bluuebunny/crossref_metadata_2025 huggingface dataset:
Multimodal Social Media Analysis for Gang Violence Prevention
Authors: Blandfort Philipp , Patton Desmond U. , Frey William R. , Karaman Svebor , Bhargava Surabhi , Lee Fei-Tzin , Varia Siddharth , Kedzie Chris , Gaskell Michael B. , Schifanella Rossano , McKeown Kathleen , Chang Shih-Fu
Published: November 2022

Gang violence is a severe issue in major cities across the U.S. and recent studies have found evidence of social media communications that can be linked to such violence in communities with high rates of exposure to gang activity. In this paper we partnered computer scientists with social work researchers, who have domain expertise in gang violence, to analyze how public tweets with images posted by youth who mention gang associations on Twitter can be leveraged to automatically detect psychosocial factors and conditions that could potentially assist social workers and violence outreach workers in prevention and early intervention programs. To this end, we developed a rigorous methodology for collecting and annotating tweets. We gathered 1,851 tweets and accompanying annotations related to visual concepts and the psychosocial codes: aggression, loss, and substance use. These codes are relevant to social work interventions, as they represent possible pathways to violence on social media. We compare various methods for classifying tweets into these three classes, using only the text of the tweet, only the image of the tweet, or both modalities as input to the classifier. In particular, we analyze the usefulness of mid-level visual concepts and the role of different modalities for this tweet classification task. Our experiments show that individually, text information dominates classification performance of the loss class, while image information dominates the aggression and substance use classes. Our multimodal approach provides a very promising improvement (18% relative in mean average precision) over the best single modality approach. Finally, we also illustrate the complexity of understanding social media data and elaborate on open challenges. The annotated dataset will be made available for research with strong ethical protection mechanism.
More Than Money: Correlation among Worker Demographics, Motivations, and Participation in Online Labor Market
Authors: Chen Wei-Chu , Suri Siddharth , Gray Mary L.
Published: November 2022

Most prior research about online labor markets examines the dynamics of a single work platform and either worker demographics or motivations associated with that site. How demographics and motives correlate with each other, and with engagement across multiple platforms, remains understudied. To bridge this gap, we analyze survey responses from 1700 people working across four different online labor platforms to understand: What motivates people to participate in online labor markets and how do individual motives correspond to larger demographic patterns and structural dynamics that more broadly shape traditional employment opportunities? Our results show that age, gender, education, and number of income sources help explain who does ondemand work, when they do it, and why. Even more striking, these broader social dimensions of work correlate with when and why individuals work across multiple on-demand platform companies. Together, these factors structure ondemand labor markets more than individual choice or the presumed “flexibility” of on-demand work alone.
Analyzing the Traits and Anomalies of Political Discussions on Reddit
Authors: Guimaraes Anna , Balalau Oana , Terolli Erisa , Weikum Gerhard
Published: November 2022

Online communities like Reddit feature intensive discussions on political topics, engaging many users who give and receive votes for their posts and comments. Prior work has focused on detecting controversial discussions and analyzing the role of troll-like users. This paper provides a systematic analysis of the characteristics of user actions, like replies and votes, in discussion threads, identifying different patterns that refine the notion of controversies into disputes, disruptions and discrepancies. Most importantly, our study connects the dimension of user actions with a rich feature space comprising also the sentiments expressed in post contents and the topical variation across the posts and replies. We use this framework to gain insight on the traits of different archetypes of discussions, and to statistically test a variety of hypotheses on expected and abnormal behavior.
Understanding and Measuring Psychological Stress Using Social Media
Authors: Chandra Guntuku Sharath , Buffone Anneke , Jaidka Kokil , Eichstaedt Johannes C. , Ungar Lyle H.
Published: November 2022

A body of literature has demonstrated that users’ mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data. Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed. Secondly, we find that Facebook language is more predictive of stress than Twitter language. Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends. Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language. We find that domain-adapted and scaled social mediabased measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties. We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.
Rating Reliability and Bias in News Articles: Does AI Assistance Help Everyone?
Authors: Horne Benjamin D. , Nevo Dorit , O’Donovan John , Cho Jin-Hee , Adalı Sibel
Published: November 2022

With the spread of false and misleading information in current news, many algorithmic tools have been introduced with the aim of assessing bias and reliability in written content. However, there has been little work exploring how effective these tools are at changing human perceptions of content. To this end, we conduct a study with 654 participants to understand if algorithmic assistance improves the accuracy of reliability and bias perceptions, and whether there is a difference in the effectiveness of the AI assistance for different types of news consumers. We find that AI assistance with featurebased explanations improves the accuracy of news perceptions. However, some consumers are helped more than others. Specifically, we find that participants who read and share news often on social media are worse at recognizing bias and reliability issues in news articles than those who do not, while frequent news readers and those familiar with politics perform much better. We discuss these differences and their implication to offer insights for future research.
Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images
Authors: Kyriakou Kyriakos , Barlas Pınar , Kleanthous Styliani , Otterbacher Jahna
Published: November 2022

There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of image tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information ecosystem, facilitating new modes of visual communication and sharing. Recently, they have become widely available as Cognitive Services. But while tagging APIs offer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and proprietary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some offer more interpretation on images, they may exhibit less fairness toward the depicted persons, by misuse of gender-related tags and/or making judgments on a person’s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarked against a ground truth.
Adapting Sequence to Sequence Models for Text Normalization in Social Media
Authors: Lourentzou Ismini , Manghnani Kabir , Zhai ChengXiang
Published: November 2022

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-theshelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.
Thou Shalt Not Hate: Countering Online Hate Speech
Authors: Mathew Binny , Saha Punyajoy , Tharad Hardik , Rajgaria Subham , Singhania Prajwal , Maity Suman Kalyan , Goyal Pawan , Mukherjee Animesh
Published: November 2022

Hate content in social media is ever increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle the hateful content, they have mostly been unsuccessful. Counterspeech is seen as an effective way of tackling the online hate without any harm to the freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 13,924 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive much more likes as compared to the noncounterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting non-counterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.71. We also build multilabel models that can detect different types of counterspeech in a comment with an F1-score of 0.60.
Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles
Authors: Rajadesingan Ashwin , Mahalingam Ramaswami , Jurgens David
Published: November 2022

Discriminatory caste attitudes currently stigmatize millions of Indians, subjecting individuals to prejudice in all aspects of life. Governmental incentives and societal movements have attempted to counter these attitudes, yet accurate measurements of public opinions on caste are not yet available for understanding whether progress is being made. Here, we introduce a novel approach to measure public attitudes of caste through an indicator variable: openness to intercaste marriage. Using a massive dataset of over 313K profiles from a major Indian matrimonial site, we precisely quantify public attitudes, along with differences between generations and between Indian residents and diaspora. We show that younger generations are more open to intercaste marriage, yet attitudes are based on a complex function of social status beyond their own caste. In examining the desired qualities in a spouse, we find that individuals open to intercaste marriage are more individualistic in the qualities they desire, rather than favoring family-related qualities, which mirrors larger societal trends away from collectivism. Finally, we show that attitudes in diaspora are significantly less open, suggesting a bi-cultural model of integration. Our research provides the first empirical evidence identifying how various intersections of identity shape attitudes toward intercaste marriage in India and among the Indian diaspora in the US.
The Language of Dialogue Is Complex
Authors: Robertson Alexander , Aiello Luca Maria , Quercia Daniele
Published: November 2022

Integrative Complexity (IC) is a psychometric that measures the ability of a person to recognize multiple perspectives and connect them, thus identifying paths for conflict resolution. IC has been linked to a wide variety of political, social and personal outcomes but evaluating it is a time-consuming process requiring skilled professionals to manually score texts, a fact which accounts for the limited exploration of IC at scale on social media. We combine natural language processing and machine learning to train an IC classification model that achieves state-of-the-art performance on unseen data and more closely adheres to the established structure of the IC coding process than previous automated approaches. When applied to the content of 400k+ comments from online fora about depression and knowledge exchange, our model was capable of replicating key findings of prior work, thus providing the first example of using IC tools for large-scale social media analytics.
PhAITV: A Phrase Author Interaction Topic Viewpoint Model for the Summarization of Reasons Expressed by Polarized Stances
Authors: Trabelsi Amine , Zaïane Osmar R.
Published: November 2022

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints in text documents. It proposes a pipeline framework that is centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint (PhAITV) model. The evaluation is conducted on all the components of the framework. It is mainly based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The framework shows a significant improvement over several configurations and state-of-the-art methods in contrastive summarization on online debate datasets.
Empirical Analysis of the Relation between Community Structure and Cascading Retweet Diffusion
Authors: Tsugawa Sho
Published: November 2022

Social networks have community structure, in which the network is composed of highly clustered subnetworks (communities) with sparse links between them. Such community structure is expected to affect information diffusion among individuals. This paper empirically investigates how the community structure of a social network among Twitter users affects cascading diffusion of retweets among them. The results show that the frequency of retweets between users who are in the same community is approximately two times that between users who are in different communities. In contrast, the results also show that tweets disseminated via inter-community retweets have future popularity about 1.5-fold that of tweets disseminated via intra-community retweets. By using this fact, we construct classifiers to predict the future popularity of tweets from community-based features as well as features related to influence of users and tweet contents. Our experimental results show that contrary to our expectations, community-based features have little contributions for predicting the future popularity of tweets. This paper discusses the implications of the counterintuitive result.
Demographic Differentials in Facebook Usage around the World
Authors: Gil-Clavel Sofia , Zagheni Emilio
Published: November 2022

We use data from the Facebook Advertisement Platform to study patterns of demographic disparities in usage of Facebook across countries. We address three main questions: (1) How does Facebook usage differ by age and by gender around the world? (2) How does the size of friendship networks vary by age and by gender? (3) What are the demographic characteristics of specific subgroups of Facebook users? We find that in countries in North America and northern Europe, patterns of Facebook usage differ little between older people and younger adults. In Asian countries, which have high levels of gender inequality, differences in Facebook adoption by gender disappear at older ages, possibly as a result of selectivity. We also observe that across countries, women tend to have larger networks of close friends than men, and that female users who are living away from their hometown are more likely to engage in Facebook use than their male counterparts, regardless of their region and age group. Our findings contextualize recent research on gender gaps in online usage, and offer new insights into some of the nuances of demographic differentials in the adoption and the use of digital technologies.
From “Welcome New Gabbers” to the Pittsburgh Synagogue Shooting: The Evolution of Gab
Authors: McIlroy-Young Reid , Anderson Ashton
Published: November 2022

Gab, an online social media platform with very little content moderation, has recently come to prominence as an alt-right community and a haven for hate speech. We document the evolution of Gab since its inception until a Gab user carried out the most deadly attack on the Jewish community in US history. We investigate Gab language use, study how topics evolved over time, and find that the shooters’ posts were among the most consistently anti-Semitic on Gab, but that hundreds of other users were even more extreme.
Race, Ethnicity and National Origin-Based Discrimination in Social Media and Hate Crimes across 100 U.S. Cities
Authors: Relia Kunal , Li Zhengyi , Cook Stephanie H. , Chunara Rumi
Published: November 2022

We study malicious online content via a specific type of hate speech: race, ethnicity and national-origin based discrimination in social media, alongside hate crimes motivated by those characteristics, in 100 cities across the United States. We develop a spatially-diverse training dataset and classification pipeline to delineate targeted and self-narration of discrimination on social media, accounting for language across geographies. Controlling for census parameters, we find that the proportion of discrimination that is targeted is associated with the number of hate crimes. Finally, we explore the linguistic features of discrimination Tweets in relation to hate crimes by city, features used by users who Tweet different amounts of discrimination, and features of discrimination compared to non-discrimination Tweets. Findings from this spatial study can inform future studies of how discrimination in physical and virtual worlds vary by place, or how physical and virtual world discrimination may synergize.
Communal Quirks and Circlejerks: A Taxonomy of Processes Contributing to Insularity in Online Communities
Authors: Allison Kimberley , Bussey Kay
Published: November 2022

Online communication offers the potential for bridging con-nections, exposing users to new views and experiences by fostering socially heterogenous communities. However, in the absence of deliberate attempts to promote diversity, communities may tend towards insularity: a state where members and content are similar or homogenous, and where deviation from these norms is discouraged. This paper presents a taxonomy of processes contributing to insularity, synthesizing findings from a broader longitudinal interview study on engagement with online communities over time with previous literature. Using thematic analysis, sixteen processes were identified which were associated with four broad stages: formation (selective connections, network homophily, shared interests, audience segmentation); propagation (circlejerking, upholding community standards, avoiding conflict, tailoring content); reaction (individual avoidance, collective reaction, mocking deviance, derogating outsiders); and perpetuation (modelling, prior feedback, echo chambers, gatekeeping). These findings highlight the need to consider more diverse mechanisms by which communities become insular, and the role that platform features play in facilitating these processes.
Auditing Race and Gender Discrimination in Online Housing Markets
Authors: Asplund Joshua , Eslami Motahhare , Sundaram Hari , Sandvig Christian , Karahalios Karrie
Published: November 2022

While researchers have developed rigorous practices for offline housing audits to enforce the US Fair Housing Act, the online world lacks similar practices. In this work we lay out principles for developing and performing online fairness audits. We demonstrate a controlled sock-puppet audit technique for building online profiles associated with a specific demographic profile or intersection of profiles, and describe the requirements to train and verify profiles of other demographics. We also present two audits using these sock-puppet profiles. The first audit explores the number and content of housing-related ads served to a user. The second compares the ordering of personalized recommendations on major housing and real-estate sites. We examine whether the results of each of these audits exhibit indirect discrimination: whether there is correlation between the content served and users' protected features, even if the system does not know or use these features explicitly. Our results show differential treatment in the number and type of housing ads served based on the user's race, as well as bias in property recommendations based on the user's gender. We believe this framework provides a compelling foundation for further exploration of housing fairness online.
Auditing News Curation Systems: A Case Study Examining Algorithmic and Editorial Logic in Apple News
Authors: Bandy Jack , Diakopoulos Nicholas
Published: November 2022

This work presents an audit study of Apple News as a sociotechnical news curation system that exercises gatekeeping power in the media. We examine the mechanisms behind Apple News as well as the content presented in the app, outlining the social, political, and economic implications of both aspects. We focus on the Trending Stories section, which is algorithmically curated, and the Top Stories section, which is human-curated. Results from a crowdsourced audit showed minimal content personalization in the Trending Stories section, and a sock-puppet audit showed no location-based content adaptation. Finally, we perform an extended two-month data collection to compare the human-curated Top Stories section with the algorithmically-curated Trending Stories section. Within these two sections, human curation outperformed algorithmic curation in several measures of source diversity, concentration, and evenness. Furthermore, algorithmic curation featured more “soft news” about celebrities and entertainment, while editorial curation featured more news about policy and international events. To our knowledge, this study provides the first data-backed characterization of Apple News in the United States.
Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News about the 2016 U.S. Presidential Nominees
Authors: Bozarth Lia , Saraf Aparajita , Budak Ceren
Published: November 2022

The spread of fake news on social media platforms has garnered much public attention and apprehension. Consequently, both the tech industry and academia alike are investing increased effort to understand, detect, and curb fake news. Yet, researchers differ in what they consider to be fake news sites. In this paper, we first aggregate 5 lists of fake and 3 of mainstream news sites published by experts and reputable organizations. Then, focusing on tweets about the democratic (Hillary Clinton) and republican (Donald Trump) nominees in the 2016 U.S. presidential election, we use each pair of fake and traditional news lists as an independent “groundtruth” to examine i) the prevalence, ii) temporal characteristics and iii) the agenda-setting differences between fake and traditional news sites. We observe that depending on the groundtruth, the prevalence of fake news varies significantly. However, the temporal trends and agenda-setting differences between fake and mainstream news sites remain moderately consistent across different groundtruth lists.
Falling into the Echo Chamber: The Italian Vaccination Debate on Twitter
Authors: Cossard Alessandro , De Francisci Morales Gianmarco , Kalimeri Kyriaki , Mejova Yelena , Paolotti Daniela , Starnini Michele
Published: November 2022

The reappearance of measles in the US and Europe, a disease considered eliminated in early 2000s, has been accompanied by a growing debate on the merits of vaccination on social media. In this study we examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We focus on Italy, one of the countries most affected by the latest measles outbreaks. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct “echo chambers”. The structure of these communities differs as well, with skeptics arranged in a tightly connected cluster, and advocates organizing themselves around few authoritative hubs. At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers (attaining 95% cross-validated accuracy). Insights of this study provide several avenues for potential future interventions, including network-guided targeting, accounting for the political context, and monitoring of alternative sources of information.
Unsupervised User Stance Detection on Twitter
Authors: Darwish Kareem , Stefanov Peter , Aupetit Michaël , Nakov Preslav
Published: November 2022

We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics. In particular, we use dimensionality reduction to project users onto a low-dimensional space, followed by clustering, which allows us to find core users that are representative of the different stances. Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification. First, we do not require any prior labeling of users: instead, we create clusters, which are much easier to label manually afterwards, e.g., in a matter of seconds or minutes instead of hours. Second, there is no need for domain- or topic-level knowledge either to specify the relevant stances (labels) or to conduct the actual labeling. Third, our framework is robust in the face of data skewness, e.g., when some users or some stances have greater representation in the data. We experiment with different combinations of user similarity features, dataset sizes, dimensionality reduction methods, and clustering algorithms to ascertain the most effective and most computationally efficient combinations across three different datasets (in English and Turkish). We further verified our results on additional tweet sets covering six different controversial topics. Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2–3, with more than 98% purity. The resulting user clusters can be used to train downstream classifiers. Moreover, our framework is robust to variations in the hyper-parameter values and also with respect to random initialization.
No Robots, Spiders, or Scrapers: Legal and Ethical Regulation of Data Collection Methods in Social Media Terms of Service
Authors: Fiesler Casey , Beard Nathan , Keegan Brian C.
Published: November 2022

Researchers from many different disciplines rely on social media data as a resource. Whereas some platforms explicitly allow data collection, even facilitating it through an API, others explicitly forbid automated or manual collection processes. A current topic of debate within the social computing research community involves the ethical (or even legal) implications of collecting data in ways that violate Terms of Service (TOS). Using a sample of TOS from over one hundred social media sites from around the world, we analyze TOS language and content in order to better understand the landscape of prohibitions on this practice. Our findings show that though these provisions are very common, they are also ambiguous, inconsistent, and lack context. By considering our analysis of the nature of these provisions alongside legal and ethical analysis, we propose that ethical decision-making for data collection should extend beyond TOS and consider contextual factors of the data source and research.
#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement
Authors: Gautam Akash , Mathur Puneet , Gosangi Rakesh , Mahata Debanjan , Sawhney Ramit , Shah Rajiv Ratn
Published: November 2022

In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.
REST: A Thread Embedding Approach for Identifying and Classifying User-Specified Information in Security Forums
Authors: Gharibshah Joobin , Papalexakis Evangelos E. , Faloutsos Michalis
Published: November 2022

How can we extract useful information from a security forum? We focus on identifying threads of interest to a security professional: (a) alerts of worrisome events, such as attacks, (b) offering of malicious services and products, (c) hacking information to perform malicious acts, and (d) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Novel approaches are needed to address the challenges in this domain: (a) the difficulty in specifying the “topics” of interest efficiently, and (b) the unstructured and informal nature of the text. We propose, REST, a systematic methodology to: (a) identify threads of interest based on a, possibly incomplete, bag of words, and (b) classify them into one of the four classes above. The key novelty of the work is a multi-step weighted embedding approach: we project words, threads and classes in appropriate embedding spaces and establish relevance and similarity there. We evaluate our method with real data from three security forums with a total of 164k posts and 21K threads. First, REST robustness to initial keyword selection can extend the user-provided keyword set and thus, it can recover from missing keywords. Second, REST categorizes the threads into the classes of interest with superior accuracy compared to five other methods: REST exhibits an accuracy between 63.3-76.9%. We see our approach as a first step for harnessing the wealth of information of online forums in a user-friendly way, since the user can loosely specify her keywords of interest.
Two Computational Models for Analyzing Political Attention in Social Media
Authors: Hemphill Libby , Schöpke-Gonzalez Angela M.
Published: November 2022

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models—one supervised classifier and one unsupervised topic model—provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.
Modeling and Measuring Expressed (Dis)belief in (Mis)information
Authors: Jiang Shan , Metzger Miriam , Flanagin Andrew , Wilson Christo
Published: November 2022

The proliferation of online misinformation has been raising increasing societal concerns about its potential consequences, e.g., polarizing the public and eroding trust in institutions. These consequences are framed under the public's susceptibility to such misinformation — a narrative that needs further investigation and quantification. To this end, our paper proposes an observational approach to model and measure expressed (dis)beliefs in (mis)information by leveraging social media comments as a proxy. We collect a sample of tweets in response to (mis)information and annotate them with (dis)belief labels, explore the dataset using lexicon-based methods, and finally build classifiers based on the state-of-the-art neural transfer-learning models (BERT, XLNet, and RoBERTa). Under a domain-specific thresholding strategy for unbiasedness, the best-performing classifier archives macro-F1 scores around 0.86 for disbelief and 0.80 for belief. Applying the classifier, we conduct a large-scale measurement study and show that, for true/mixed/false claims on social media, 12%/14%/15% of comments express disbelief and 26%/21%/20% of comments express belief. In addition, our results suggest an extremely slight time effect of falsehood awareness, a positive effect of fact-checks to false claims, and differences in (dis)belief across social media platforms.
Behind the Mask: A Computational Study of Anonymous' Presence on Twitter
Authors: Jones Keenan , Nurse Jason R. C. , Li Shujun
Published: November 2022

The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.
Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities
Authors: Kadar Cristina , Feuerriegel Stefan , Noulas Anastasios , Mascolo Cecilia
Published: November 2022

Crime has been previously explained by social characteristics of the residential population and, as stipulated by crime pattern theory, might also be linked to human movements of non-residential visitors. Yet a full empirical validation of the latter is lacking. The prime reason is that prior studies are limited to aggregated statistics of human visitors rather than mobility flows and, because of that, neglect the temporal dynamics of individual human movements. As a remedy, we provide the first work which studies the ability of granular human mobility in describing and predicting crime concentrations at an hourly scale. For this purpose, we propose the use of data from location technology platforms. This type of data allows us to trace individual transitions and, therefore, we succeed in distinguishing different mobility flows that (i) are incoming or outgoing from a neighborhood, (ii) remain within it, or (iii) refer to transitions where people only pass through the neighborhood. Our evaluation infers mobility flows by leveraging an anonymized dataset from Foursquare that includes almost 14.8 million consecutive check-ins in three major U.S. cities. According to our empirical results, mobility flows are significantly and positively linked to crime. These findings advance our theoretical understanding, as they provide confirmatory evidence for crime pattern theory. Furthermore, our novel use of digital location services data proves to be an effective tool for crime forecasting. It also offers unprecedented granularity when studying the connection between human mobility and crime.
Gravity of Location-Based Service: Analyzing the Effects for Mobility Pattern and Location Prediction
Authors: Ochiai Keiichi , Fukazawa Yusuke , Yamada Wataru , Manabe Hiroyuki , Matsuo Yutaka
Published: November 2022

Predicting user location is one of the most important topics in data mining. Although human mobility is reasonably predictable for frequently visited places, novel location prediction is much more difficult. However, location-based services (LBSs) can influence users' choice of destination and can be exploited to more accurately predict user location even for new locations. In this study, we assessed the behavior difference for specific LBS users and non-users by using large-scale check-in data. We found a remarkable difference between specific LBS users and non-users (e.g., check-in locations) that had previously not been revealed. Then, we proposed a location prediction method exploiting the characteristics of check-in locations and analyzed how specific LBS usage influences location predictability. We assumed that users who use the same LBS tend to visit similar locations. The results showed that the novel location predictability of specific LBS users is up to 43.9% higher than that of non-users.
Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits
Authors: Rajadesingan Ashwin , Resnick Paul , Budak Ceren
Published: November 2022

Online communities about similar topics may maintain very different norms of interaction. Past research identifies many processes that contribute to maintaining stable norms, including self-selection, pre-entry learning, post-entry learning, and retention. We analyzed political subreddits that had distinctive, stable levels of toxic comments on Reddit, in order to identify the relative contribution of these four processes. Surprisingly, we find that the largest source of norm stability is pre-entry learning. That is, newcomers' first comments in these distinctive subreddits differ from those same people's prior behavior in other subreddits. Through this adjustment, they nearly match the toxicity level of the subreddit they are joining. We also show that behavior adjustments are community-specific and not broadly transformative. That is, people continue to post toxic comments at their previous rates in other political subreddits. Thus, we conclude that in political subreddits, compatible newcomers are neither born nor made– they make local adjustments on their own.
Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
Authors: Risch Julian , Krestel Ralf
Published: November 2022

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users and are better conversation starters.In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives. Based on comment texts, we train a model to distinguish comments that have either a high or low chance of receiving many upvotes and replies. Our evaluation on user comments from TheGuardian.com compares recurrent and convolutional neural network models, and a traditional feature-based classifier. Further, we investigate what makes some comments more engaging than others. To this end, we identify engagement triggers and arrange them in a taxonomy. Explanation methods for neural networks reveal which input words have the strongest influence on our model's predictions. In addition, we evaluate on a dataset of product reviews, which exhibit similar properties as user comments, such as featuring upvotes for helpfulness.
Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms
Authors: Sharma Ashish , Choudhury Monojit , Althoff Tim , Sharma Amit
Published: November 2022

Mental illness is a global health problem, but access to mental healthcare resources remain poor worldwide. Online peer-to-peer support platforms attempt to alleviate this fundamental gap by enabling those who struggle with mental illness to provide and receive social support from their peers. However, successful social support requires users to engage with each other and failures may have serious consequences for users in need. Our understanding of engagement patterns on mental health platforms is limited but critical to inform the role, limitations, and design of these platforms. Here, we present a large-scale analysis of engagement patterns of 35 million posts on two popular online mental health platforms, TalkLife and Reddit. Leveraging communication models in human-computer interaction and communication theory, we operationalize a set of four engagement indicators based on attention and interaction. We then propose a generative model to jointly model these indicators of engagement, the output of which is synthesized into a novel set of eleven distinct, interpretable patterns. We demonstrate that this framework of engagement patterns enables informative evaluations and analysis of online support platforms. Specifically, we find that mutual back-and-forth interactions are associated with significantly higher user retention rates on TalkLife. Such back-and-forth interactions, in turn, are associated with early response times and the sentiment of posts.
Variation across Scales: Measurement Fidelity under Twitter Data Sampling
Authors: Wu Siqi , Rizoiu Marian-Andrei , Xie Lexing
Published: November 2022

A comprehensive understanding of data quality is the cornerstone of measurement studies in social media research. This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing complete tweet streams, we show that Twitter rate limit message is an accurate indicator for the volume of missing tweets. Sampling also differs significantly across timescales. While the hourly sampling rate is influenced by the diurnal rhythm in different time zones, the millisecond level sampling is heavily affected by the implementation choices. For Twitter entities such as users, we find the Bernoulli process with a uniform rate approximates the empirical distributions well. It also allows us to estimate the true ranking with the observed sample data. For networks on Twitter, their structures are altered significantly and some components are more likely to be preserved. For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect models that rely on these features. This work calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects.
Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification
Authors: Ziems Caleb , Vigfusson Ymir , Morstatter Fred
Published: November 2022

Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.