bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Search Queries to Understand Health Information Needs in Africa
Authors: Abebe Rediet , Hill Shawndra , Vaughan Jennifer Wortman , Small Peter M. , Schwartz H. Andrew
Published: November 2022

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding health information needs of people. Without understanding people’s everyday concerns, health organizations and policymakers are less able to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data to uncover and gain insight into health information needs of individuals in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common topics, revealing a widespread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and gender) and country. Using finergrained data, we also uncover discrepancies in the quality of content returned by search engines to users by topic and highlight differences in user behavior and satisfaction. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and off-line.
Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity
Authors: Carton Samuel , Mei Qiaozhu , Resnick Paul
Published: November 2022

We present an experimental assessment of the impact of feature attribution-style explanations on human performance in predicting the consensus toxicity of social media posts with advice from an unreliable machine learning model. By doing so we add to a small but growing body of literature inspecting the utility of interpretable machine learning in terms of human outcomes. We also evaluate interpretable machine learning for the first time in the important domain of online toxicity, where fully-automated methods have faced criticism as being inadequate as a measure of toxic behavior.We find that, contrary to expectations, explanations have no significant impact on accuracy or agreement with model predictions, through they do change the distribution of subject error somewhat while reducing the cognitive burden of the task for subjects. Our results contribute to the recognition of an intriguing expectation gap in the field of interpretable machine learning between the general excitement the field has engendered and the ambiguous results of recent experimental work, including this study.
Understanding Visual Memes: An Empirical Analysis of Text Superimposed on Memes Shared on Twitter
Authors: Du Yuhao , Masood Muhammad Aamir , Joseph Kenneth
Published: November 2022

Visual memes have become an important mechanism through which ideologically potent and hateful content spreads on today's social media platforms. At the same time, they are also a mechanism through which we convey much more mundane things, like pictures of cats with strange accents. Little is known, however, about the relative percentage of visual memes shared by real people that fall into these, or other, thematic categories. The present work focuses on visual memes that contain superimposed text. We carry out the first large-scale study on the themes contained in the text of these memes, which we refer to as image-with-text memes. We find that 30% of the image-with-text memes in our sample which have identifiable themes are politically relevant, and that these politically relevant memes are shared more often by Democrats than Republicans. We also find disparities in who expresses themselves via image-with-text memes, and images in general, versus other forms of expression on Twitter. The fact that some individuals use images with text to express themselves, instead of sending a plain text tweet, suggests potential consequences for the representativeness of analyses that ignore text contained in images.
Driving the Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks
Authors: Lamba Hemank , Srikanth Shashank , Pailla Dheeraj Reddy , Singh Shwetanshu , Juneja Karandeep Singh , Kumaraguru Ponnurangam
Published: November 2022

In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions - often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.
See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data
Authors: Mann Paulo , Paes Aline , Matsushima Elton H.
Published: November 2022

Mental disorders such as depression and anxiety have been increasing at alarming rates in the worldwide population. Notably, the major depressive disorder has become a common problem among higher education students, aggravated, and maybe even occasioned, by the academic pressures they must face. While the reasons for this alarming situation remain unclear (although widely investigated), the student already facing this problem must receive treatment. To that, it is first necessary to screen the symptoms. The traditional way for that is relying on clinical consultations or answering questionnaires. However, nowadays, the data shared at social media is a ubiquitous source that can be used to detect the depression symptoms even when the student is not able to afford or search for professional care. Previous works have already relied on social media data to detect depression on the general population, usually focusing on either posted images or texts or relying on metadata. In this work, we focus on detecting the severity of the depression symptoms in higher education students, by comparing deep learning to feature engineering models induced from both the pictures and their captions posted on Instagram. The experimental results show that students presenting a BDI score higher or equal than 20 can be detected with 0.92 of recall and 0.69 of precision in the best case, reached by a fusion model. Our findings show the potential of large-scale depression screening, which could shed light upon students at-risk.
Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018
Authors: Recuero Raquel , Soares Felipe Bonow , Gruzd Anatoliy
Published: November 2022

This paper examines the role of hyperpartisanship and polarization on Twitter during the 2018 Brazilian Presidential Election. Based on a mixed-methods approach, we collected and analyzed a dataset of over 8 million tweets about Jair Bolsonaro, a far-right candidate from the Social Liberty Party. Our results show that there is a strong connection between polarization, hyperpartisanship and disinformation. As the centrality of hyperpartisan outlets on Twitter grew, more traditional media outlets became less central and conversations became more polarized. We also confirmed that hyperpartisan outlets often shared disinformation or biased information, presented as a “truth-telling” alternative to journalistic outlets. And while disinformation was more frequently observed in the far-right group, it was also present in the anti-Bolsonaro cluster, especially towards the runoff period.
Characterizing the Social Media News Sphere through User Co-Sharing Practices
Authors: Samory Mattia , Kesiz Abnousi Vartan , Mitra Tanushree
Published: November 2022

We describe the landscape of news sources which share social media audience. We focus on 639 news sources, both credible and questionable, and characterize them according to the audience that shares their articles on Twitter. Based on user co-sharing practices, what communities of news sources emerge? We find four groups: one is home to mainstream, high-circulation sources from all sides of the political spectrum; one to satirical, left-leaning sources; one to bipartisan conspiratorial, pseudo-scientific sources; and one to right-leaning, deliberate misinformation sources. Next, we measure which assessments of credibility, impartiality, and journalistic integrity correspond to social media readers' choices of news sources, and uncover the multifaceted structure of the social news sphere. We show how news articles shared on Twitter differ across the four groups along linguistic and psycholinguistics measures. Further, we find that with a high degree of accuracy (∼80%), we can classify in what news community an article belongs to. Our data-driven categorization of news sources will help to navigate the complex landscape of online news and has implications for social media platform maintainers to reliably triage questionable outlets.
Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events
Authors: Stewart Ian , Yang Diyi , Eisenstein Jacob
Published: November 2022

Social media datasets make it possible to rapidly quantify collective attention to emerging topics and breaking news, such as crisis events. Collective attention is typically measured by aggregate counts, such as the number of posts that mention a name or hashtag. But according to rationalist models of natural language communication, the collective salience of each entity will be expressed not only in how often it is mentioned, but in the form that those mentions take. This is because natural language communication is premised on (and customized to) the expectations that speakers and writers have about how their messages will be interpreted by the intended audience. We test this idea by conducting a large-scale analysis of public online discussions of breaking news events on Facebook and Twitter, focusing on five recent crisis events. We examine how people refer to locations, focusing specifically on contextual descriptors, such as “San Juan” versus “San Juan, Puerto Rico.” Rationalist accounts of natural language communication predict that such descriptors will be unnecessary (and therefore omitted) when the named entity is expected to have high prior salience to the reader. We find that the use of contextual descriptors is indeed associated with proxies for social and informational expectations, including macro-level factors like the location's global salience and micro-level factors like audience engagement. We also find a consistent decrease in descriptor context use over the lifespan of each crisis event. These findings provide evidence about how social media users communicate with their audiences, and point towards more fine-grained models of collective attention that may help researchers and crisis response organizations to better understand public perception of unfolding crisis events.
Understanding the Political Ideology of Legislators from Social Media Images
Authors: Xi Nan , Ma Di , Liou Marcus , Steinert-Threlkeld Zachary C. , Anastasopoulos Jason , Joo Jungseock
Published: November 2022

In this paper, we seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U.S. House and Senate. In the era of social media, politics has become saturated with imagery, a potent and emotionally salient form of political rhetoric which has been used by politicians and political organizations to influence public sentiment and voting behavior for well over a century. To date, however, little is known about how images are used as political rhetoric. Using deep learning techniques to automatically predict Republican or Democratic party affiliation solely from the Facebook photographs of the members of the 114th U.S. Congress, we demonstrate that predicted class probabilities from our model function as an accurate proxy of the political ideology of images along a left–right (liberal–conservative) dimension. After controlling for the gender and race of politicians, our method achieves an accuracy of 59.28% from single photographs and 82.35% when aggregating scores from multiple photographs (up to 150) of the same person. To better understand image content distinguishing liberal from conservative images, we also perform in-depth content analyses of the photographs. Our findings suggest that conservatives tend to use more images supporting status quo political institutions and hierarchy maintenance, featuring individuals from dominant social groups, and displaying greater happiness than liberals.
A Quantitative Approach to Understanding Online Antisemitism
Authors: Zannettou Savvas , Finkelstein Joel , Bradlyn Barry , Blackburn Jeremy
Published: November 2022

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand these troubling trends.In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million posts and images from alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the “Unite the Right” rally in Charlottesville. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic “Happy Merchant” meme, and in particular how these fringe communities influence its propagation to more mainstream communities like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of hate online.
A Benchmark Dataset of Check-Worthy Factual Claims
Authors: Arslan Fatma , Hassan Naeemul , Li Chengkai , Tremayne Mark
Published: November 2022

In this paper we present the ClaimBuster dataset of 23,533 statements extracted from all U.S. general election presidential debates and annotated by human coders. The ClaimBuster dataset can be leveraged in building computational methods to identify claims that are worth fact-checking from the myriad of sources of digital or traditional media. The ClaimBuster dataset is publicly available to the research community, and it can be found at http://doi.org/10.5281/zenodo.3609356.
The Pushshift Telegram Dataset
Authors: Baumgartner Jason , Zannettou Savvas , Squire Megan , Blackburn Jeremy
Published: November 2022

Messaging platforms, especially those with a mobile focus, have become increasingly ubiquitous in society. These mobile messaging platforms can have deceivingly large user bases, and in addition to being a way for people to stay in touch, are often used to organize social movements, as well as a place for extremists to congregate.In this paper, we present a dataset from one such mobile messaging platform: Telegram. Our dataset is made up of over 27.8K channels and 317M messages from 2.2M unique users. To the best of our knowledge, our dataset comprises the largest and most complete of its kind. In addition to the raw data, we also provide the source code used to collect it, allowing researchers to run their own data collection instance. We believe the Pushshift Telegram dataset can help researchers from a variety of disciplines interested in studying online social movements, protests, political extremism, and disinformation.
The Long-Running Debate about Brexit on Social Media
Authors: Calisir Emre , Brambilla Marco
Published: November 2022

Online social media platforms have become a major place where people also discuss their opinions and express their feelings about socio-political phenomena such as elections and referendums. Human-generated online content is a fruitful resource for a deeper understanding of these happenings. In this study, we present a dataset comprising 45 months (from January 2016 until September 2019) of long-running discussions on Twitter about the Brexit referendum, which can be used by social scientists and journalists for understanding the evolution of the public debate about the phenomenon. This dataset comprises 50.8 million tweets and 3.97 million users, and is also enriched with additional meta-data attributes: bot score of users, sentiment information detected by our sentiment analyzer, political stance information predicted by our stance classifier. Considering all Brexit related tweets of users during our time period, we also determine their overall stance and sentiment.
The Media Coverage of the 2020 US Presidential Election Candidates through the Lens of Google's Top Stories
Authors: Kawakami Anna , Umarova Khonzodakhon , Mustafaraj Eni
Published: November 2022

Choosing the political party nominees, who will appear on the ballot for the US presidency, is a long process that starts two years before the general election. The news media plays a particular role in this process by continuously covering the state of the race. How can this news coverage be characterized? Given that there are thousands of news organizations, but each of us is exposed to only a few of them, we might be missing most of it. Online news aggregators, which aggregate news stories from a multitude of news sources and perspectives, could provide an important lens for the analysis. One such aggregator is Google's Top stories, a recent addition to Google's search result page. For the duration of 2019, we have collected the news headlines that Google Top stories has displayed for 30 candidates of both US political parties. Our dataset contains 79,903 news story URLs published by 2,168 unique news sources. Our analysis indicates that despite this large number of news sources, there is a very skewed distribution of where the Top stories are originating, with a very small number of sources contributing the majority of stories. We are sharing our dataset1 so that other researchers can answer questions related to algorithmic curation of news as well as media agenda setting in the context of political elections.
Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board
Authors: Papasavva Antonis , Zannettou Savvas , De Cristofaro Emiliano , Stringhini Gianluca , Blackburn Jeremy
Published: November 2022

This paper presents a dataset with over 3.3M threads and 134.5M posts from the Politically Incorrect board (/pol/) of the imageboard forum 4chan, posted over a period of almost 3.5 years (June 2016-November 2019). To the best of our knowledge, this represents the largest publicly available 4chan dataset, providing the community with an archive of posts that have been permanently deleted from 4chan and are otherwise inaccessible. We augment the data with a set of additional labels, including toxicity scores and the named entities mentioned in each post. We also present a statistical analysis of the dataset, providing an overview of what researchers interested in using it can expect, as well as a simple content analysis, shedding light on the most prominent discussion topics, the most popular entities mentioned, and the toxicity level of each post. Overall, we are confident that our work will motivate and assist researchers in studying and understanding 4chan, as well as its role on the greater Web. For instance, we hope this dataset may be used for cross-platform studies of social media, as well as being useful for other types of research like natural language processing. Finally, our dataset can assist qualitative work focusing on in-depth case studies of specific narratives, events, or social theories.
Aligning Public Feedback to Requests for Comments on Regulations.gov
Authors: Wadhwa Manya , Amir Silvio , Dredze Mark
Published: November 2022

In an effort to democratize the regulatory process, the United States Federal government created regulations.gov, a portal through which federal agencies can share proposed regulations and solicit feedback from the public. A proposed regulation will contain several requests for feedback on specific topics, and the public can then submit comments in response. While this reduces barriers to soliciting feedback, it still leaves regulators with a challenge: how to produce a summary and incorporate feedback from the sometimes tens of thousands of submitted comments. We propose an information retrieval system by which comments are aligned to specific regulatory requests. We evaluate several measures of semantic similarity for matching comments to information requests. We evaluate our proposed system over a dataset containing several regulations proposed for electronic cigarettes, an issue that energized tens of thousands of comments in response1.
BotSlayer: DIY Real-Time Influence Campaign Detection
Authors: Hui Pik-Mai , Yang Kai-Cheng , Torres-Lugo Christopher , Menczer Filippo
Published: November 2022

BotSlayer is an application that helps track and detect potential manipulation of information spreading on Twitter. It can be used by journalists, corporations, political candidates, and civil society organizations to discover online coordinated campaigns in real time. BotSlayer uses an anomaly detection algorithm to flag hashtags, links, accounts, phrases, and media that are trending and amplified in a coordinated fashion by likely bots. A Web dashboard lets users explore the tweets and accounts associated with suspicious campaigns, visualize their spread, and search related content on multiple search engines and social media platforms. BotSlayer is easily installed and configured in the cloud. It will aid in the study and early detection of social media manipulation phenomena.
CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms
Authors: Chowdhury Farhan Asif , Liu Yozen , Saha Koustuv , Vincent Nicholas , Neves Leonardo , Shah Neil , Bos Maarten W.
Published: November 2022

To improve the user experience as well as business outcomes, social platforms aim to predict user behavior. To this end, recurrent models are often used to predict a user's next behavior based on their most recent behavior. However, people have habits and routines, making it plausible to predict their behavior from more than just their most recent activity. Our work focuses on the interplay between ephemeral and cyclical components of user behaviors. By utilizing user activity data from social platform Snapchat, we uncover cyclic and ephemeral usage patterns on a per user level. Based on our findings, we imbued recurrent models with awareness: we augment an RNN with a cyclic module to complement traditional RNNs that model ephemeral behaviors and allow a flexible weighting of the two for the prediction task. We conducted extensive experiments to evaluate our model's performance on four user behavior prediction tasks on the Snapchat platform. We achieve improved results on each task compared against existing methods, using this simple, but important insight in user behavior: Both cyclical and ephemeral components matter. We show that in some situations and for some people, ephemeral components may be more helpful for predicting behavior, while for others and in other situations, cyclical components may carry more weight.
Multilayer Graph Association Rules for Link Prediction
Authors: Coscia Michele , Szell Michael
Published: November 2022

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multilayer link prediction we also ask: in which layer? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multilayer link prediction. We derive such rules by identifying all frequent patterns in a network via multilayer graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multilayer link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. In our experiments, we show that, exploiting graph association rules, we are able to achieve a prediction performance close to an ideal ensemble classifier. Further, we perform a case study on a signed multilayer network, showing how graph association rules can provide valuable insights to extend social balance theory.
Discovering and Categorising Language Biases in Reddit
Authors: Ferrer Xavier , Van Nuenen Tom , Such Jose M. , Criado Natalia
Published: November 2022

We present a data-driven approach using word embeddings to discover and categorise language biases on the discussion platform Reddit. As spaces for isolated user communities, platforms such as Reddit are increasingly connected to issues of racism, sexism and other forms of discrimination, signalling the need to monitor the language of these groups. One of the most promising AI approaches to trace linguistic biases in large textual datasets involves word embeddings, which transform text into high-dimensional dense vectors and capture semantic relations between words. Yet, previous studies require predefined sets of potential biases to study, e.g., whether gender is more or less associated with particular types of jobs. This makes these approaches unfit to deal with smaller and community-centric datasets such as those on Reddit, which contain smaller vocabularies and slang, as well as biases that may be particular to that community. This paper proposes a data-driven approach to automatically discover language biases encoded in the vocabulary of online discourse communities on Reddit. In our approach, protected attributes are connected to evaluative words found in the data, which are then categorised through a semantic analysis system. We verify the effectiveness of our method by comparing the biases we discover in the Google News dataset with those found in previous literature. We then successfully discover gender bias, religion bias, and ethnic bias in different Reddit communities. We conclude by discussing potential application scenarios and limitations of this data-driven bias discovery method.
How Medical Crowdfunding Helps People? A Large-scale Case Study on the Waterdrop Fundraising
Authors: Huang Junjie , Shen Huawei , Cao Qi , Cai Li , Cheng Xueqi
Published: November 2022

While online medical crowdfunding achieved tremendous success, quantitative study about whether and how medical crowdfunding helps people remains little explored.
In this paper, we empirically study how online medical crowdfunding helps people using more than 27,000 fundraising cases in Waterdrop Fundraising, one of the most popular online medical crowdfunding platforms in China.
We find that the amount of money obtained by fundraisers is broadly distributed, i.e., a majority of lowly donated cases coexist with a handful of very successful cases. We further investigate the factors that potentially correlate with the success of medical fundraising cases.
Profile information of fundraising cases, e.g., geographic information of fundraisers, affects the donated amounts, since detailed description may increase the credibility of a fundraising case.
One prominent finding lies in the effect of social network on the success of fundraising cases: the spread of fundraising information along social network is a key factor of fundraising success, and the social capital of fundraisers play an important role in fundraising.
Finally, we conduct prediction of donations using machine learning models, verifying the effect of potential factors on the success of medical crowdfunding.
Altogether, this work presents a data-driven view of medical fundraising on the web and opens a door to understanding medical crowdfunding.
Deception Detection in Group Video Conversations using Dynamic Interaction Networks
Authors: Kumar Srijan , Bai Chongyang , Subrahmanian V.S. , Leskovec Jure
Published: November 2022

Predicting groups of people who are jointly deceptive is critical in settings such as sales pitches and negotiations. Past work on deception in videos focuses on detecting single deceivers and uses facial or visual features only. We propose the concept of Face-to Face Interaction Networks (FFINs) and Negative Interaction Networks (NINs) to model interactions within a group of people. The use of FFINs and NINs in this paper enables us to leverage network relations in predicting face-to-face deception for the first time. We will use a dataset of 185 videos from a deception-based game called Resistance for deception based prediction using FFINs. We first characterize the behavior of individuals, pairs, and groups of deceptive participants compared to non-deceptive participants. Our analysis reveals that less engaged deceivers are identified early. Moreover, pairs of deceivers tend to avoid mutual interaction and focus their attention on non-deceivers. In contrast, non-deceivers interact with everyone equally. We propose the notion of Negative Interaction Networks (NINs) and create a belief propagation neural net algorithm called BPNN based on dynamic FFINs and NINs to detect deceivers from videos that are just 1 minute long. We show that our method outperforms recent state-of-the-art computer vision, graph embedding, and ensemble methods by at least 20.9% AUROC in identifying deception from videos.
Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)
Authors: Rajadesingan Ashwin , Budak Ceren , Resnick Paul
Published: November 2022

Research on online political communication has primarily focused on content in explicitly political spaces. In this work, we set out to determine the amount of political talk missed using this approach. Focusing on Reddit, we estimate that nearly half of all political talk takes place in subreddits that host political content less than 25% of the time. In other words, cumulatively, political talk in non-political spaces is abundant. We further examine the nature of political talk and show that political conversations are less toxic in non-political subreddits. Indeed, the average toxicity of political comments replying to a out-partisan in non-political subreddits is less than even the toxicity of co-partisan replies in explicitly political subreddits.
On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation
Authors: Silva Amila , Lo Pei-Chi , Lim Ee Peng
Published: November 2022

Personal values have significant influence on individuals' behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users' personal values, and developing effective models to predict their personal values using their Facebook data. These models leverage on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations among personal values. The LIWC word categories are adapted to non-English word use in Singapore. We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross stitch layer model. Through experiments, we show that our proposed model predicts personal values with considerable improvement of accuracy over the previous works. Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.
Perceptions of Retrospective Edits, Changes, and Deletion on Social Media
Authors: Yılmaz Günce Su , Gasaway Fiona , Ur Blase , Mondal Mainack
Published: November 2022

Many social media sites permit users to delete, edit, anonymize, or otherwise modify past posts. These mechanisms enable users to protect their privacy, but also to essentially change the past. We investigate perceptions of the necessity and acceptability of these mechanisms. Drawing on boundary-regulation theories of privacy, we first identify how users who reshared or responded to a post could be impacted by its retrospective modification. These mechanisms can cause boundary turbulence by recontextualizing past content and limiting accountability. In contrast, not permitting modification can lessen privacy and perpetuate harms of regrettable content. To understand how users perceive these mechanisms, we conducted 15 semi-structured interviews. Participants deemed retrospective modification crucial for fixing past mistakes. Nonetheless, they worried about the potential for deception through selective changes or removal. Participants were aware retrospective modification impacts others, yet felt these impacts could be minimized through context-aware usage of markers and proactive notifications.
VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter
Authors: Abilov Anton , Hua Yiqing , Matatov Hana , Amir Ofra , Naaman Mor
Published: November 2022

The wide spread of unfounded election fraud claims surrounding the U.S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U.S. capitol. Under these circumstances, it is critical to understand the discussions surrounding these claims on Twitter, a major platform where the claims were disseminated. To this end, we collected and released the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and 25.6M retweets from 2.6M users related to voter fraud claims. To make this data immediately useful for a diverse set of research projects, we further enhance the data with cluster labels computed from the retweet graph, each user's suspension status, and the perceptual hashes of tweeted images. The dataset also includes aggregate data for all external links and YouTube videos that appear in the tweets. Preliminary analyses of the data show that Twitter's user suspension actions mostly affected a specific community of voter fraud claim promoters, and exposes the most common URLs, images and YouTube videos shared in the data.
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
Authors: Dutta Hridoy Sankar , Arora Udit , Chakraborty Tanmoy
Published: November 2022

The rise of online media has incentivized users to adopt various unethical and artificial ways of gaining social growth to boost their credibility within a short time period. In this paper, we introduce ABOME, a novel multi-platform data repository consisting of artificially boosted online media entities (also known as blackmarket-driven collusive entities), which are prevalent but often unnoticed in online media. ABOME allows quick querying of collusive entities across platforms. These include details of collusive entities involved in blackmarket services to gain artificially boosted appraisals in the form of likes, retweets, views, comments, follows and subscriptions. ABOME contains data related to tweets and users on Twitter, YouTube videos and YouTube channels. We believe that ABOME is a unique data repository which can be used as a benchmark to identify and analyze blackmarket-driven fraudulent activities in online media. We also develop SearchBM, an API and a web portal to identify blackmarket entities.
A Dataset of State-Censored Tweets
Authors: Elmas Tuğrulcan , Overdorf Rebekah , Aberer Karl
Published: November 2022

Many governments impose traditional censorship methods on social media platforms. Instead of removing it completely, many social media companies, including Twitter, only withhold the content from the requesting country. This makes such content still accessible outside of the censored region, allowing for an excellent setting in which to study government censorship on social media. We mine such content using the Internet Archive's Twitter Stream Grab. We release a dataset of 583,437 tweets by 155,715 users that were censored between 2012-2020 July. We also release 4,301 accounts that were censored in their entirety. Additionally, we release a set of 22,083,759 supplemental tweets made up of all tweets by users with at least one censored tweet as well as instances of other users retweeting the censored user. We provide an exploratory analysis of this dataset. Our dataset will not only aid in the study of government censorship but will also aid in studying hate speech detection and the effect of censorship on social media users. The dataset is publicly available at https://doi.org/10.5281/zenodo.4439509
A Research Agenda for Financial Opinion Mining
Authors: Chen Chung-Chi , Huang Hen-Hsen , Chen Hsin-Hsi
Published: November 2022

Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. In this position paper, we first define the financial opinions from both coarse-grained and fine-grained points of views, and then provide an overview of the issues already tackled. In addition to listing research issues of the existing topics, we further propose a road map of fine-grained financial opinion mining for future researches, and point out several challenges yet to explore. Moreover, we provide possible directions to deal with the proposed research issues.
Community Under Surveillance: Impacts of Marginalization on an Online Labor Forum
Authors: Barakat Hanna , Redmiles Elissa M.
Published: November 2022

A breadth of literature has examined how gig workers use online forums. The past literature focuses primarily on how gig workers for mainstream corporate platforms leverage forums. Yet, marginalization, stigma, censorship, and criminalization all shape how people, including gig workers, use digital technology. In this work, we seek to take a first step toward understanding how marginalized, stigmatized, digitally censored, and in some cases criminalized gig workers leverage the affordances of online public forums to build community and increase their welfare. To do so, we conduct a qualitative analysis of 4,000 posts and 25,851 comments shared over four months in a large online public forum used by sex workers for peer support. Sex workers sit at the intersection of multiple marginalized communities, and thus offer a lens into the broader use of forums by marginalized labor communities. Our findings offer insight into how these workers use moderation to preserve their safety and community. Further, we highlight similarities and differences between how this community utilizes the forum platform and prior scholarship on the use of online forums by gig workers.
Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control
Authors: Beel Jacob , Xiang Tong , Soni Sandeep , Yang Diyi
Published: November 2022

As public discourse continues to move and grow online, conversations about divisive topics on social media plat-forms have also increased. These divisive topics prompt both contentious and non-contentious conversations. Although what distinguishes these conversations, often framed as what makes these conversations contentious, is known in broad strokes, much less is known about the linguistic signature of these conversations. Prior work has shown that contentious content and structure can be a predictor for this task, however,most of them have been focused on conversation in general,very specific events, or complex structural analysis. Additionally, many models used in prior work have lacked interpretability, a key factor in online moderation. Our work fills these gaps by focusing on conversations from highly divisive topics (abortion, climate change, and gun control), operationalizing a set of novel linguistic and conversational characteristics and user factors, and incorporating them to build interpretable models. We demonstrate that such characteristics can largely improve the performance of prediction on this task, and also enable nuanced interpretability. Our case studies on these three contentious topics suggest that certain generic linguistic characteristics are highly correlated with contentiousness in conversations while others demonstrate significant contextual influences on specific divisive topics.
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms
Authors: Danilchenko Kiril , Segal Michael , Vilenchik Dan
Published: November 2022

E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate
and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is hard to identify opinion spammers automatically, including the absence of reliable labeled data. This limitation precludes an off-the-shelf application of a machine learning pipeline. We propose a new method for classifying reviewers as spammers or benign, combining machine learning with a message-passing algorithm that capitalizes on the users' graph structure to compensate for the possible scarcity of labeled data. We devise a new way of sampling the labels for the training step (active learning), replacing the typical uniform sampling. Experiments on three large real-world datasets from Yelp.com show that our method outperforms state-of-the-art active learning approaches and also machine learning methods that use a much larger set of labeled data for training.
On the Infrastructure Providers That Support Misinformation Websites
Authors: Han Catherine , Kumar Deepak , Durumeric Zakir
Published: November 2022

In this paper, we analyze the service providers that power 440 misinformation and hate sites, including hosting platforms, domain registrars, CDN providers, DDoS protection companies, advertising networks, donation processors, and e-mail providers. We find that several providers are disproportionately responsible for serving misinformation websites, most prominently Cloudflare. We further show that misinformation sites disproportionately rely on several popular ad networks and payment processors, including RevContent and Google DoubleClick. When misinformation websites are deplatformed by hosting providers, DDoS protection services, and registrars, sites nearly always resurface through alternative providers. However, anecdotally, we find that sites struggle to remain online when mainstream monetization channels are severed. We conclude with insights for infrastructure providers and researchers working to stem the spread of misinformation and hate content.
Twitter User Representation Using Weakly Supervised Graph Embedding
Authors: Islam Tunazzina , Goldwasser Dan
Published: November 2022

Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, this rapidly growing access has also increased the diverse information, and characterizing user types to understand people’s lifestyle decisions shared in social media is challenging. In this paper, we propose a weakly supervised graph embedding based framework for understanding user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on 'Yoga’, 'Keto diet’. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.