bluuebunny/crossref_metadata_2025 huggingface dataset:
Web and Social Media Image Forensics for News Professionals
Authors: Zampoglou Markos , Papadopoulos Symeon , Kompatsiaris Yiannis , Bouwmeester Ruben , Spangenberg Jochen
Published: November 2022

User-generated content -commonly referred to as "eyewitness media"- has become an essential component in journalism and news reporting. Increasingly more news providers, such as news agencies, broadcasters and Web-only players have set up teams of dedicated investigators or are in the process of training parts of their journalistic workforce to gather and evaluate material from social networks and the Web. If verified, such content can be invaluable in delivering a news story. However, while source checking and verification is as old as journalism itself, the verification of digital material is a relatively young field, with protocols and assisting tools still being developed. In this work, we present our efforts towards a Web-based image verification platform. The platform, currently in its alpha stage, features image tampering detection using a number of state-of-the-art algorithms and image metadata visualization. We discuss the current strengths and limitations of the platform and the implemented state-of-the-art with respect to the specific requirements of the task, resulting from its Web-based nature and its intended use by news investigators with limited expertise in the domain of image forensics.
Data Sets: Word Embeddings Learned from Tweets and General Data
Authors: Li Quanzhi , Shah Sameena , Liu Xiaomo , Nourbakhsh Armineh
Published: November 2022

A word embedding is a low-dimensional, dense and real-valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually generated from a large text corpus. The embedding of a word captures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets and the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general data. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks.
TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia
Authors: Flöck Fabian , Erdogan Kenan , Acosta Maribel
Published: November 2022

We present a dataset that contains every instance of all tokens (words) ever written in undeleted, non-redirect English Wikipedia articles until October 2016, in total 13,545,349,78 instances. Each token is annotated with (i) the article revision it was originally created in, and (ii) lists with all the revisions in which the token was ever deleted and (potentially) re-added and re-deleted from its article, enabling a complete and straightforward tracking of its history. This data would be exceedingly hard to create by an average potential user as it is (i) very expensive to compute and as (ii) accurately tracking the history of each token in revisioned documents is a non-trivial task. Adapting a state-of-the-art algorithm, we have produced a dataset that allows for a range of analyses and metrics, already popular in research and going beyond, to be generated on complete-Wikipedia scale; ensuring quality and allowing researchers to forego expensive text-comparison computation, which so far has hindered scalable usage. We show how this data enables, on token-level, computation of provenance, measuring survival of content over time, very detailed conflict metrics, and fine-grained interactions of editors like partial reverts, re-additions and other metrics, in the process gaining several novel insights.
To Help or Hinder: Real-Time Chat in Citizen Science
Authors: Tinati Ramine , Simperl Elena , Luczak-Roesch Markus
Published: November 2022

In this paper we investigate the implications of providing a real-time messaging interface in a Web-based citizen science game. Our study draws on data from two weeks of chat messages and survey responses collected from Eyewire, a highly successful citizen science game which enables players to take part in scientific enquiries, within a semi-gamified environment. Our analysis reveals that real-time chat facilitates and supports players for several types of engagement; to collaboration on tasks, knowledge sharing, learning, socialising, supporting other in the community, and to help sustain long-term participation. Based on the analysis, we derive a set of design recommendations for citizen science platforms designers, focusing on the role of real-time chat on improving participation and performance.
Tracing the Use of Practices Through Networks of Collaboration
Authors: Rotabi Rahmtin , Danescu-Niculescu-Mizil Cristian , Kleinberg Jon
Published: November 2022

An active line of research has used on-line data to study the ways in which discrete units of information — including messages, photos, product recommendations, group invitations — spread through social networks. There is relatively little understanding, however, of how on-line data might help in studying the diffusion of more complex practices — roughly, routines or styles of work that are generally handed down from one person to another through collaboration or mentorship. In this work, we propose a framework together with a novel type of data analysis that seeks to study the spread of such practices by tracking their syntactic signatures in large document collections. Central to this framework is the notion of an inheritance graph that represents how people pass the practice on to others through collaboration. Our analysis of these inheritance graphs demonstrates that we can trace a significant number of practices over long time-spans, and we show that the structure of these graphs can help in predicting the longevity of collaborations within a field, as well as the fitness of the practices themselves.
Is Slacktivism Underrated? Measuring the Value of Slacktivists for Online Social Movements
Authors: Bozarth Lia , Budak Ceren
Published: November 2022

We measure the value of “slacktivists” involved in more than 80 protest movement hashtags on Twitter, focusing on their value for content generation, communication, encouragement, and content diversity. Further, we move beyond the existing literature which primarily focuses on direct content contributions by investigating whether slacktivists induce further contributions from other activists by providing them simple cues of encouragement that enhance non-slacktivists’ commitment to a movement. Our findings suggest that slacktivist actions, unlike those by dedicated activists, do not encourage others, and if anything, discourage them.
Predicting Movie Genre Preferences from Personality and Values of Social Media Users
Authors: Mukta Md. Saddam Hossain , Khan Euna Mehnaz , Ali Mohammed Eunus , Mahmud Jalal
Published: November 2022

We propose a novel technique to predict a user’s movie genre preference from her psycholinguistic attributes obtained from user social media interactions. In particular, we build machine learning based classiﬁcation models that take user tweets as input to derive her psychological attributes: personality and value scores, and gives her movie genre preference as output. We train these models using user tweets in Twitter, and her reviews and ratings of movies of different genres in Internet movie database (IMDb). We exploit a key concept of psychology, i.e., an individual’s personality and values may inﬂuence her choice in performing different actions in real life. We have investigated how personality and values independently and collectively inﬂuence a user preference on different movie genres. Our proposed model can be used for recommending movies to social media users.
QT2S: A System for Monitoring Road Traffic Via Fine Grounding of Tweets
Authors: Al Emadi Noora , Abbar Sofiane , Borge-Holthoefer Javier , Guzman Francisco , Sebastiani Fabrizio
Published: November 2022

Social media platforms provide continuous access to user generated content that enables real-time monitoring of user behavior and of events. The geographical dimension of such user behavior and events has recently caught a lot of attention in several domains: mobility, humanitarian, or infrastructural. While resolving the location of a user can be straightforward, depending on the affordances of their device and/or of the application they are using, in most cases, locating a user demands a larger effort, such as exploiting textual features. On Twitter for instance, only 2% of all tweets are geo-referenced. In this paper, we present a system for zoomed-in grounding (below city level) for short messages (for example, tweets). The system combines different natural language processing and machine learning techniques to increase the number of geo-grounded tweets, which is essential to many applications such as disaster response and real-time traffic monitoring.
Separating the Wheat from the Chaff: Evaluating Success Determinants for Online Q&A Communities
Authors: Aumayr Erik , Hayes Conor
Published: November 2022

Researchers and community managers try to measure the success of online communities using a variety of success determinants, such as member activity, turnover and interaction. Although many success determinants have been proposed in the literature, it remains largely unclear which are applicable to which kind of online communities. In this work, we focus on the use case of question answering (Q&A) communities, where we can measure asker satisfaction as an aspect of community success towards the goal of solving questions. We evaluate how well the proposed success determinants correlate to asker satisfaction, and we find that less than half of them are at least moderately (±0.3 or more) correlated. That underlines the importance of evaluating the proposed success determinants on real data of specific community types.
The Ebb and Flow of Controversial Debates on Social Media
Authors: Garimella Kiran , De Francisci Morales Gianmarco , Gionis Aristides , Mathioudakis Michael
Published: November 2022

We explore how the polarization around controversial topics evolves on Twitter — over a long period of time (2011 to 2016), and also as a response to major external events that lead to increased related activity. We find that increased activity is typically associated with increased polarization; however, we find no consistent long-term trend in polarization over time among the topics we study.
Should We Be Confident in Peer Effects Estimated From Social Network Crawls?
Authors: Yang Jiasen , Ribeiro Bruno , Neville Jennifer
Published: November 2022

Research in social network analysis and statistical relational learning has produced a number of methods for learning relational models from large-scale network data. Unfortunately, these methods have been developed under the unrealistic assumption of full data access. In practice, however, the data are often collected by crawling the network, due to proprietary access, limited resources, and privacy concerns. While prior studies have examined the impact of network crawling on the structural characteristics of the resulting samples, this work presents the first empirical study designed to assess the impact of widely used network crawlers on the estimation of peer effects. Our experiments demonstrate that the estimates obtained from network samples collected by existing crawlers can be quite inaccurate, unless a significant portion of the network is crawled. Meanwhile, motivated by recent advances in partial network crawling, we develop crawl-aware relational methods that provide accurate estimates of peer effects with statistical guarantees from partial crawls.
#Élysée2017fr: The 2017 French Presidential Campaign on Twitter
Authors: Fraisier Ophélie , Cabanac Guillaume , Pitarch Yoann , Besançon Romaric , Boughanem Mohand
Published: November 2022

The French presidential election was one of the main political event of 2017, and triggered a lot of activity on Twitter. The campaign was highly unpredictable and led to the rise of 5 main parties instead of the historical bipartite (left-right) confrontation, ranging from far-left to far-right. This dataset paper proposes #Élysée2017fr, a large and complex dataset of 22853 Twitter profiles active during the campaign (from November 2016 to May 2017), and their corresponding tweets and retweets, plus the retweet and mention networks related to these profiles. The profiles were manually annotated with their political affiliations (up to 2 political parties per profile), their nature (individual or collective), and the sex of the profile's owner when available. This is one of the rare datasets that considers a non-binary stance classification and, to our knowledge, the first one with a large number of profiles, and the first one proposing overlapping political communities. This dataset can be used as-is to study the campaign mechanisms on Twitter, or used to test stance detection models or network analysis tools. Mining these data might reveal new insights on current issues like echo chambers or fake news diffusion.
CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation
Authors: Tay Yi , Tuan Luu , Hui Siu
Published: November 2022

Dating and romantic relationships not only play a huge role in our personal lives but also collectively influence and shape society. Today, many romantic partnerships originate from the Internet, signifying the importance of technology and the web in modern dating. In this paper, we present a text-based computational approach for estimating the relationship compatibility of two users on social media. Unlike many previous works that propose reciprocal recommender systems for online dating websites, we devise a distant supervision heuristic to obtain real world couples from social platforms such as Twitter. Our approach, the CoupleNet is an end-to-end deep learning basedestimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users. Intuitively, our approach performs both user profiling and match-making within a unified end-to-end framework. CoupleNet utilizes hierarchical recurrent neural models for learning representations of user profiles and subsequently coupled attention mechanisms to fuse information aggregated from two users.To the best of our knowledge, our approach is the first data-driven deep learning approach for our novel relationship recommendation problem. We benchmarkour CoupleNet against several machine learning and deep learning baselines. Experimental results show that our approach outperformsall approaches significantly in terms of precision. Qualitative analysis shows that our model is capable of also producing explainable results to users.
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
Authors: Sun Jiankai , Moosavi Sobhan , Ramnath Rajiv , Parthasarathy Srinivasan
Published: November 2022

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of ``social agony'' to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models.
Facebook versus Twitter: Differences in Self-Disclosure and Trait Prediction
Authors: Jaidka Kokil , Guntuku Sharath , Ungar Lyle
Published: November 2022

This study compares self-disclosure on Facebook and Twitter through the lens of demographic and psychological traits. Predictive evaluation reveals that language models trained on Facebook posts are more accurate at predicting age, gender, stress, and empathy than those trained on Twitter posts. Qualitative analyses of the underlying linguistic and demographic differences reveal that users are significantly more likely to disclose information about their family, personal concerns, and emotions and provide a more `honest' self-representation on Facebook. On the other hand, the same users significantly preferred to disclose their needs, drives, and ambitions on Twitter. The higher predictive performance of Facebook is also partly due to the greater volume of language on Facebook than Twitter -- Facebook and Twitter are equally good at predicting user traits when the same-sized language samples are used to train language models. We explore the implications of these differences in cross-platform user trait prediction.
Modeling Evolution of Topics in Large-Scale Temporal Text Corpora
Authors: Momeni Elaheh , Karunasekera Shanika , Goyal Palash , Lerman Kristina
Published: November 2022

Large text temporal collections provide insights into social and cultural change over time. To quantify changes in topics in these corpora, embedding methods have been used as a diachronic tool. However, they have limited utility for modeling changes in topics due to the stochastic nature of training. We propose a new computational approach for tracking and detecting temporal evolution of topics in a large collection of texts. This approach for identifying dynamic topics and modeling their evolution combines the advantages of two methods: (1) word embeddings to learn contextual semantic representation of words from temporal snapshots of the data and (2) dynamic network analysis to identify dynamic topics by using dynamic semantic similarity networks developed using embedding models. Experimenting with two large temporal data sets from the legal and real estate domains, we show that this approach performs faster (due to parallelizing different snapshots), uncovers more coherent topics (compared to available dynamic topic modeling approaches), and effectively enables modeling evolution leveraging the network structure.
Mater Certa Est, Pater Numquam: What Can Facebook Advertising Data Tell Us about Male Fertility Rates?
Authors: Rampazzo Francesco , Zagheni Emilio , Weber Ingmar , Testa Maria Rita , Billari Francesco
Published: November 2022

In many developing countries, timely and accurate information about birth rates and other demographic indicators is still lacking, especially for male fertility rates. Using anonymous and aggregate data from Facebook’s Advertising Platform, we produce global estimates of the Mean Age at Childbearing (MAC), a key indicator of fertility postponement. Our analysis indicates that fertility measures based on Facebook data are highly correlated with conventional indicators based on traditional data, for those countries for which we have statistics. For instance, the correlation of the MAC computed using Facebook and United Nations data is 0.47 (p = 4.02e -08) and 0.79 (p = 2.2e-15) for female and male respectively. Out of sample validation for a simple regression model indicates that the mean absolute percentage error is 2.3%.We use the linear model and Facebook data to produce estimates of the male MAC for countries for which we do not have data.
Polarized, Together: Comparing Partisan Support for Trump’s Tweets Using Survey and Platform-Based Measures
Authors: Joseph Kenneth , Swire-Thompson Briony , Masuga Hannah , Baum Matthew A. , Lazer David
Published: November 2022

Using both survey- and platform-based measures of support, we study how polarization manifests for 4,313 of President Donald Trump’s tweets since he was inaugurated in 2017. We find high levels of polarization in response to Trump’s tweets. However, after controlling for mean differences, we surprisingly find a high degree of agreement across partisan lines across both survey and platform-based measures. This suggests that Republicans and Democrats, while disagreeing on an absolute level, tend to agree on the relative quality of Trump’s tweets. We assess potential reasons for this, for example, by studying how support changes in response to tweets containing positive versus negative language. We also explore how Democrats and Republicans respond to tweets containing insults of individuals with particular socio-demographics, finding that Republican support decreases when Republicans, relative to Democrats, are insulted, and Democrats respond negatively to insults of women and members of the media.
Explaining Multimodal Deceptive News Prediction Models
Authors: Volkova Svitlana , Ayton Ellyn , Arendt Dustin L. , Huang Zhuanyi , Hutchinson Brian
Published: November 2022

In this study we present in-depth quantitative and qualitative analyses of the behavior of multimodal deceptive news classification models. We present several neural network architectures trained on thousands of tweets that leverage combinations of text, lexical, and, most importantly, image input signals. The behavior of these models is analyzed across four deceptive news prediction tasks. Our quantitative analysis reveals that text only models outperform those leveraging only the image signals (by 3-13% absolute in F-measure). Neural network models that combine image and text signals with lexical features e.g., biased and subjective language markers perform even better e.g., F-measure is as high as 0.74 for binary classification setup for distinguishing between verified and deceptive content identified as disinformation and propaganda. Our qualitative analysis of model performance, that goes beyond the F-score, performed using a novel interactive tool ERRFILTER1 allows a user to characterize text and image traits of suspicious news content and analyze patterns of errors made by the various models, which in turn will inform the design of future deceptive news prediction models.
Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK
Authors: Agarwal Pushkal , Sastry Nishanth , Wood Edward
Published: November 2022

Disengagement and disenchantment with the Parliamentary process is an important concern in today’s Western democracies. Members of Parliament (MPs) in the UK are therefore seeking new ways to engage with citizens, including being on digital platforms such as Twitter. In recent years, nearly all (579 out of 650) MPs have created Twitter accounts, and have amassed huge followings comparable to a sizable fraction of the country’s population. This paper seeks to shed light on this phenomenon by examining the volume and nature of the interaction between MPs and citizens. We find that although there is an information overload on MPs, attention on individual MPs is focused during small time windows when something topical may be happening relating to them. MPs manage their interaction strategically, replying selectively to UKbased citizens and thereby serving in their role as elected representatives, and using retweets to spread their party’s message. Most promisingly, we find that Twitter opens up new avenues with substantial volumes of cross-party interaction, between MPs of one party and citizens who support (follow) MPs of other parties.
Minimizing Interference and Selection Bias in Network Experiment Design
Authors: Fatemi Zahra , Zheleva Elena
Published: November 2022

Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to biased causal effect estimation. Prominent methods for network experiment design rely on two-stage randomization, in which sparsely-connected clusters are identified and cluster randomization dictates the node assignment to treatment and control. Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users. To address this problem, we propose a principled framework for network experiment design which jointly minimizes interference and selection bias. We introduce the concepts of edge spillover probability and cluster matching and demonstrate their importance for designing network A/B testing. Our experiments on a number of real-world datasets show that our proposed framework leads to significantly lower error in causal effect estimation than existing solutions.
Confidence Boost in Dyadic Online Teamwork: An Individual-Focused Perspective
Authors: Fu Liye , Wang Andrew Z. , Danescu-Niculescu-Mizil Cristian
Published: November 2022

Individuals are often more confident in their solutions when working in teams than when working on their own. This confidence boost is observed even when it is not accompanied by a corresponding gain in performance, raising the question of what other factors might be responsible. We address this question by developing a large-scale experimental setting in the form of a two-player online game that allows us to track the confidence of individuals in naturally-occurring online collaborative tasks. This setting enables us to disentangle and compare the effects of different components of the collaborative process on the confidence of each team member. We show that confidence evaluations are subject to social influence: a low-confidence individual receives a confidence boost as a direct consequence of interacting with their teammate, and the extent of the increase depends more on the confidence, rather than on the competence, of the teammate. The resulting framework can enhance our understanding of confidence boost as an often overlooked byproduct of online teamwork and has implications for designing better online collaboration platforms to meet diverse collaborative objectives.
Towards Quantifying the Distance between Opinions
Authors: Gurukar Saket , Ajwani Deepak , Dutta Sourav , Lauri Juho , Parthasarathy Srinivasan , Sala Alessandra
Published: November 2022

Increasingly, critical decisions in public policy, governance, and business strategy rely on a deeper understanding of the needs and opinions of constituent members (e.g. citizens, shareholders). While it has become easier to collect a large number of opinions on a topic, there is a necessity for automated tools to help navigate the space of opinions. In such contexts understanding and quantifying the similarity between opinions is key. We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. Thus, we propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation – similar opinions express similar sentiment polarity on specific relevant entities-of-interest. Specifically, in an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches. Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity.
When Does Trust in Online Social Groups Grow?
Authors: Iyer Shankar , Cheng Justin , Brown Nick , Wang Xiuhua
Published: November 2022

The trust that people feel in their social groups is linked to important social outcomes such as member satisfaction and collective task performance. To understand the behaviors and conditions linked to trust, past studies of trust in groups have typically relied on cross-sectional surveys, but these are limited in their ability to identify causation. To better test the potential causal pathways between trust and behaviors or group properties, we paired a two-wave longitudinal survey of 2358 participants in Facebook Groups with logged activity on Facebook. Using latent change score modeling, we examined how trust may predict changes in behavior or group properties and how behaviors and group properties may predict changes in trust. On one hand, people who trust a group tend to contribute more written content to the group over time; and while groups that are more trusted tend to add more administrators and moderators over time, groups that have many administrators and moderators does not tend to be trusted more over time. On the other hand, people's trust in a group increases over time when the group is well-connected and active overall, while that trust decreases over time when that person is also actively involved in multiple other groups. These findings suggest a positive feedback loop related to active engagement and trust: seeing activity in a group drives trust, which in turn leads to increased individual activity and hence greater overall activity in the group. Overall, trust may be best promoted by encouraging both active engagement and friendship formation.
Beyond Positive Emotion: Deconstructing Happy Moments Based on Writing Prompts
Authors: Jaidka Kokil , Chhaya Niyati , Mumick Saran , Killingsworth Matthew , Halevy Alon , Ungar Lyle
Published: November 2022

This study reports experiments with the newly-released CL-Aff HappyDB dataset, which looks beyond positive emotion in modeling descriptions of happy moments collected through writing prompts. The widespread adoption of social media has improved researchers' access to unsolicited expressions and behaviors. However, most of the approaches to analyzing these expressions involve a keyword search and focuses on predicting sentiment or emotional content rather than understanding a deeper psychological state, such as happiness. The CL-Aff HappyDB dataset is the first effort to distinguish the personal agency and social interaction in writings about happiness, which do not yet have an exact equivalent concept in existing text-based approaches. We report that state of the art approaches for emotion detection have different topical characteristics, and do not generalize well to detect happiness in the CL-Aff HappyDB dataset. Language models trained on the dataset, on the other hand, generalize to social media writing and are a valid approach for downstream tasks, such as predicting life satisfaction from social media posts.
Style Matters! Investigating Linguistic Style in Online Communities
Authors: Khalid Osama , Srinivasan Padmini
Published: November 2022

Content has historically been the primary lens used to study language in online communities. This paper instead focuses on the linguistic style of communities. While we know that individuals have distinguishable styles, here we ask whether communities have distinguishable styles. Additionally, while prior work has relied on a narrow definition of style, we employ a broad definition involving 262 features to analyze the linguistic style of 9 online communities from 3 social media platforms discussing politics, television and travel. We find that communities indeed have distinct styles. Also, style is an excellent predictor of group membership (F-score 0.952 and Accuracy 96.09%). While on average it is statistically equivalent to predictions using content alone, it is more resilient to reductions in training data.
What Makes People Feel Close to Online Groups? The Roles of Group Attributes and Group Types
Authors: Kraut Robert E. , Levine John M. , Escobar Marisol Martinez , Herdağdelen Amaç
Published: November 2022

Most research assumes that the determinants of members’ feelings of connection to groups are constant across types of groups. The current paper challenges this assumption by assessing members' feelings of affinity toward a large, diverse sample of online groups. 10,567 members of 6,458 Facebook groups reported on their feelings of connection to these groups. Objectively measured group characteristics and features of members' relationship to the groups explained over 16% of the variance in members’ affinity. Being an administrator and being in groups with fewer members, more even communication, and more close friends were the strongest predictors. Half of the independent variables significantly interacted with group type in predicting affinity (e.g., large group size was negatively associated with affinity in task groups and positively associated with affinity in topical groups).
Quasi-Experimental Designs for Assessing Response on Social Media to Policy Changes
Authors: Tian Yijun , Chunara Rumi
Published: November 2022

Regulation of tobacco products is rapidly evolving. Understanding public sentiment in response to changes is very important as authorities assess how to effectively protect population health. Social media systems are widely recognized to be useful for collecting data about human preferences and perceptions. However, how social media data may be used, in rapid policy change settings, given challenges of narrow time periods and specific locations and non-representative the population using social media is an open question. In this paper we apply quasi-experimental designs, which have been used previously in observational data such as social media, to control for time and location confounders on social media, and then use content analysis of Twitter and Reddit posts to illustrate the content of reactions to tobacco flavor bans and the effect of taxation on e-cigarettes. Conclusions distill the potential role of social media in settings of rapidly changing regulation, in complement to what is learned by traditional denominator-based representative surveys.
Local Trends in Global Music Streaming
Authors: Way Samuel F. , Garcia-Gathright Jean , Cramer Henriette
Published: November 2022

Audio streaming services have made it easier for countries around the world to listen to each other's music. This expansion in listeners' access to global content, however, has raised questions about streaming's impact on the import and export flows of music between countries and their preferences for local or global content. Here, we analyze five and a half years of all streaming data from Spotify, a global music streaming service, and find that preferences for local content have increased from 2014 through 2019, reversing previously noted trends. Perhaps correspondingly, both common official language and geographic proximity between countries increasingly shape listener consumption during this period, particularly for younger audiences. Further, we show that these trends persist across different genres, listener age groups, and early- and late-adopters of streaming, providing new insights into this newest phase in the continued evolution of music and its impact on listeners around the world.
WikiHist.html: English Wikipedia's Full Revision History in HTML Format
Authors: Mitrevski Blagoj , Piccardi Tiziano , West Robert
Published: November 2022

Wikipedia is written in the wikitext markup language. When serving content, the MediaWiki software that powers Wikipedia parses wikitext to HTML, thereby inserting additional content by expanding macros (templates and modules). Hence, researchers who intend to analyze Wikipedia as seen by its readers should work with HTML, rather than wikitext. Since Wikipedia's revision history is publicly available exclusively in wikitext format, researchers have had to produce HTML themselves, typically by using Wikipedia's REST API for ad-hoc wikitext-to-HTML parsing. This approach, however, (1) does not scale to very large amounts of data and (2) does not correctly expand macros in historical article revisions. We solve these problems by developing a parallelized architecture for parsing massive amounts of wikitext using local instances of MediaWiki, enhanced with the capacity of correct historical macro expansion. By deploying our system, we produce and release WikiHist.html, English Wikipedia's full revision history in HTML format. We highlight the advantages of WikiHist.html over raw wikitext in an empirical analysis of Wikipedia's hyperlinks, showing that over half of the wiki links present in HTML are missing from raw wikitext, and that the missing links are important for user navigation. Data and code are publicly available at https://doi.org/10.5281/zenodo.3605388.
Mining Archive.org’s Twitter Stream Grab for Pharmacovigilance Research Gold
Authors: Tekumalla Ramya , Rafiei Asl Javad , Banda Juan M.
Published: November 2022

In the last few years, Twitter has become an important resource for the identification of Adverse Drug Reactions (ADRs), monitoring flu trends, and other pharmacovigilance and general research applications. Most researchers spend their time crawling Twitter, buying expensive pre-mined datasets, or tediously and slowly building datasets using the limited Twitter API. However, there are a large number of datasets that are publicly available to researchers that are underutilized or unused. In this work, we demonstrate how we mined over 9.4 billion Tweets from archive.org’s Twitter stream grab using a drug-term dictionary and plenty of computing power. Knowing that not everything that shines is gold, we used pre-existing drug-related datasets to build machine learning models to filter our findings for relevance. In this work, we present our methodology and the 3,346,758 identified tweets for public use in future research.
On the Splitting Dynamics of Meetup Social Groups
Authors: Bhowmick Ayan Kumar , Pramanik Soumajit , Pathak Sayan , Mitra Bivas
Published: November 2022

Groups in online social networks witness continuous evolution by loss of existing members and gain of new members. In this paper, we present a study of group split in Meetup, where a major fraction of members leave the existing group together and join a newly formed group. We identify pivotal group members, called splitters, playing key roles in group split by influencing the existing members to leave the group. We provide an in-depth analysis of the empirical data to reveal key motivating factors leading to a group split and its subsequent impact. Finally, we develop a prediction model for early detection of splitters, as well as the group members likely to be influenced by the splitter to leave the group.
Realtime Predictive Patrolling and Routing with Mobility and Emergency Calls Data
Authors: Rumi Shakila Khan , Shao Wei , Salim Flora D.
Published: November 2022

A well-planned patrol route plays a crucial role in increasing public security. Most of the existing studies designed the patrol route in a static manner. Situations when rerouting of patrol path are required due to the emergencies, e.g., an accident or ongoing homicide, are not considered. In this paper, we formulate the crime patrol routing problem jointly with dynamic crime event prediction, utilising crowdsourced check-in and real-time emergency call data. The extensive experiment on real-world datasets verifies the effectiveness of the proposed dynamic crime patrol route using different evaluation metrics.
The Political Dashboard: A Tool for Online Political Transparency
Authors: Medina Serrano Juan Carlos , Papakyriakopoulos Orestis , Shahrezaye Morteza , Hegelich Simon
Published: November 2022

Contemporary political communication is a multi- and cross-platform process. Because of its complexity, new tools are necessary to monitor and understand it. We present a system that ingests, stores, and processes political data from Twitter, Facebook, and online news articles. We visualize the data in the form of a freely accessible online dashboard. The political dashboard (https://political-dashboard.com/) aims to provide online political transparency and assist researchers, journalists, and the general public in understanding the German online political landscape.
An Analysis of Replies to Trump’s Tweets
Authors: An Zijian , Joseph Kenneth
Published: November 2022

Donald Trump has tweeted thousands of times during his presidency. These public statements are an increasingly important way through which Trump communicates his political and personal views. A better understanding of the way the American public consumes and responds to these tweets is therefore critical. In the present work, we address both consumption of and response to Trump's tweets by studying replies to them on Twitter. With respect to response, we find that a small number of older, white, left-leaning, and female Americans are responsible for the vast majority of replies to Trump's tweets. These individuals also attend to a broader range of Trump's tweets than the rest of the individuals we study. With respect to consumption, we note that Trump's tweets are often viewed not in isolation, but rather in the context of a set of algorithmically-curated replies. These replies may therefore color the way Americans consume Trump's tweets. To this end, we find some evidence that Twitter accounts see replies in line with their political leanings. However, we show that this can be largely, although not entirely, attributed to the fact that Twitter is more likely to show replies by accounts a user follows. As a basis for comparison, all results for Trump are compared and contrasted with replies to Joe Biden's tweets.
Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques
Authors: Kim Taehyun , Shin Hyomin , Hwang Hyung Ju , Jeong Seungwon
Published: November 2022

Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatically and get rewards, which discourages real human users from creating good content. We call this type of bot a posting bot. While there are many papers that studied bots on traditional centralized social media platforms such as Facebook and Twitter, we are the first to study posting bots on a blockchain-based social media platform. Compared with the bot detection on the usual social media platforms, the features we created have an advantage that posting bots can be detected without limiting the number or length of posts. We can extract the features of posts by clustering distances between blog data or replies. These features are obtained from the Minimum Average Cluster from Clustering Distance between Frequent words and Articles (MAC-CDFA), which is not used in any of the previous social media research. Based on the enriched features, we enhanced the quality of classification tasks. Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.
Towards Emotion- and Time-Aware Classification of Tweets to Assist Human Moderation for Suicide Prevention
Authors: Sawhney Ramit , Joshi Harshit , Nobles Alicia , Shah Rajiv Ratn
Published: November 2022

Social media platforms are already engaged in leveraging existing online socio-technical systems to employ just-in-time interventions for suicide prevention to the public. These efforts primarily rely on self-reports of potential self-harm content that is reviewed by moderators. Most recently, platforms have employed automated models to identify self-harm content, but acknowledge that these automated models still struggle to understand the nuance of human language (e.g., sarcasm). By explicitly focusing on Twitter posts that could easily be misidentified by a model as expressing suicidal intent (i.e., they contain similar phrases such as ``wanting to die''), our work examines the temporal differences in historical expressions of general and emotional language prior to a clear expression of suicidal intent. Additionally, we analyze time-aware neural models that build on these language variants and factors in the historical, emotional spectrum of a user's tweeting activity. The strongest model achieves high (statistically significant) performance (macro F1=0.804, recall=0.813) to identify social media indicative of suicidal intent. Using three use cases of tweets with phrases common to suicidal intent, we qualitatively analyze and interpret how such models decided if suicidal intent was present and discuss how these analyses may be used to alleviate the burden on human moderators within the known constraints of how moderation is performed (e.g., no access to the user's timeline). Finally, we discuss the ethical implications of such data-driven models and inferences about suicidal intent from social media. Content warning: this article discusses self-harm and suicide.
The Healthy States of America: Creating a Health Taxonomy with Social Media
Authors: Šćepanović Sanja , Aiello Luca Maria , Zhou Ke , Joglekar Sagar , Quercia Daniele
Published: November 2022

Since the uptake of social media, researchers have mined online discussions to track the outbreak and evolution of specific diseases or chronic conditions such as influenza or depression. To broaden the set of diseases under study, we developed a Deep Learning tool for Natural Language Processing that extracts mentions of virtually any medical condition or disease from unstructured social media text. With that tool at hand, we processed Reddit and Twitter posts, analyzed the clusters of the two resulting co-occurrence networks of conditions, and discovered that they correspond to well-defined categories of medical conditions. This resulted in the creation of the first comprehensive taxonomy of medical conditions automatically derived from online discussions. We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22. Based on the mentions of our taxonomy's sub-categories on Reddit posts geo-referenced in the U.S., we were then able to compute disease-specific health scores. As opposed to counts of disease mentions or counts with no knowledge of our taxonomy's structure, we found that our disease-specific health scores are causally linked with the officially reported prevalences of 18 conditions.
Textual Analysis and Timely Detection of Suspended Social Media Accounts
Authors: Seyler Dominic , Tan Shulong , Li Dingcheng , Zhang Jingyuan , Li Ping
Published: November 2022

Suspended accounts are high-risk accounts that violate the rules of a social network. These accounts contain spam, offensive and explicit language, among others, and are incredibly variable in terms of textual content. In this work, we perform a detailed linguistic and statistical analysis into the textual information of suspended accounts and show how insights from our study significantly improve a deep-learning-based detection framework. Moreover, we investigate the utility of advanced topic modeling for the automatic creation of word lists that can discriminate suspended from regular accounts. Since early detection of these high-risk accounts is crucial, we evaluate multiple state-of-the-art classification models along the temporal dimension by measuring the minimum amount of textual signal needed to perform reliable predictions. Further, we show that the best performing models are able to detect suspended accounts earlier than the social media platform.