Arnoux Pierre-Hadrien , Xu Anbang , Boyette Neil , Mahmud Jalal , Akkiraju Rama , Sinha Vibha
Published: November 2022

Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users’ written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state-of-the-art techniques with 8 times fewer data.
Querying Documents Annotated by Interconnected Entities
Authors: Sadah Shouq , Shahbazi Moloud , Hristidis Vagelis
Published: November 2022

In a large number of applications, from biomedical literature to social networks, there are collections of text documents that are annotated by interconnected entities, which are related to each other through association graphs. For example, social posts are related through the friendship graph of their authors, and PubMed articles area annotated by Mesh terms, which are related through ontological relationships. To effectively query such collections, in addition to the text content relevance of a document, the semantic distance between the entities of a document and the query must be taken into account. In this paper, we propose a novel query framework, which we refer as keyword querying on graph-annotated documents, and query techniques to answer such queries. Our methods automatically balance the impact of the graph entities and the text content in the ranking. Our qualitative evaluation on real dataset shows that our methods improve the ranking quality compared to baseline ranking systems.
Designing a Social Support System for College Adjustment and Social Support
Authors: Wohn Donghee Yvette , Ahmadi Mousa , Gong Leiping , Kulkarni Indraneel , Poojari Atisha
Published: November 2022

A main characteristic of current social systems is that they are designed to never-endingly captivate the attention of users, but are rarely designed to give support to those that need it. We describe the development process of Buddy, a social support mobile app for college students. We created a message stream algorithmically designed to give more attention to those with low psychological wellbeing, hidden feedback metrics to discourage competition or social comparison, and bots to deliver automatic one-click feedback in the form of virtual hugs. Across two field studies, we found that these small design changes can facilitate different types of behavior and community norms.
Estimating the Effect Of Exercising On Users’ Online Behavior
Authors: Mirlohi Falavarjani Seyed , Hosseini Hawre , Noorian Zeinab , Bagheri Ebrahim
Published: November 2022

This study aims to estimate the influence of offline activity on users’ online behavior, relying on a matching method to reduce the effect of confounding variables. We analyze activities of 850 users who are active on both Twitter and Foursquare social networks. Users’ offline activity is extracted from Foursquare posts and users’ online behavior is extracted from Twitter posts. Users’ interests, representing their online behavior, are extracted with regards to a set of topics in several subsequent time intervals. The shift of users’ interests across different time intervals is taken as a measure of user behavior change on the social network. On the other hand, we employ user check-ins at a gym or fitness center as a sign of exercise and consider it to be an offline activity. In order to find the effect of exercise on online behavior, we identify users who did not go to the gym for at least two months but did so at least nine times in the next three months. We show that shift in interest reduces significantly for users after they start exercising, which implies that the offline activity of exercising can influence how users’ interests are shaped and change on the social network over time.
News Media Coverage of Refugees in 2016: A GDELT Case Study
Authors: Boudemagh Emina , Moise Izabela
Published: November 2022

For many citizens, the main source of information about world events is the news media. Undeniably, media coverage of topics influences the perceived importance of these topics. Recent media discourse has given extensive attention to the topic of refugees and migration. In this context, we conduct a study on news media coverage of refugees for the time period of January 1st 2016 to December 31st 2016. We rely on the Global Database of Events, Language, and Tone (GDELT) project that monitors and analyses news articles around the world. In a first phase, we explore the global media conversation on refugees along two important dimensions: article quantity and sentiment. We identify and characterize events that generated an extensive media coverage and also extreme sentiment in the news reports. In a second phase, we refine the analysis by focusing on the news media coverage related to refugees in Europe. GDELT features allow us to identify the key countries and time evolution of the media coverage. Lastly, we determine the main actors linked to refugees in the news and study their interaction through a network analysis.
CrisisMMD: Multimodal Twitter Datasets from Natural Disasters
Authors: Alam Firoj , Ofli Ferda , Imran Muhammad
Published: November 2022

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multimedia content to report updates about injured or dead people, infrastructure damage, missing or found people, among other information types. Studies have revealed that this online information, if processed timely and effectively, is extremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset from natural disasters collected from Twitter. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.
BuzzFace: A News Veracity Dataset with Facebook User Commentary and Egos
Authors: Santia Giovanni , Williams Jake
Published: November 2022

Veracity assessment of news and social bot detection have become two of the most pressing issues for social media platforms, yet current gold-standard data are limited. This paper presents a leap forward in the development of a sizeable and feature rich gold-standard dataset. The dataset was built by using a collection of news items posted to Facebook by nine news outlets during September 2016, which were annotated for veracity by BuzzFeed. These articles were refined beyond binary annotation to the four categories: mostly true, mostly false, mixture of true and false, and no factual content. Our contribution integrates data on Facebook comments and reactions publicly available on the platform’s Graph API, and provides tailored tools for accessing news article web content. The features of the accessed articles include body text, images, links, Facebook plugin comments, Disqus plugin comments, and embedded tweets. Embedded tweets provide a potent possible avenue for expansion across social media platforms. Upon development, this utility yielded over 1.6 million text items, making it over 400 times larger than the current gold-standard. The resulting dataset—BuzzFace—is presently the most extensive created, and allows for more robust machine learning applications to news veracity assessment and social bot detection than ever before.
The Hoaxy Misinformation and Fact-Checking Diffusion Network
Authors: Hui Pik-Mai , Shao Chengcheng , Flammini Alessandro , Menczer Filippo , Ciampaglia Giovanni Luca
Published: November 2022

Massive amounts of misinformation flood social media like Twitter and Facebook. Digital misinformation includes articles about hoaxes, conspiracy theories, fake news, and other misleading claims. This content has been alleged to disrupt the public debate, leading to questions about its impact on the real world. A number of research questions have been formulated around the ways misinformation spreads, who are its main purveyors, and whether fact-checking efforts can be helpful at mitigating its diffusion. Here we release a large longitudinal dataset from Twitter, consisting of retweeted messages with links to misinformation and fact-checking articles. These data have been collected using Hoaxy (hoaxy.iuni.iu.edu), an open social media analytics platform whose goal is to provide a comprehensive picture of how digital misinformation spreads and competes with fact-checking efforts. The released dataset contains over 20 million retweets, spanning the period from May 2016 to the end of 2017. We provide basic statistics about the data and the associated diffusion networks.
Follow the “Mastodon”: Structure and Evolution of a Decentralized Online Social Network
Authors: Zignani Matteo , Gaito Sabrina , Rossi Gian Paolo
Published: November 2022

In this paper we present a dataset containing both the network of the "follow" relationships and its growth in terms of new connections and users, all which we obtained by mining the decentralized online social network named Mastodon. The dataset is combined with usage statistics and meta-data (geographical location and allowed topics) about the servers comprising the platform's architecture. These server are called instances. The paper also analyzes the overall structure of the Mastodon social network, focusing on its diversity w.r.t. other commercial microblogging platforms such as Twitter. Finally, we investigate how the instance-like paradigm influences the connections among the users. The newest and fastest-growing microblogging platform, Mastodon is set to become a valid alternative to established platforms like Twitter. The interest in Mastodon is mainly motivated as follows: a) the platform adopts an advertisement and recommendation-free business model; b) the decentralized architecture makes it possible to shift the control over user contents and data from the platform to the users; c) it adopts a community-like paradigm from both user and architecture viewpoints. In fact, Mastodon is composed of interconnected communities, placed on different servers; in addition, each single instance, with specific topics and languages, is independently owned and moderated. The released dataset paves the way to a number of research activities, which range from classic social network analysis to the modeling of social network dynamics and platform adoption in the early stage of the service. This data would also enable community detection validation since each instance hinges on specific topics and, lastly, the study of the interplay between the physical architecture of the platform and the social network it supports.
Automatically Conceptualizing Social Media Analytics Data via Personas
Authors: Jung Soon-gyo , Salminen Joni , An Jisun , Kwak Haewoon , Jansen Bernard
Published: November 2022

Social media analytics is insightful but can also be difficult to use within organizations. To address this, we present Automatic Persona Generation (APG), a system and methodology for quantitatively generating personas using large amounts of online social media data. The APG system is operational, deployed in a pilot version with several organizations in multiple industry verticals. APG uses a robust web and stable back-end database framework to process tens of millions of user interactions with thousands of online digital products on multiple social media platforms, including Facebook and YouTube. APG identifies both distinct and impactful audience segments for an organization to create persona profiles by enhancing the social media analytics data with pertinent features, such as names, photos, interests, etc. We demonstrate the architecture development, and main system features. APG provides value for organizations distributing content via online platforms and is unique in its approach to leveraging social media data for audience understanding. APG is online at https://persona.qcri.org.
Net Benefits: Digital Inequities in Social Capital, Privacy Preservation, and Digital Parenting Practices of U.S. Social Media Users
Authors: Redmiles Elissa
Published: November 2022

Social media adoption has been shown to exhibit digital inequality: sociodemographic background has been highly correlated with usage rates. As social media use has also been shown to correlate with important benefits, chiefly social capital, low adoption rates can mean a portion of the population is not receiving these benefits. In this work, we examine the equity of social media benefits among users, introducing and validating the existence of two new social media benefits: learning of privacy-preserving behaviors and parental engagement in children's social media use; and explore the equity of their distribution among social media users. To draw generalizable conclusions, we use a probabilistic telephone survey (n=3,000), weighted to represent the responses of the U.S. population within 2.7%. We encouragingly find no difference in adoption of social media based on education and find that lower-income users are more likely to use social media. Yet, we find an inequality in benefits: older and less educated social media users report lower degrees of nearly all examined benefits. Further, we find preliminary suggestion of an inherited digital inequality: parents who use social media, especially those who are more educated and higher paid, are more likely both to help their children set up privacy settings and teach them safe posting behaviors.
Perceptions of Censorship and Moderation Bias in Political Debate Forums
Authors: Shen Qinlan , Yoder Michael , Jo Yohan , Rose Carolyn
Published: November 2022

Moderators are believed to play a crucial role in ensuring the quality of discussion in online political debate forums. The line between moderation and illegitimate censorship, where certain views or individuals are unfairly suppressed, however, is often difficult to define. To better understand the relationship between moderation and censorship, we investigate whether users' perception of moderator bias is supported by how moderators act, using the Big Issues Debate (BID) group on Ravelry as our platform of study. We present our method for measuring bias while taking into account the posting behavior of a user, then apply our method to investigate whether moderators make decisions biased against viewpoints that they may have the incentive to suppress. We find evidence to suggest that while moderators may make decisions biased against individuals with unpopular viewpoints, the effect of this bias is small and often overblown by the users experiencing bias.We argue that the perception of bias by itself is an issue in online political discussions and suggest technological interventions to counteract the discrepancy between perceived and actual censorship in moderation.
“I’m Never Happy with What I Write”: Challenges and strategies of people with dyslexia on social media
Authors: Reynolds Lindsay , Wu Shamoei
Published: November 2022

This work studies the experiences, challenges, and strategies of people with dyslexia when using social media. We interviewed 11 people with dyslexia to understand their gen- eral experiences with reading and writing content on Facebook. The interview study findings highlight the challenges they face when writing content on Social Networking Sites (SNSs), and their strategies for mitigating these challenges. Following up on this, we surveyed 492 participants, among whom 67 self-reported as having dyslexia. The survey results confirm challenges with and strategies for writing unique to people with dyslexia, although they also suggest that better writing support on SNSs may be more broadly beneficial.
Ecosystem or Echo-System? Exploring Content Sharing across Alternative Media Domains
Authors: Starbird Kate , Arif Ahmer , Wilson Tom , Van Koevering Katherine , Yefimova Katya , Scarnecchia Daniel
Published: November 2022

This research examines the competing narratives about the role and function of Syria Civil Defence, a volunteer humanitarian organization popularly known as the White Helmets, working in war-torn Syria. Using a mixed-method approach based on seed data collected from Twitter, and then extending out to the websites cited in that data, we examine content sharing practices across distinct media domains that functioned to construct, shape, and propagate these narratives. We articulate a predominantly alternative media “echo-system” of websites that repeatedly share content about the White Helmets. Among other findings, our work reveals a small set of websites and authors generating content that is spread across diverse sites, drawing audiences from distinct communities into a shared narrative. This analysis also reveals the integration of government-funded media and geopolitical think tanks as source content for anti-White Helmets narratives. More broadly, the analysis demonstrates the role of alternative newswire-like services in providing content for alternative media websites. Though additional work is needed to understand these patterns over time and across topics, this paper provides insight into the dynamics of this multi-layered media ecosystem.
Using Simpson’s Paradox to Discover Interesting Patterns in Behavioral Data
Authors: Alipourfard Nazanin , Fennell Peter , Lerman Kristina
Published: November 2022

We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Given an outcome of interest and a set of covariates, the method follows three steps. First, it disaggregates data into subgroups, by conditioning on a particular covariate, so as minimize the variation of the outcome within the subgroups. Next, it models the outcome as a linear function of another covariate, both in the subgroups and in the aggregate data. Finally, it compares trendsto identify disaggregations that produce subgroups with different behaviors from the aggregate.We illustrate the method by applying it to three real-world behavioral datasets, including Q\&A site Stack Exchange and online learning platforms Khan Academy and Duolingo.
Characterizing Clickbaits on Instagram
Authors: Ha Yui , Kim Jeongmin , Won Donghyeon , Cha Meeyoung , Joo Jungseock
Published: November 2022

Clickbaits are routinely utilized by online publishers to attract the attention of people in competitive media markets. Clickbaits are increasingly used in visual-centric social media but remain a largely unexplored problem. Existing defense mechanisms rely on text-based features and are thus inapplicable to visual social media. By exploring the relationships between images and text, we develop a novel approach to characterize clickbaits on visual social media. Focusing on the topic of fashion, we first examined the prevalence of clickbaits on Instagram and surveyed their negative impacts on user experience through a focus group study (N=31). In a largescale analysis, we collected 450,000 Instagram posts and manually labeled 12,659 of these posts to determine what people consider to be clickbaits. By combining three different types of features (e.g., image, text, and meta features), our classifier was able detect clickbaits with an accuracy of 0.863. We performed an extensive feature analysis and showed that content-based features are much more important than meta features (e.g., number of followers) in clickbait classification. Our analysis indicates that approximately 11% of fashion-related Instagram posts are clickbait and that these posts are consistently accompanied by many hashtags, thus demonstrating that clickbait is prevalent in visual social media.
Unsupervised Model for Topic Viewpoint Discovery in Online Debates Leveraging Author Interactions
Authors: Trabelsi Amine , Zaiane Osmar
Published: November 2022

Online debate forums provide a valuable resource for textual discussions about controversial social and political issues. Discovering the viewpoints and their discourse or arguments from such resources is important for policy and decision makers. In order to detect the stance, most of the existing methods rely on expensively obtained human annotations and propose supervised solutions. In this work, we introduce a purely unsupervised Author Interaction Topic Viewpoint model (AITV) for viewpoint identification at the post and the discourse levels. The model favors "heterophily" over "homophily" when encoding the nature of the authors' interactions in online debates. It assumes that the difference in viewpoints breeds interactions, unlike similar studies based on social network analysis, which hypothesize that similar viewpoints encourage interactions. We evaluate the model's viewpoint identification and clustering accuracies at the author and post levels. Experiments are held on six corpora about four different controversial issues, extracted from two online debate forums. AITV's results show a better performance in terms of viewpoint identification at the post level than the state-of-the-art supervised methods in terms of stance prediction, even though it is unsupervised. It also outperforms a recently proposed topic model for viewpoint discovery in social networks and achieves close results to a weakly guided unsupervised method in terms of author level viewpoint identification. Our results highlight the importance of encoding "heterophily" for purely unsupervised viewpoint identification in the context of online debates. We also carry out a brief qualitative evaluation of the discourse modeling in terms of Topic-Viewpoint word clusters. AITV shows encouraging results suggesting an accurate discovery of the viewpoints and topics' discourses.
#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate
Authors: Rizoiu Marian-Andrei , Graham Timothy , Zhang Rui , Zhang Yifei , Ackland Robert , Xie Lexing
Published: November 2022

Serious concerns have been raised about the role of `socialbots' in manipulating public opinion and influencing the outcome of elections by retweeting partisan content to increase its reach. Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets during the 1st U.S. presidential debate in 2016 and we analyze its 1.5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness. First, we define a measure of user influence based on the user's active contributions to information diffusions, i.e. their tweets and retweets. Given that Twitter does not expose the retweet structure -- it associates all retweets with the original tweet -- we model the latent diffusion structure using only tweet time and user features, and we implement a scalable novel approach to estimate influence over all possible unfoldings. Next, we use partisan hashtag analysis to quantify user political polarization and engagement. Finally, we use the BotOrNot API to measure user botness (the likelihood of being a bot). We build a two-dimensional "polarization map" that allows for a nuanced analysis of the interplay between botness, partisanship and influence. We find that not only are socialbots more active on Twitter -- starting more retweet cascades and retweeting more -- but they are 2.5 times more influential than humans, and more politically engaged. Moreover, pro-Republican bots are both more influential and more politically engaged than their pro-Democrat counterparts. However we caution against blanket statements that software designed to appear human dominates politics-related activity on Twitter. Firstly, it is known that accounts controlled by teams of humans (e.g. organizational accounts) are often identified as bots. Secondly, we find that many highly influential Twitter users are in fact pro-Democrat and that most pro-Republican users are mid-influential and likely to be human (low botness).
Reddit Rules! Characterizing an Ecosystem of Governance
Authors: Fiesler Casey , Jiang Jialun , McCann Joshua , Frye Kyle , Brubaker Jed
Published: November 2022

The social sharing and news aggregation site Reddit provides a unique example of an ecosystem of community-created rules. Not only do individual subreddits create and enforce their own regulations, but site-wide guidelines and norms may also influence behavior. This paper reports on a mixed-methods study of 100,000 subreddits and their rules. Our findings characterize the types of rules across Reddit, the frequency of rules at scale, and patterns of rules based on subreddit characteristics. We find that rules appear to be context-dependent for individual subreddits but also share common characteristics across the site. Taken together, our findings provide a rich description of this ecosystem of rules, motivating further inquiry into underlying mechanisms for rule formation and enforcement in online communities.
Network Structure, Efficiency, and Performance in WikiProjects
Authors: Platt Edward , Romero Daniel
Published: November 2022

The internet has enabled collaborations at a scale never before possible, but the best practices for organizing such large collaborations are still not clear. Wikipedia is a visible and successful example of such a collaboration which might offer insight into what makes large-scale, decentralized collaborations successful. We analyze the relationship between the structural properties of WikiProject coeditor networks and the performance and efficiency of those projects. We confirm the existence of an overall performance-efficiency trade-off, while observing that some projects are higher than others in both performance and efficiency, suggesting the existence factors correlating positively with both. Namely, we find an association between low-degree coeditor networks and both high performance and high efficiency. We also confirm results seen in previous numerical and small-scale lab studies: higher performance with less skewed node distributions, and higher performance with shorter path lengths. We use agent-based models to explore possible mechanisms for degree-dependent performance and efficiency. We present a novel local-majority learning strategy designed to satisfy properties of real-world collaborations. The local-majority strategy as well as a localized conformity-based strategy both show degree-dependent performance and efficiency, but in opposite directions, suggesting that these factors depend on both network structure and learning strategy. Our results suggest possible benefits to decentralized collaborations made of smaller, more tightly-knit teams, and that these benefits may be modulated by the particular learning strategies in use.
Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media
Authors: ElSherief Mai , Kulkarni Vivek , Nguyen Dana , Yang Wang William , Belding Elizabeth
Published: November 2022

While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either directed towards a specific person or entity, or generalized towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications, but also its detection.
Time Reveals All Wounds: Modeling Temporal Characteristics of Cyberbullying
Authors: Soni Devin , Singh Vivek
Published: November 2022

Cyberbullying is a critical socio-technical problem that seriously limits the use of online interaction spaces by different individuals. Emerging literature identifies cyberbullying as a continuous temporal phenomena rather than one-off incidents. However, as of yet, little computational work has been done to model the temporal dynamics of cyberbullying in online sessions. In this work, we model the temporal dynamics of commenting behavior as point processes and validate it over a crowd-labeled cyberbullying data-set of Instagram media sessions. We define several temporal features to model the distinguishing characteristics between cyberbullying and regular media sessions. We find that our approach is successfully able to identify significant differences between cyberbullying and regular media sessions, and provide a performance increase in cyberbullying detection. This paves the way for more nuanced work on the use of temporal modeling to detect and mitigate the occurrence of cyberbullying.