bluuebunny/crossref_metadata_2025 huggingface dataset:
Better When It Was Smaller? Community Content and Behavior After Massive Growth
Authors: Lin Zhiyuan , Salehi Niloufar , Yao Bowen , Chen Yiqi , Bernstein Michael
Published: November 2022

Online communities have a love-hate relationship with membership growth: new members bring fresh perspectives, but old-timers worry that growth interrupts the community’s social dynamic and lowers content quality. To arbitrate these two theories, we analyze over 45 million comments from 10 Reddit subcommunities following an exogenous shock when each subcommunity was added to the default set for all Reddit users. Capitalizing on these natural experiments, we test for changes to the content vote patterns, linguistic patterns, and community network patterns before and after being defaulted. Results support a narrative that the communities remain high-quality and similar to their previous selves even post-growth. There is a temporary dip in upvote scores right after the communities were defaulted, but the communities quickly recover to pre-default or even higher levels. Likewise, complaints about low-quality posts do not rise in frequency after getting defaulted. Strong moderation also helps keep upvotes common and complaint levels low. Communities’ language use does not become more like the rest of Reddit after getting defaulted. However, growth does have some impact on attention: community members cluster their activity around a smaller proportion of posts after the community is defaulted.
Gendered Conversation in a Social Game-Streaming Platform
Authors: Nakandala Supun , Ciampaglia Giovanni , Su Norman , Ahn Yong-Yeol
Published: November 2022

Online social media and games are increasingly replacing offline social activities. Social media is now an indispensable mode of communication; online gaming is not only a genuine social activity but also a popular spectator sport. Although online interaction shrinks social and geographical barriers, it is argued that social disparities, such as gender inequality, persists. For instance, online gaming communities have been criticized for objectifying women, which is a pressing question as gaming evolves into a social platform. However, few large-scale, systematic studies of gender inequality and objectification in social gaming platforms exist. Here we analyze more than one billion chat messages from Twitch, a social game-streaming platform, to study how the gender of streamers is associated with the nature of conversation. We find that female streamers receive significantly more objectifying comments while male streamers receive more game- related comments. This difference is more pronounced for popular streamers. We also show that the viewers’ choice of channels is also strongly gendered. Our findings suggest that gendered conversation and objectification is prevalent, and most users produce strongly gendered messages.
How User Condition Affects Community Dynamics in a Forum on Autism
Authors: Samory Mattia , Pizzi Cinzia , Peserico Enoch
Published: November 2022

Individuals on the autistic spectrum and their families look for peer support in specialized online forums. These venues also attract advocates and people interested in autism, providing valuable first-hand experience. Previous research focused on quantifying how autistic individuals interact in online communities, and if they benefit from computer-mediated communication. However, there is limited quantitative understanding of the different roles that diagnosed individuals, family members, and neurotypical users play in these communities. This paper analyses Wrong Planet, a large online autism forum where users may openly state their condition in their profile. The sentiment, discourse, and network characteristics of content users contribute (and respond to) differs by user condition. Also, interaction patterns between users with different conditions shed light on the dynamics of the forum community. Content exchanges between family members and neurotypical users are emotionally charged and supportive; however, this support is less present in exchanges with diagnosed members. This paper gives insights on what factors facilitate participation of diagnosed users.
Spatio-Temporal Analysis of Reverted Wikipedia Edits
Authors: Kiesel Johannes , Potthast Martin , Hagen Matthias , Stein Benno
Published: November 2022

Little is known about what causes anti-social behavior online. The paper at hand analyzes vandalism and damage in Wikipedia with regard to the time it is conducted and the country it originates from. First, we identify vandalism and damaging edits via ex post facto evidence by mining Wikipedia’s revert graph. Second, we geolocate the cohort of edits from anonymous Wikipedia editors using their associated IP addresses and edit times, showing the feasibility of reliable historic geolocation with respect to country and time zone, even under limited geolocation data. Third, we conduct the first spatio-temporal analysis of vandalism on Wikipedia. Our analysis reveals significant differences for vandalism activities during the day, and for different days of the week, seasons, countries of origin, as well as Wikipedia’s languages. For the analyzed countries, the ratio is typically highest at non-summer workday mornings, with additional peaks after break times. We hence assume that Wikipedia vandalism is linked to labor, perhaps serving as relief from stress or boredom, whereas cultural differences have a large effect. Our results open up avenues for new research on collaborative writing at scale, and advanced technologies to identify and handle antisocial behavior in online communities.
Untangling Emoji Popularity Through Semantic Embeddings
Authors: Ai Wei , Lu Xuan , Liu Xuanzhe , Wang Ning , Huang Gang , Mei Qiaozhu
Published: November 2022

Emojis have gone viral on the Internet across platforms and devices. Interwoven into our daily communications, they have become a ubiquitous new language. However, little has been done to analyze the usage of emojis at scale and in depth. Why do some emojis become especially popular while others don't? How are people using them among the words? In this work, we take the initiative to study the collective usage and behavior of emojis, and specifically, how emojis interact with their context. We base our analysis on a very large corpus collected from a popular emoji keyboard, which contains a full month of inputs from millions of users. Our analysis is empowered by a state-of-the-art machine learning tool that computes the embeddings of emojis and words in a semantic space. We find that emojis with clear semantic meanings are more likely to be adopted. While entity-related emojis are more likely to be used as alternatives to words, sentiment-related emojis often play a complementary role in a message. Overall, emojis are significantly more prevalent in a sentimental context.
How Editorial, Temporal and Social Biases Affect Online Food Popularity and Appreciation
Authors: Rokicki Markus , Herder Eelco , Trattner Christoph
Published: November 2022

Measures of popularity and appreciation provide useful information for search and recommendation systems that facilitate access to growing amounts of user-generated content, such as online recipes. However, user rating and commenting behavior is not only influenced by the content itself, but also due to additional effects introduced by biases and contexts. Based on a large dataset of more than 400,000 online recipes, we investigate the nature of such biases and the impact on the number of ratings, comments and views. Our analysis shows that user feedback is significantly influenced by the recipe author's prior reputation, by friendship relations, similarity between user profiles, temporal and seasonal effects, and editorial choices. Furthermore, a regression analysis shows that for the number of ratings received by recipes in particular, an excellent fit can be obtained based on a combination of these biases. These results imply that the popularity of an item is heavily influenced by random bias introduced by various external factors that impact rating and commenting behavior in a relatively short time span after publication.
Dynamics of Content Quality in Collaborative Knowledge Production
Authors: Ferrara Emilio , Alipoufard Nazanin , Burghardt Keith , Gopal Chiranth , Lerman Kristina
Published: November 2022

We explore the dynamics of user performance in collaborative knowledge production by studying the quality of answers to questions posted on Stack Exchange. We propose four indicators of answer quality: answer length, the number of code lines and hyperlinks to external web content it contains, and whether it is accepted by the asker as the most helpful answer to the question. Analyzing millions of answers posted over the period from 2008 to 2014, we uncover regular short-term and long-term changes in quality. In the short-term, quality deteriorates over the course of a single session, with each successive answer becoming shorter, with fewer code lines and links, and less likely to be accepted. In contrast, performance improves over the long-term, with more experienced users producing higher quality answers. These trends are not a consequence of data heterogeneity, but rather have a behavioral origin. Our findings highlight the complex interplay between short-term deterioration in performance, potentially due to mental fatigue or attention depletion, and long-term performance improvement due to learning and skill acquisition, and its impact on the quality of user-generated content.
Understanding How People Attend to and Engage with Foreign Language Posts in Multilingual Newsfeeds
Authors: Lim Hajin , Fussell Susan
Published: November 2022

We explore how language affects people’s attention to and engagement with social media posts in an eye-tracking experiment in which participants viewed a mock newsfeed containing English and foreign language posts, half with and half without images. Participants spent less time looking at foreign language posts than English posts, and more time looking at posts with images than those without images. Participants reported being more likely to like or comment on posts in English and posts with images. We suggest some new design ideas for supporting people’s interaction on multilingual social media sites.
Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network
Authors: Stewart Ian , Arendt Dustin , Bell Eric , Volkova Svitlana
Published: November 2022

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word's contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus — VKontakte collected during the Russia-Ukraine crisis in 2014 — 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.
The Role of Optimal Distinctiveness and Homophily in Online Dating
Authors: Maldeniya Danaja , Varghese Arun , Stuart Toby , Romero Daniel
Published: November 2022

Users of online dating sites compete for attention from potential matches. Member profiles provide an opportunity for candidates to present information about themselves that their counterparts use to assess compatibility and desirability. In this paper, we explore how text-based similarities among users of a dating site impact their success in attracting attention. The principle of homophily predicts that to be successful, a user should be perceived as similar to the person they would prefer to date. Conversely, theories of distinctiveness suggest that standing out from the crowd should be beneficial. Using profiles, we explore how the text similarity between a user, the opposite-sex member they are targeting, and their same-sex competitors impacts the likelihood that a sender of a message receives a response conditional on initiating contact. We find that the probability of receiving a response is maximized when the user has high text similarity to the person they message, but low text similarity to the competitors that are also seeking the same individual’s attention. This suggests a balance between homophily and distinctiveness theory.
How to Manipulate Social Media: Analyzing Political Astroturfing Using Ground Truth Data from South Korea
Authors: Keller Franziska , Schoch David , Stier Sebastian , Yang JungHwan
Published: November 2022

This project investigates political astroturfing, that is, hidden propaganda by powerful political actors aimed at mimicking grassroots activity, on social media. We focus on Twitter accounts used by the South Korean secret service to influence the 2012 presidential elections in favor of the eventual winner, Park Geun-hye. Two independent cluster analyses based on activity patterns of the Twitter accounts and textual features of tweets reliably reveal that there are three groups of NIS accounts, including one group that engages mostly in retweeting, and another group focused on posting news articles with a link. We show that these groups reflect different strategic agendas and correspond to several secret service agents identified in the court documents. We argue that these patterns of coordinated tweeting are consistent with predictions derived from principal-agent theory, and should therefore appear in other astroturfing campaigns as well.
Towards Measuring Fine-Grained Diversity Using Social Media Photographs
Authors: Singh Vivek , Hegde Saket , Atrey Akanksha
Published: November 2022

Diversity is an important socio-economic construct that influences multiple aspects of human lives from the prosperity of a city to corporate earnings and from criminal justice to health and social engagement. Large, heavily populated urban areas can be highly diverse at the city or even neighborhood level, but we know very little about how much people from diverse demographics (such as age and race) interact with each other. Previous work has shown that photos are important in social relationships. The growing presence of photos online and on social media, therefore presents a unique opportunity to study diversity in interactions. In this paper, we explore a novel approach to measure p-diversity, that is, a personal, photo-level diversity metric computed using social media data. Specifically, we focus on Instagram photos of multiple people interacting, and employ automatic methods for race, age, and gender estimation to quantify mixing in such photos. We compare and contrast this new measure of diversity with traditional (that is, census-based) metrics using a dataset for New York City. Results obtained motivate the use of social media photos to complement census data to develop cheaper, faster, mechanisms for studying diversity and applying them in social, economic, political, and urban planning contexts.
How Fast Will You Get a Response? Predicting Interval Time for Reciprocal Link Creation
Authors: Dave Vachik , Al Hasan Mohammad , Reddy Chandan
Published: November 2022

In the recent years, reciprocal link prediction has received some attention from the data mining and social network analysis researchers, who solved this problem as a binary classification task. However, it is also important to predict the interval time for the creation of reciprocal link. This is a challenging problem for two reasons: First, the lack of effective features, because well-known link prediction features are designed for undirected networks and for the binary classification task, hence they do not work well for the interval time prediction; Second, the presence of censored data instances makes the traditional supervised regression methods unsuitable for solving this problem. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem into survival analysis framework and show through extensive experiments on real-world datasets that, survival analysis methods perform better than traditional regression, neural network based model and support vector regression (SVR).
Characteristics of On-Time and Late Reward Delivery Projects
Authors: Tran Thanh , Lee Kyumin
Published: November 2022

The crowdfunding market size has increased exponentially, reaching tens of billions of dollars and showing the popularity of crowdfunding. However, according to Kickstarter, 35% backers did not receive rewards on time. To maintain the trust between creators and backers, and sustain the crowdfunding business growth, it is crucial to understand how on-time and late reward delivery projects are different. In this paper, we analyze characteristics of on-time and late reward delivery projects, especially, focusing on project descriptions, creator profiles, and activeness and linguistic patterns of creators and backers. Our analysis reveals that the less complicated a project is and more actively a creator responds to backers, the higher an on-time reward delivery probability is. It shows there are significant differences between on-time and late reward delivery projects.
Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape
Authors: Horne Benjamin , Khedr Sara , Adali Sibel
Published: November 2022

The complexity and diversity of today's media landscape provides many challenges for researchers studying news producers.These producers use many different strategies to get theirmessage believed by readers through thewriting styles they employ, by repetition across different media sources with or without attribution, as well as other mechanismsthat are yet to be studied deeply. To better facilitate systematic studies in this area, we present a large political news data set, containing over 136K news articles, from 92 news sources, collected over 7 months of 2017. These news sources are carefully chosen to include well-established and mainstream sources, maliciously fake sources, satire sources, and hyper-partisan political blogs. In addition to each article we compute 130 content-based and social media engagement features drawn from a wide range of literature on political bias, persuasion, and misinformation. With the release of the data set, we also provide the source code for feature computation. In this paper, we discuss the first release of the data set and demonstrate 4 use cases of the data and features: news characterization, engagement characterization, news attribution and content copying, and discovering news narratives.
DeepCredit: Exploiting User Cickstream for Loan Risk Prediction in P2P Lending
Authors: Yang Zhi , Zhang Yusi , Guo Binghui , Y.Zhao Ben , Dai Yafei
Published: November 2022

Peer-to-peer (P2P) lending or crowdlending,is a recent innovation allows a group of individual or institutional lenders tolend funds to individuals or businesses in return for interest payment on top of capital repayments.The rapid growth of P2P lending marketplaces has heightenedthe need to develop a support system to help lenders make sound lending decisions.But realizing such system is challenging in the absence of formal credit data used by the banking sector.In this paper, we attempt toexplore the possible connections between user credit riskand how users behave in the lendingsites.Wepresent the first analysis of user detailed clickstream datafrom a large P2P lending provider.Our analysisreveals that the users' sequences of repayment histories and financial activities in the lending site,have significant predictive value for their future loan repayments.In the light of this, we propose a deep architecture named DeepCredit, toautomatically acquire theknowledge of credit risk from the sequences of activities that users conduct on the site.Experiments on our large-scale real-world datasetshow that our model generates a high accuracy in predicting both loan delinquency and default, and significantly outperformsa numberof baselines and competitive alternatives.
Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping
Authors: Pavllo Dario , Piccardi Tiziano , West Robert
Published: November 2022

We propose Quootstrap, a method for extracting quotations, as well as the names of the speakers who uttered them, from large news corpora. Whereas prior work has addressed this problem primarily with supervised machine learning, our approach follows a fully unsupervised bootstrapping paradigm. It leverages the redundancy present in large news corpora, more precisely, the fact that the same quotation often appears across multiple news articles in slightly different contexts. Starting from a few seed patterns, such as ["Q", said S.], our method extracts a set of quotation-speaker pairs (Q, S), which are in turn used for discovering new patterns expressing the same quotations; the process is then repeated with the larger pattern set. Our algorithm is highly scalable, which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus. Validating our results against a crowdsourced ground truth, we obtain 90% precision at 40% recall using a single seed pattern, with significantly higher recall values for more frequently reported (and thus likely more interesting) quotations. Finally, we showcase the usefulness of our algorithm's output for computational social science by analyzing the sentiment expressed in our extracted quotations.
Detecting Misflagged Duplicate Questions in Community Question-Answering Archives
Authors: Hoogeveen Doris , Bennett Andrew , Li Yitong , Verspoor Karin , Baldwin Timothy
Published: November 2022

In this paper we introduce the task of misflagged duplicate question detection for question pairs in community question-answer (cQA) archives and compare it to the more standard task of detecting valid duplicate questions. A misflagged duplicate is a question that has been erroneously hand-flagged by the community as a duplicate of an archived one, where the two questions are not actually the same. We find that form is flagged duplicate detection, meta data features that capture user authority, question quality, and relational data between questions, outperform pure text-based methods, while for regular duplicate detection a combination of meta data features and semantic features gives the best results. We show that misflagged duplicate questions are even more challenging to model than regular duplicate question detection, but that good results can still be obtained.
Songs of a Future Past — An Experimental Study of Online Persuaders
Authors: Antenore Marzia , Panconesi Alessandro , Terolli Erisa
Published: November 2022

In this paper, we present the results of an extensive experimental study on users decisions inside an online setting. In the experiment, participants purchase songs using real money while having enough time to explore them at leisure before buying. In such a set up, surpisingly, common social influence signals such as star ratings, download counts and recommendations had no influence. However, as soon as the exploration was made slightly more cumbersome market inequality appeared. This is an indication that it is decision-making shortcuts, rather then social influence, to trigger distorting market effects.
When Online Harassment Is Perceived as Justified
Authors: Blackwell Lindsay , Chen Tianying , Schoenebeck Sarita , Lampe Cliff
Published: November 2022

Most models of criminal justice seek to identify and punish offenders. However, these models break down in online environments, where offenders can hide behind anonymity and lagging legal systems. As a result, people turn to their own moral codes to sanction perceived offenses. Unfortunately, this vigilante justice is motivated by retribution, often resulting in personal attacks, public shaming, and doxing— behaviors known as online harassment. We conducted two online experiments (n=160; n=432) to test the relationship between retribution and the perception of online harassment as appropriate, justified, and deserved. Study 1 tested attitudes about online harassment when directed toward a woman who has stolen from an elderly couple. Study 2 tested the effects of social conformity and bystander intervention. We find that people believe online harassment is more deserved and more justified—but not more appropriate—when the target has committed some offense. Promisingly, we find that exposure to a bystander intervention reduces this perception. We discuss alternative approaches and designs for responding to harassment online.
Peer to Peer Hate: Hate Speech Instigators and Their Targets
Authors: ElSherief Mai , Nilizadeh Shirin , Nguyen Dana , Vigna Giovanni , Belding Elizabeth
Published: November 2022

While social media has become an empowering agent to individual voices and freedom of expression, it also facilitates anti-social behaviors including online harassment, cyberbullying, and hate speech. In this paper, we present the first comparative study of hate speech instigators and target users on Twitter. Through a multi-step classification process, we curate a comprehensive hate speech dataset capturing various types of hate. We study the distinctive characteristics of hate instigators and targets in terms of their profile self-presentation, activities, and online visibility. We find that hate instigators target more popular and high profile Twitter users, and that participating in hate speech can result in greater online visibility. We conduct a personality analysis of hate instigators and targets and show that both groups have eccentric personality facets that differ from the general Twitter population. Our results advance the state of the art of understanding online hate speech engagement.
Characterizing and Detecting Hateful Users on Twitter
Authors: Ribeiro Manoel , Calais Pedro , Santos Yuri , Almeida Virgílio , Meira Jr. Wagner
Published: November 2022

Current approaches to characterize and detect hate speech focus on content posted in Online Social Networks (OSNs). They face shortcomings to get the full picture of hate speech due to its subjectivity and the noisiness of OSN text. This work partially addresses these issues by shifting the focus towards users. We obtain a sample of Twitter's retweet graph with 100,386 users and annotate 4,972 as hateful or normal, and also find 668 users suspended after 4 months. Our analysis shows that hateful/suspended users differ from normal/active ones in terms of their activity patterns, word usage and network structure. Exploiting Twitter's network of connections, we find that a node embedding algorithm outperforms content-based approaches for detecting both hateful and suspended users. Overall, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.
Assessing the Accuracy of Four Popular Face Recognition Tools for Inferring Gender, Age, and Race
Authors: Jung Soon-gyo , An Jisun , Kwak Haewoon , Salminen Joni , Jansen Bernard
Published: November 2022

In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as all four tools performed poorly. The findings of our quantitative evaluation are helpful for future computational social science research using these tools, as their accuracy needs to be taken into account when applied to classifying individuals on social media and other contexts. Triangulation and manual verification are suggested for researchers employing these tools.
Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks
Authors: Junuthula Ruthwik , Xu Kevin , Devabhaktuni Vijay
Published: November 2022

On-line social networks (OSNs) often contain many different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily studied. There has also been prior work on link prediction in interaction networks,independent of friendship networks. In this paper, we study the predictive power of combining friendship and interaction networks. We hypothesize that, by leveraging friendship networks, we can improve the accuracy of link prediction in interaction networks. We augment several interaction link prediction algorithms to incorporate friendships and predicted friendships. From experiments on Facebook data, we find that incorporating friendships into interaction link prediction algorithms results in higher accuracy, but incorporating predicted friendships does not when compared to incorporating current friendships.
On Group Popularity Prediction in Event-Based Social Networks
Authors: Li Guangyu , Liu Yong , Ribeiro Bruno , Ding Hao
Published: November 2022

Although previous work has shown that member and structural features are important to the future popularity of groups in EBSN, it is not yet clear how different member roles and the interplay between them contribute to group popularity. In this paper, we study a real-world dataset from Meetup --- a popular EBSN platform --- and propose a deep neural network based method to predict the popularity of new Meetup groups. Our method uses group-level features specific to event-based social networks, such as time and location of events in a group, as well as the structural features internal to a group, such as the inferred member roles in a group and social substructures among members. Empirically, our approach reduces the RMSE of the popularity prediction (measured in RSVPs) of a group's future events by up to 12%, against the state-of-the-art baselines.
Emotional Influence Prediction of News Posts
Authors: Giachanou Anastasia , Rosso Paolo , Mele Ida , Crestani Fabio
Published: November 2022

Nowadays, on-line news agents post news articles on social media platforms with the aim to spread information as well as to attract more users and understand their reactions and opinions. Predicting the emotional influence of news on users is very important not only for news agents but also for users, who can filter out news articles based on the reactions they trigger. In this paper, we focus on the problem of emotional influence prediction of a news post on users before publication. For the prediction, we explore a range of textual and semantic features derived from the content of the posts. Our results show that terms is the most important feature and that features extracted from news posts' content allow to effectively predict the amount of emotional reactions triggered by a news post.
Real-World Witness Detection in Social Media via Hybrid Crowdsensing
Authors: Cresci Stefano , Cimino Andrea , Avvenuti Marco , Tesconi Maurizio , Dell'Orletta Felice
Published: November 2022

The task of witness detection in social media is crucial for many practical applications, including rumor debunking, emergency management, and public opinion mining. Yet to date, it has been approached in an approximated way. We propose a method for addressing witness detection in a strict and realistic fashion. By employing hybrid crowdsensing over Twitter, we contact real-life witnesses and use their reactions to build a strong ground-truth, thus avoiding a manual, subjective annotation of the dataset. Using this dataset, we develop a witness detection system based on a machine learning classifier using a wide set of linguistic features and metadata associated with the tweets.
Extracting Inter-Community Conflicts in Reddit
Authors: Datta Srayan , Adar Eytan
Published: November 2022

Anti-social behaviors in social media can happen both at user and community levels. While a great deal of attention is on the individual as an ‘aggressor,’ the banning of entire Reddit subcommunities (i.e., subreddits) demonstrates that this is a multi-layer concern. Existing research on inter-community conflict has largely focused on specific subcommunities or ideological opponents. However, antagonistic behaviors may be more pervasive and integrate into the broader network. In this work, we study the landscape of conflicts among subreddits by deriving higher-level (community) behaviors from the way individuals are sanctioned and rewarded. By constructing a conflict network, we characterize different patterns in subreddit-to-subreddit conflicts as well as communities of ‘co-targeted’ subreddits .The dynamics of these interactions also reveals a shift in conflict focus over time.
Studying Cultural Differences in Emoji Usage across the East and the West
Authors: Chandra Guntuku Sharath , Li Mingyang , Tay Louis , Ungar Lyle H.
Published: November 2022

Global acceptance of Emojis suggests a cross-cultural, normative use of Emojis. Meanwhile, nuances in Emoji use across cultures may also exist due to linguistic differences in expressing emotions and diversity in conceptualizing topics. Indeed, literature in cross-cultural psychology has found both normative and culture-specific ways in which emotions are expressed. In this paper, using social media, we compare the Emoji usage based on frequency, context, and topic associations across countries in the East (China and Japan) and the West (United States, United Kingdom, and Canada). Across the East and the West, our study examines a) similarities and differences on the usage of different categories of Emojis such as People, Food & Drink, Travel & Places etc., b) potential mapping of Emoji use differences with previously identified cultural differences in users’ expression about diverse concepts such as death, money emotions and family, and c) relative correspondence of validated psycho-linguistic categories with Ekman’s emotions. The analysis of Emoji use in the East and the West reveals recognizable normative and culture specific patterns. This research reveals the ways in which Emojis can be used for cross-cultural communication.
Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media
Authors: Horne Benjamin D. , Nørregaard Jeppe , Adalı Sibel
Published: November 2022

In this paper, we analyze content sharing between news sources in the alternative and mainstream media using a dataset of 713K articles and 194 sources. We find that content sharing happens in tightly formed communities, and these communities represent relatively homogeneous portions of the media landscape. Through a mix-method analysis, we find several primary content sharing behaviors. First, we find that the vast majority of shared articles are only shared with similar news sources (i.e. same community). Second, we find that despite these echo-chambers of sharing, specific sources, such as The Drudge Report, mix content from both mainstream and conspiracy communities. Third, we show that while these differing communities do not always share news articles, they do report on the same events, but often with competing and counter-narratives. Overall, we find that the news is homogeneous within communities and diverse in between, creating different spirals of sameness.
The Willingness of Crowds: Cohort Disclosure Preferences for Personally Identifying Information
Authors: Marmion Vincent , Millard David E , Gerding Enrico H , Stevenage Sarah V
Published: November 2022

Exploiting personal identifying information (PII) is critical for secure access to digital and web-based systems, it is also a significant element of the online social media business model. However, how this exploitation relates to users’ valuation of their PII is poorly understood as an individual’s willingness to disclose items of PII in different situations is unknown. For instance, an individual may delight in accessing their smartphone using facial recognition, yet they may hesitate when accessing banking services or vice versa. Moreover, the actual cost of disclosure gets obfuscated within dense and lengthy policies in a manner designed to exploit additional data. Thus, an individual may not understand that systems such as facial recognition can be a gateway to infer further PII.

Even with respectful intentions, identity-dependent technologies face a myriad of challenges to transparently balance users’ sensitivities with their own need for high veracity PII. In a novel application of the ELO ranking algorithm, we detail a frugal and scalable method of capturing and combining some of these sensitivities. The design involves a set of 33 items of PII, and a cohort (N = 115) divided into three contexts: expression (35), transaction (40) and submission (40). The results indicate that while individuals may have many differences, as a cohort the personal utility of PII still collates and forms distinct clusters of PII that relate within and across contexts. This result means that technologies that treat PII as one amorphous group, and those transferring PII across contexts, risk failing to adhere to the sensitivities of the user. However, by working with these cohort-based clusters in mind, it is plausible that system designers and policymakers may better appropriate system needs with the wants of the individual.
On the Migration of Researchers across Scientific Domains
Authors: Pramanik Soumajit , Gora Surya Teja , Sundaram Ravi , Ganguly Niloy , Mitra Bivas
Published: November 2022

Shift of research interest is an inherent part of a scientific career. Accordingly, researchers tend to migrate from one field of research to another. In this paper, we systematically study the publication records of more than 200,000 researchers working in Computer Science domain and propose a simple algorithm to identify the migrating researchers. Just like human migration, this kind of research field migration is driven by various latent factors. Inspired by the classical theories of human migration, here we present a theoretical framework which models the decision-making processes of the individual migrating researchers and helps us to derive those latent factors. We further investigate the impact of these key factors in regulating a researcher’s decision to migrate to a specific research field and observe the effect of such migration on her career. We note that in general publication quantity & quality, collaborator profile, fields’ popularity contribute to a researcher’s decision of field-migration. Importantly, effects of migration are not only limited to just one individual’s career but also extend to the prospect of the research fields associated with it. Despite few initial capacity issues, field migration in general contribute in flourishing the research field people migrate into, in long term.
Social B(eye)as: Human and Machine Descriptions of People Images
Authors: Barlas Pınar , Kyriakou Kyriakos , Kleanthous Styliani , Otterbacher Jahna
Published: November 2022

Image analysis algorithms have become an indispensable tool in our information ecosystem, facilitating new forms of visual communication and information sharing. At the same time, they enable large-scale socio-technical research which would otherwise be difficult to carry out. However, their outputs may exhibit social bias, especially when analyzing people images. Since most algorithms are proprietary and opaque, we propose a method of auditing their outputs for social biases. To be able to compare how algorithms interpret a controlled set of people images, we collected descriptions across six image tagging algorithms. In order to compare these results to human behavior, we also collected descriptions on the same images from crowdworkers in two anglophone regions. The dataset we present consists of tags from these eight taggers, along with a typology of concepts, and a python script to calculate vector scores for each image and tagger. Using our methodology, researchers can see the behaviors of the image tagging algorithms and compare them to those of crowdworkers. Beyond computer vision auditing, the dataset of humanand machine-produced tags, the typology, and the vectorization method can be used to explore a range of research questions related to both algorithmic and human behaviors.
WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks
Authors: Consonni Cristian , Laniado David , Montresor Alberto
Published: November 2022

Wikipedia articles contain multiple links connecting a subject to other pages of the encyclopedia. In Wikipedia parlance, these links are called internal links or wikilinks. We present a complete dataset of the network of internal Wikipedia links for the 9 largest language editions. The dataset contains yearly snapshots of the network and spans 17 years, from the creation of Wikipedia in 2001 to March 1st, 2018. While previous work has mostly focused on the complete hyperlink graph which includes also links automatically generated by templates, we parsed each revision of each article to track links appearing in the main text. In this way we obtained a cleaner network, discarding more than half of the links and representing all and only the links intentionally added by editors. We describe in detail how the Wikipedia dumps have been processed and the challenges we have encountered, including the need to handle special pages such as redirects, i.e., alternative article titles. We present descriptive statistics of several snapshots of this network. Finally, we propose several research opportunities that can be explored using this new dataset.
Shouting into the Void: A Database of the Alternative Social Media Platform Gab
Authors: Fair Gabriel , Wesslen Ryan
Published: November 2022

As social media platforms have increased their role as content moderators, alternative social media platforms have emerged with fewer rules and policies on moderating content. One such platform is Gab, a social media platform similar to Twitter that champions free speech with minimal standards on content. Early research has linked this platform with alt-right, hate speech, conspiracy theories, and other alternative content that is sometimes marginalized in mainstream social media platforms like Twitter and Facebook. In an effort to provide a means for researchers to study this platform, we introduce a database of 37,012,061 posts (with additional edit histories), 24,551,804 comments, and 819,957 user profiles webscraped from Gab between August 2016 and December 2018. In this paper, we outline our data collection process, describe the data, consider the ethics of our data collection process, and provide suggested avenues of inquiry for researchers interested in analyzing our database.
Detecting Journalism in the Age of Social Media:Three Experiments in Classifying Journalists on Twitter
Authors: Zeng Li , Dailey Dharma , Mohamed Owla , Starbird Kate , Spiro Emma S.
Published: November 2022

The widespread adoption of networked information and communications technologies (i.e. ICTs) blurs traditional boundaries between journalist and citizen. The role of the journalist is adapting to structural changes in the news industry and dynamic audience expectations. For researchers who seek to understand what, if any, distinct role journalists play in the production and propagation of breaking news, it is vital to be able to identify journalists in social media spaces. In many cases, this can be challenging due to the limited information and metadata about social media users. In this work, we use a supervised machine learning model to automatically distinguish journalists from non-journalists in social media spaces. Leveraging Twitter data collected from three crisis events of different types, we examine how profile information, social network structure, posting behavior and language distinguish journalists from others. Additionally, we evaluate how the performance of the journalist classification model varies by context (i.e. types of crisis events) and by journalism outlets (i.e. print versus broadcast journalism), and discuss challenges in automatic journalist detection. Implications of this work are discussed; in particular we argue for the value of such methods for scaling analysis in journalism studies beyond the capacity of human coders. Employing classification methods in this context allows for systematic, large-scale studies of the role of journalists online.
Self-Disclosure of Bullying Experiences and Social Support in Avatar Communication: Analysis of Verbal and Nonverbal Communications
Authors: Takano Masanori , Tsunoda Takaaki
Published: November 2022

Avatar communication through the Internet has great potential to be an appropriate environment for self-disclosure and social support. Anonymity and ease of access drive selfdisclosure of even the most serious problems. Rich nonverbal communication, co-presence, and real-time interaction increase emotional closeness. However, there has not been much research with regard to examining social support in avatar communication. In this paper, we aim to facilitate self-disclosure and social support for bullied people through avatar communication. For this purpose, we analyzed verbal and nonverbal communication about bullying experiences through an avatar communication service. We demonstrate that people who emotionally disclosed their bullying experiences received better social support. In addition, people who provided social support used emotional expressions to convey emotional empathy. These were observed in conversations with a few acquaintances in closed spaces. Our findings reveal areas where we can improve upon the design of avatar communication spaces for effective social support.
Using Platform Signals for Distinguishing Discourses: The Case of Men’s Rights and Men’s Liberation on Reddit
Authors: LaViolette Jack , Hogan Bernie
Published: November 2022

Reddit’s men’s rights community (/r/MensRights) has been criticized for the promotion of misogynistic language, toxic masculinity and discourses that reinforce alt-right ideologies. Conversely, the men’s liberation (/r/MensLib) community integrates inclusive politics, intersectionality and masculinity within a broad umbrella of self-reflection that suggests toxic masculinity harms men as well as women.

We use machine learning text classifiers, keyword frequencies, and qualitative approaches first to distinguish these two subreddits, and second to interpret the differences ideologically rather than topically. We further integrate platform metadata (referred to as ‘platform signals’) to distinguish the subreddits. These signals help us understand how similar terms can be used to arrive at different interpretations of gender and discrimination. Where /r/MensLib tends to see masculinity as an adjective and women as peers, /r/MensRights views being a man as an essential quality, men as the target of discrimination, and women as sources of personalized grievances.