bluuebunny/crossref_metadata_2025 huggingface dataset:
"Bacon Bacon Bacon": Food-Related Tweets and Sentiment in Metro Detroit
Authors: Vydiswaran V. G. , Romero Daniel , Zhao Xinyan , Yu Deahan , Gomez-Lopez Iris , Lu Jin , Iott Bradley , Baylin Ana , Clarke Philippa , Berrocal Veronica , Goodspeed Robert , Veinot Tiffany
Published: November 2022

Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. In this paper, we examine the validity of Twitter as a source of information for analysis of dietary patterns and attitudes. We analyze the "healthiness" quotient of food-related tweets and sentiment regarding those tweets from metropolitan Detroit. Our findings demonstrate feasibility of using Twitter to understand neighborhood characteristics regarding food attitudes and potential use in studying neighborhood-based health disparities.
Tweeting AI: Perceptions of Lay versus Expert Twitterati
Authors: Manikonda Lydia , Kambhampati Subbarao
Published: November 2022

In light of the significant public interest in the AI technology and its impacts, in this research we set out to analyze the contours of public discourse and perceptions of AI, as reflected in the social media. We focus on Twitter, and analyze over two million AI related tweets posted by over 40,000 users. In addition to analyzing the macro characteristics of this whole discourse in terms of demographics, sentiment, and topics, we also provide a differential analysis of tweets from experts vs. non-experts, as well as a differential analysis of male vs. female tweeters. We see that (i) by and large the sentiments expressed in the AI discourse are more positive than is par for twitter (ii) that lay public tend to be more positive about AI than expert tweeters and (iii) that women tend to be more positive about AI impacts than men. Analysis of topics discussed also shows interesting differential patterns across experts vs. non-experts and men vs. women. For example, we see that women tend to focus a lot more on the ethical issues surrounding AI. Our analysis provides a far more nuanced picture of the public discourse on AI.
The Million Tweets Fallacy: Activity and Feedback Are Uncorrelated
Authors: Vilenchik Dan
Published: November 2022

In this paper we point to a surprising phenomenon in Online Social Networks (OSNs), which we call "The Million Tweets Fallacy". Our hypothesis is that the measurements of activity of a user in an OSN are not correlated with the measurements of feedback that the user receives on that activity. For example, the number of tweets is uncorrelated with the number of retweets or likes. In other words, a voluminously tweeting user is not necessarily fuelled by the attention he gets from his followers. An innovative aspect of this work is that we treat "activity" and "feedback" as multidimensional axes, and do not reduce the problem to a one-dimensional pairwise correlation problem. We apply our methodology to six OSNs. For Twitter, Instagram, LinkedIn and Steam we gathered the data ourselves, collecting features that cover both users' activity and feedback in the OSN. For YouTube and Flickr we used existing data from the literature. In all OSNs, with the only exception of Steam, we confirmed our hypothesis.
From the User to the Medium: Neural Profiling Across Web Communities
Authors: Akbari Mohammad , Relia Kunal , Elghafari Anas , Chunara Rumi
Published: November 2022

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.
"With 1 Follower I Must Be AWESOME :P." Exploring the Role of Irony Markers in Irony Recognition
Authors: Ghosh Debanjan , Muresan Smaranda
Published: November 2022

Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean. Irony markers are the meta-communicative clues that inform the reader that an utterance is ironic. We propose a thorough analysis of theoretically grounded irony markers in two social media platforms: Twitter and Reddit. Classification and frequency analysis shows that for Twitter, typographic markers such as emoticons and emojis are the most discriminative markers to recognize ironic utterances, while for Reddit the morphological markers (e.g., interjections, tag questions) are the most discriminative.
Linguistic Cues to Deception: Identifying Political Trolls on Social Media
Authors: Addawood Aseel , Badawy Adam , Lerman Kristina , Ferrara Emilio
Published: November 2022

The ease with which information can be shared on social media has opened it up to abuse and manipulation. One example of a manipulation campaign that has garnered much attention recently was the alleged Russian interference in the 2016 U.S. elections, with Russia accused of, among other things, using trolls and malicious accounts to spread misinformation and politically biased information. To take an in-depth look at this manipulation campaign, we collected a dataset of 13 million election-related posts shared on Twitter in 2016 by over a million distinct users. This dataset includes accounts associated with the identified Russian trolls as well as users sharing posts in the same time period on a variety of topics around the 2016 elections. To study how these trolls attempted to manipulate public opinion, we identified 49 theoretically grounded linguistic markers of deception and measured their use by troll and non-troll accounts. We show that deceptive language cues can help to accurately identify trolls, with average F1 score of 82% and recall 88%.
View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations
Authors: Aldous Kholoud Khalil , An Jisun , Jansen Bernard J.
Published: November 2022

We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and platforms. Finally, we show that one can predict if an article will be publicly shared to another platform by individuals with precision of approximately 80%. This research has implications for news organizations desiring to increase and to prioritize types of user engagement.
Political Discussions in Homogeneous and Cross-Cutting Communication Spaces
Authors: An Jisun , Kwak Haewoon , Posegga Oliver , Jungherr Andreas
Published: November 2022

Online platforms, such as Facebook, Twitter, and Reddit, provide users with a rich set of features for sharing and consuming political information, expressing political opinions, and exchanging potentially contrary political views. In such activities, two types of communication spaces naturally emerge: those dominated by exchanges between politically homogeneous users and those that allow and encourage crosscutting exchanges in politically heterogeneous groups. While research on political talk in online environments abounds, we know surprisingly little about the potentially varying nature of discussions in politically homogeneous spaces as compared to cross-cutting communication spaces. To fill this gap, we use Reddit to explore the nature of political discussions in homogeneous and cross-cutting communication spaces. In particular, we develop an analytical template to study interaction and linguistic patterns within and between politically homogeneous and heterogeneous communication spaces. Our analyses reveal different behavioral patterns in homogeneous and cross-cutting communications spaces. We discuss theoretical and practical implications in the context of research on political talk online.
Detecting Potential Warning Behaviors of Ideological Radicalization in an Alt-Right Subreddit
Authors: Grover Ted , Mark Gloria
Published: November 2022

Over the past few years, new ideological movements like the Alt-Right have captured the attention and concern of both mainstream media, policy makers, and scholars alike. Today, the methods by which right-wing extremists are radicalized are increasingly taking place within social media platforms and online communities. However, no research has yet investigated methods for proactively detecting online communities that may be displaying overall warning signs of mass ongoing ideological and political radicalization. In our work, we use a variety of text analysis methods to investigate the behavioral patterns of a radical right-wing community on Reddit (r/altright) over a 6-month period until right before it was banned for violation of Reddit terms of service. We find that this community showed aggregated behavioral patterns that aligned with past literature on warning behaviors of individual extremists in online environments, and that these behavioral patterns were not seen in a comparison group of eight other online political communities, similar in size and user engagement. Our research helps build upon the established literature on the detection of extremism in online environments, and has implications for proactive monitoring of online communities.
What Twitter Profile and Posted Images Reveal about Depression and Anxiety
Authors: Chandra Guntuku Sharath , Preotiuc-Pietro Daniel , Eichstaedt Johannes C. , Ungar Lyle H.
Published: November 2022

Previous work has found strong links between the choice of social media images and users’ emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users’ depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.
Gender Differences in the Global Music Industry: Evidence from MusicBrainz and The Echo Nest
Authors: Wang Yixue , Horvát Emőke-Ágnes
Published: November 2022

With digital music consumption being at an all-time high, online music encyclopedia like MusicBrainz and music intelligence platforms like The Echo Nest are becoming increasingly important in identifying, organizing, and recommending music for listeners around the globe. As a byproduct, such sites collect comprehensive information about a vast amount of artists, their recorded songs, institutional support, and the collaborations between them. Using a unique mash-up of crowdsourced, curated, and algorithmically augmented data, this paper unpacks an unsolved problem that is key to promoting artistic innovation, i.e., how gender penetrates into artistic context leading to the globally perceived gender gap in the music industry. Specifically, we investigate gender-related differences in the sonic features of artists’ work, artists’ tagging by listeners, their record label affiliations, and collaboration networks. We find statistically significant disparities along all these dimensions. Moreover, the differences allow models to reliably identify the gender of songs’ creators and help elucidate the role of cultural and structural factors in sustaining inequality. Our findings contribute to a better understanding of gender differences in music production and inspire strategies that could improve the recognition of female artists and advance gender equity in artistic leadership and innovation.
News Sharing User Behaviour on Twitter: A Comprehensive Data Collection of News Articles and Social Interactions
Authors: Brena Giovanni , Brambilla Marco , Ceri Stefano , Di Giovanni Marco , Pierri Francesco , Ramponi Giorgia
Published: November 2022

Online social media are changing the news industry and revolutionizing the traditional role of journalists and newspapers. In this scenario, investigating the behaviour of users in relationship to news sharing is relevant, as it provides means for understanding the impact of online news, their propagation within social communities, their impact on the formation of opinions, and also for effectively detecting individual stances relative to specific news or topics.Our contribution is two-fold. First, we build a robust pipeline for collecting datasets describing news sharing; the pipeline takes as input a list of news sources and generates a large collection of articles, of the accounts that provide them on the social media either directly or by retweeting, and of the social activities performed by these accounts. Second, we also provide a large-scale dataset, built using the aforementioned tool, that can be used to study the social behavior of Twitter users and their involvement in the dissemination of news items. Finally we show an application of our data collection in the context of political stance classification and we suggest other potential usages of the presented resources.
Wikipedia Cultural Diversity Dataset: A Complete Cartography for 300 Language Editions
Authors: Miquel-Ribé Marc , Laniado David
Published: November 2022

In this paper we present the Wikipedia Cultural Diversity dataset. For each existing Wikipedia language edition, the dataset contains a classification of the articles that represent its associated cultural context, i.e. all concepts and entities related to the language and to the territories where it is spoken. We describe the methodology we employed to classify articles, and the rich set of features that we defined to feed the classifier, and that are released as part of the dataset. We present several purposes for which we envision the use of this dataset, including detecting, measuring and countering content gaps in the Wikipedia project, and encouraging cross-cultural research in the field of digital humanities.
WhatsApp Monitor: A Fact-Checking System for WhatsApp
Authors: Melo Philipe , Messias Johnnatan , Resende Gustavo , Garimella Kiran , Almeida Jussara , Benevenuto Fabrício
Published: November 2022

WhatsApp is the most popular communication application in many developing countries such as Brazil, India, and Mexico, where many people use it as an interface to the web. Due to its encrypted and peer-to-peer nature feature, it is hard for researchers to study which content people share through WhatsApp at scale. In this demo paper, we propose WhatsApp Monitor (http://www.whatsapp-monitor.dcc.ufmg.br/), a web-based system that helps researchers and journalists explore the nature of content shared on WhatsApp public groups from two different contexts: Brazil and India. Our tool monitors multiple content categories such as images, videos, audio, and textual messages posted on a set of WhatsApp groups and displays the most shared content per day. Our tool has been used for monitoring content during the 2018 Brazilian general election and was one of the major sources for estimating the spread of misinformation and helping fact-checking efforts.
Social Media Relevance Filtering Using Perplexity-Based Positive-Unlabelled Learning
Authors: Kim Sunghwan Mac , Wan Stephen , Paris Cécile , Duenser Andreas
Published: November 2022

Internet user-generated data, like Twitter, offers data scientists a public real-time data source that can provide insights, supplementing traditional data. However, identifying relevant data for such analyses can be time-consuming. In this paper, we introduce our Perplexity variant of Positive-Unlabelled Learning (PPUL) framework as a means to perform social media relevance filtering. We note that this task is particularly well suited to a PU Learning approach. We demonstrate how perplexity can identify candidate examples of the negative class, using language models. To learn such models, we experiment with both statistical methods and a Variational Autoencoder. Our PPUL method generally outperforms strong PU Learning baselines, which we demonstrate on five different data sets: the Hazardous Product Review data set, two well known social media data sets, and two real case studies in relevance filtering. All datasets have manual annotations for evaluation, and, in each case, PPUL attains state-of-the-art performance, with gains ranging from 4 to 17% improvement over competitive baselines. We show that the PPUL framework is effective when the amount of positive annotated data is small, and it is appropriate for both content that is triggered by an event and a general topic of interest.
Bridging Qualitative and Quantitative Methods for User Modeling: Tracing Cancer Patient Behavior in an Online Health Community
Authors: Levonian Zachary , Erikson Drew Richard , Luo Wenqi , Narayanan Saumik , Rubya Sabirat , Vachher Prateek , Terveen Loren , Yarosh Svetlana
Published: November 2022

Researchers construct models of social media users to understand human behavior and deliver improved digital services. Such models use conceptual categories arranged in a taxonomy to classify unstructured user text data. In many contexts, useful taxonomies can be defined via the incorporation of qualitative findings, a mixed-methods approach that offers the ability to create qualitatively-informed user models. But operationalizing taxonomies from the themes described in qualitative work is non-trivial and has received little explicit focus. We propose a process and explore challenges bridging qualitative themes to user models, for both operationalization of themes to taxonomies and the use of these taxonomies in constructing classification models. For classification of new data, we compare common keyword-based approaches to machine learning models. We demonstrate our process through an example in the health domain, constructing two user models tracing cancer patient experience over time in an online health community. We identify patterns in the model outputs for describing the longitudinal experience of cancer patients and reflect on the use of this process in future research.
Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election
Authors: Luceri Luca , Giordano Silvia , Ferrara Emilio
Published: November 2022

Since the 2016 US Presidential election, social media abuse has been eliciting massive concern in the academic community and beyond. Preventing and limiting the malicious activity of users, such as trolls and bots, in their manipulation campaigns is of paramount importance for the integrity of democracy, public health, and more. However, the automated detection of troll accounts is an open challenge. In this work, we propose an approach based on Inverse Reinforcement Learning (IRL) to capture troll behavior and identify troll accounts. We employ IRL to infer a set of online incentives that may steer user behavior, which in turn highlights behavioral differences between troll and non-troll accounts, enabling their accurate classification. As a study case, we consider the troll accounts identified by the US Congress during the investigation of Russian meddling in the 2016 US Presidential election. We report promising results: the IRL-based approach is able to accurately detect troll accounts (AUC=89.1%). The differences in the predictive features between the two classes of accounts enables a principled understanding of the distinctive behaviors reflecting the incentives trolls and non-trolls respond to.
Examining Peer-to-Peer and Patient-Provider Interactions on a Social Media Community Facilitating Ask the Doctor Services
Authors: Nobles Alicia L. , Leas Eric C. , Dredze Mark , Ayers John W.
Published: November 2022

Ask the Doctor (AtD) services provide patients the opportunity to seek medical advice using online platforms. While these services represent a new mode of healthcare delivery, study of these online health communities and how they are used is limited. In particular, it is unknown if these platforms replicate existing barriers and biases in traditional healthcare delivery across demographic groups. We present an analysis of AskDocs, a subreddit that functions as a public AtD platform on social media. We examine the demographics of users, the health topics discussed, if biases present in offline healthcare settings exist on this platform, and how empathy is expressed in interactions between users and physicians. Our findings suggest a number of implications to enhance and support peer-to-peer and patient-provider interactions on online platforms.
An Experimental Study of Structural Diversity in Social Networks
Authors: Su Jessica , Kamath Krishna , Sharma Aneesh , Ugander Johan , Goel Sharad
Published: November 2022

Several recent studies of online social networking platforms have found that adoption rates and engagement levels are positively correlated with structural diversity, the degree of heterogeneity among an individual's contacts as measured by network ties. One common theory for this observation is that structural diversity increases utility, in part because there is value to interacting with people from different network components on the same platform. While compelling, evidence for this causal theory comes from observational studies, making it difficult to rule out non-causal explanations. We investigate the role of structural diversity on retention by conducting a large-scale randomized controlled study on the Twitter platform. We first show that structural diversity correlates with user retention on Twitter, corroborating results from past observational studies. We then exogenously vary structural diversity by altering the set of network recommendations new users see when joining the platform; we confirm that this design induces the desired changes to network topology. We find, however, that low, medium, and high structural diversity treatment groups in our experiment have comparable retention rates. Thus, at least in this case, the observed correlation between structural diversity and retention does not appear to result from a causal relationship, challenging theories based on past observational studies.
Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection
Authors: Vaidya Ameya , Mai Feng , Ning Yue
Published: November 2022

With the recent rise of toxicity in online conversations on social media platforms, using modern machine learning algorithms for toxic comment detection has become a central focus of many online applications. Researchers and companies have developed a variety of models to identify toxicity in online conversations, reviews, or comments with mixed successes. However, many existing approaches have learned to incorrectly associate non-toxic comments that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a potential source of toxicity. In this paper, we evaluate several state-of-the-art models with the specific focus of reducing model bias towards these commonly-attacked identity groups. We propose a multi-task learning model with an attention layer that jointly learns to predict the toxicity of a comment as well as the identities present in the comments in order to reduce this bias. We then compare our model to an array of shallow and deep-learning models using metrics designed especially to test for unintended model bias within these identity groups.
Pie Chart or Pizza: Identifying Chart Types and Their Virality on Twitter
Authors: Vougiouklis Pavlos , Carr Leslie , Simperl Elena
Published: November 2022

We aim to understand how data, rendered visually as charts or infographics, “travels” on social media. To do so we propose a neural network architecture that is trained to distinguish among different types of charts, for instance line graphs or scatter plots, and predict how much they will be shared. This poses significant challenges because of the varying format and quality of the charts that are posted, and the limitations in existing training data. To start with, our proposed system outperforms related work in chart type classification on the ReVision corpus. Furthermore, we use crowdsourcing to build a new corpus, more suitable to our aims, consisting of chart images shared by data journalists on Twitter. We evaluate our system on the second corpus with respect to both chart identification and virality prediction, with promising results.
Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on Twitter
Authors: Zannettou Savvas , Caulfield Tristan , Bradlyn Barry , De Cristofaro Emiliano , Stringhini Gianluca , Blackburn Jeremy
Published: November 2022

State-sponsored organizations are increasingly linked to efforts aimed to exploit social media for information warfare and manipulating public opinion. Typically, their activities rely on a number of social network accounts they control, aka trolls, that post and interact with other users disguised as “regular” users. These accounts often use images and memes, along with textual content, in order to increase the engagement and the credibility of their posts.In this paper, we present the first study of images shared by state-sponsored accounts by analyzing a ground truth dataset of 1.8M images posted to Twitter by accounts controlled by the Russian Internet Research Agency. First, we analyze the content of the images as well as their posting activity. Then, using Hawkes Processes, we quantify their influence on popular Web communities like Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab, with respect to the dissemination of images. We find that the extensive image posting activity of Russian trolls coincides with real-world events (e.g., the Unite the Right rally in Charlottesville), and shed light on their targets as well as the content disseminated via images. Finally, we show that the trolls were more effective in disseminating politics-related imagery than other images.
Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository
Authors: Dai Enyan , Sun Yiwei , Wang Suhang
Published: November 2022

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection.
A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections
Authors: Reis Julio C. S. , Melo Philipe , Garimella Kiran , Almeida Jussara M. , Eckles Dean , Benevenuto Fabrício
Published: November 2022

Recently, messaging applications, such as WhatsApp, have been reportedly abused by misinformation campaigns, especially in Brazil and India. A notable form of abuse in WhatsApp relies on several manipulated images and memes containing all kinds of fake stories. In this work, we performed an extensive data collection from a large set of WhatsApp publicly accessible groups and fact-checking agency websites. This paper opens a novel dataset to the research community containing fact-checked fake images shared through WhatsApp for two distinct scenarios known for the spread of fake news on the platform: the 2018 Brazilian elections and the 2019 Indian elections.
The Relative Value of Facebook Advertising Data for Poverty Mapping
Authors: Fatehkia Masoomali , Coles Benjamin , Ofli Ferda , Weber Ingmar
Published: November 2022

Having reliable and up-to-date poverty data is a prerequisite for monitoring the United Nations Sustainable Development Goals (SDGs) and for planning effective poverty reduction interventions. Unfortunately, traditional data sources are often outdated or lacking appropriate disaggregation. As a remedy, satellite imagery has recently become prominent in obtaining geographically-fine-grained and up-to-date poverty estimates. Satellite data can pick up signals of economic activity by detecting light at night, it can pick up development status by detecting infrastructure such as roads, and it can pick up signals for individual household wealth by detecting different building footprints and roof types. It can, however, not look inside the households and pick up signals from individuals. On the other hand, alternative data sources such as audience estimates from Facebook's advertising platform provide insights into the devices and internet connection types used by individuals in different locations. Previous work has shown the value of such anonymous, publicly-accessible advertising data from Facebook for studying migration, gender gaps, crime rates, and health, among others. In this work, we evaluate the added value of using Facebook data over satellite data for mapping socioeconomic development in two low and middle income countries – the Philippines and India. We show that Facebook features perform roughly similar to satellite data in the Philippines with value added for urban locations. In India, however, where Facebook penetration is lower, satellite data perform better.
The Effects of an Informational Intervention on Attention to Anti-Vaccination Content on YouTube
Authors: Kim Sangyeon , Yalcin Omer F. , Bestvater Samuel E. , Munger Kevin , Monroe Burt L. , Desmarais Bruce A.
Published: November 2022

The spread of misinformation related to health, especially vaccination, is a potential contributor to myriad public health problems. This misinformation is frequently spread through social media. Recently, social media companies have intervened in the dissemination of misinformation regarding vaccinations. In the current study we focus on YouTube. Recognizing the extent of the problem, YouTube implemented an informational modification that affected many videos related to vaccination beginning in February 2019. We collect original data and analyze the effects of this intervention on video viewership. We find that this informational intervention reduced traffic to the affected videos, both overall, and in comparison to a carefully-matched set of control videos that did not receive the informational modification.
Can Badges Foster a More Welcoming Culture on Q&A Boards?
Authors: Santos Tiago , Burghardt Keith , Lerman Kristina , Helic Denis
Published: November 2022

Thriving online communities rely on a steady stream of newcomers to contribute new content. However, retaining newcomers has proven challenging. In this paper, we measure the success of an intervention used by Stack Exchange question-answering communities to create a more welcoming environment for newcomers. That intervention consisted in highlighting contributions by new users with a special indicator. We hypothesize that Stack Exchange's new policy would reduce negative reactions to new users and, ultimately, increase new user retention. We leverage causal modeling to assess the introduction of the so-called “new contributor indicator”, and we find it did not counter user retention decline in the short- and long-terms. However, our results indicate it did reduce unwelcoming reactions towards newcomers in the short-term. Our work has practical implications for online community managers aiming to improve their onboarding processes.
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions
Authors: Adhikari Shishir , Uppal Akshay , Mermelstein Robin , Berger-Wolf Tanya , Zheleva Elena
Published: November 2022

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes.
It’s a Thin Line Between Love and Hate: Using the Echo in Modeling Dynamics of Racist Online Communities
Authors: Arviv Eyal , Hanouna Simo , Tsur Oren
Published: November 2022

The (((echo))) symbol - triple parentheses surrounding a name, made it to mainstream social networks in early 2016, with the intensification of the U.S. Presidential race. It was used by members of the alt-right, white supremacists and internet trolls to tag people of Jewish heritage - a modern incarnation of the infamous yellow badge (Judenstern) used in Nazi-Germany. Tracking this trending meme, its meaning, and its function has proved elusive for its semantic ambiguity (e.g., a symbol for a virtual hug).

In this paper we report of the construction of an appropriate dataset allowing the reconstruction of networks of racist communities and the way they are embedded in the broader community. We combine natural language processing and structural network analysis to study communities promoting hate. In order to overcome dog-whistling and linguistic ambiguity, we propose a multi-modal neural architecture based on a BERT transformer and a BiLSTM network on the tweet level, while also taking into account the users ego-network and meta features. Our multi-modal neural architecture outperforms a set of strong baselines. We further show how the use of language and network structure in tandem allows the detection of the leaders of the hate communities. We further study the "intersectionality" of hate and show that the antisemitic echo correlates with hate speech that targets other minority and protected groups. Finally, we analyze the role IRA trolls assumed in this network as part of the Russian interference campaign. Our findings allow a better understanding of recent manifestations of racism and the dynamics that facilitate it.
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
Authors: He Lihong , Shen Chen , Mukherjee Arjun , Vucetic Slobodan , Dragut Eduard
Published: November 2022

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that user-to-user interaction factors contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health).
Social Facilitation Among Gamblers: A Large-Scale Study Using Account-Based Data
Authors: Hopfgartner Niklas , Santos Tiago , Auer Michael , Griffiths Mark , Helic Denis
Published: November 2022

Previous research suggests that the presence of other gamblers in a gambling venue intensifies individual gambling behavior. To study such potential social facilitation among gamblers, we conduct a large-scale analysis of more than one million gambling sessions using player tracking data of the Norwegian gambling operator Norsk Tipping. In particular, we empirically assess the existence and strength of this facilitation, and how it manifests in differently utilized gambling venues. In our study, we control for (i) each individual's co-gamblers (frequent vs. occasional co-gamblers) and for (ii) the magnitude of individual's social participation (social seeking vs. social avoiding gamblers). We find that gamblers stake more money and play longer sessions the more crowded the venues get and that social avoiding gamblers gamble more when they play with their most-frequent co-gambler. Interestingly, our results strongly indicate that social avoiding gamblers are more susceptible to social facilitation than gamblers who are familiar with crowded gambling venues. Overall, our research is the first large-scale study of social facilitation among gamblers introducing a novel framework to empirically measure this effect. We believe that our work will have important practical implications for both gambling behavior researchers as well as the gambling industry in designing and evaluating responsible gambling tools.
The Evolution of the Manosphere across the Web
Authors: Horta Ribeiro Manoel , Blackburn Jeremy , Bradlyn Barry , De Cristofaro Emiliano , Stringhini Gianluca , Long Summer , Greenberg Stephanie , Zannettou Savvas
Published: November 2022

We present a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements focused on men's issues, which has prospered online. Analyzing 28.8M posts from 6 forums and 51 subreddits, we paint a comprehensive picture of its evolution across the Web, showing the links between its different communities over the years. We find that milder and older communities, such as Pick Up Artists and Men's Rights Activists, are giving way to more extreme ones like Incels and Men Going Their Own Way, with a substantial migration of active users. Moreover, our analysis suggests that these newer communities are more toxic and misogynistic than the older ones.
RecTen: A Recursive Hierarchical Low Rank Tensor Factorization Method to Discover Hierarchical Patterns from Multi-modal Data
Authors: Islam Risul , Rokon Md Omar Faruk , Papalexakis Evangelos E. , Faloutsos Michalis
Published: November 2022

How can we expand the tensor decomposition to reveal a hierarchical structure of the multi-modal data in a self-adaptive way? Current tensor decomposition provides only a single layer of clusters. We argue that with the abundance of multimodal data and time-evolving networks nowadays, the ability to identify emerging hierarchies is important. To this effect, we propose RecTen, a multi-modal hierarchical clustering approach based on tensor decomposition. Our approach enables us to: (a) recursively decompose clusters identified in the previous step, and (b) identify the right conditions for terminating this process. In the absence of a well-established benchmark, we evaluate our approach with synthetic and five real datasets. First, we test the sensitivity of the performance to different scenarios and parameters. Second, we apply RecTen on four online forums and a dataset that represents user interaction on GitHub. This analysis identifies meaningful and interesting behaviors, which further increases our confidence in the usefulness of our approach. For example, we identify some real events like ransomware outbreaks (55 users, 86 threads, December 2015, February 2016), the emergence of a black-market of decryption tools (34 users, 12 threads, February 2016), and romance scamming (82 users, 172 threads, March 2018). To maximize the impact of our work, we intend to: (a) develop a usable tool, (b) make the tool and our datasets publicly available. However, RecTen is a hierarchical clustering approach that can be used to take the pulse of large multimodal data and let the data reveal its own hidden structures.
Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model
Authors: Islam Tunazzina , Goldwasser Dan
Published: November 2022

Multiview representation learning of data can help construct coherent and contextualized users' representations on social media. This paper suggests a joint embedding model, incorporating users' social and textual information to learn contextualized user representations used for understanding their lifestyle choices. We apply our model to tweets related to two lifestyle activities, `Yoga' and `Keto diet' and use it to analyze users' activity type and motivation. We explain the data collection and annotation process in detail and provide an in-depth analysis of users from different classes based on their Twitter content. Our experiments show that our model results in performance improvements in both domains.
Modeling Collective Anticipation and Response on Wikipedia
Authors: Kobayashi Ryota , Gildersleve Patrick , Uno Takeaki , Lambiotte Renaud
Published: November 2022

The dynamics of popularity in online media are driven by a combination of endogenous spreading mechanisms and response to exogenous shocks including news and events. However, little is known about the dependence of temporal patterns of popularity on event-related information, e.g. which types of events trigger long-lasting activity. Here we propose a simple model that describes the dynamics around peaks of popularity by incorporating key features, i.e., the anticipatory growth and the decay of collective attention together with circadian rhythms. The proposed model allows us to develop a new method for predicting the future page view activity and for clustering time series. To validate our methodology, we collect a corpus of page view data from Wikipedia associated to a range of planned events, that are events which we know in advance will have a fixed date in the future, such as elections and sport events. Our methodology is superior to existing models in both prediction and clustering tasks. Furthermore, restricting to Wikipedia pages associated to association football, we observe that the specific realization of the event, in our case which team wins a match or the type of the match, has a significant effect on the response dynamics after the event. Our work demonstrates the importance of appropriately modeling all phases of collective attention, as well as the connection between temporal patterns of attention and characteristic underlying information of the events they represent.
MILE: A Multi-Level Framework for Scalable Graph Embedding
Authors: Liang Jiongqian , Gurukar Saket , Parthasarathy Srinivasan
Published: November 2022

Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while generating embeddings of better quality, for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation. Our code and data are publicly available with detailed instructions for adding new base embedding methods: https://github.com/jiongqian/MILE.