bluuebunny/crossref_metadata_2025 huggingface dataset:
Detecting Camouflaged Content Polluters
Authors: Wu Liang , Hu Xia , Morstatter Fred , Liu Huan
Published: November 2022

The connectivity and openness of the Internet have cultivated a blistering expansion of online media websites. However, the culture of openness also makes the emerging platforms an effective channel for content pollution, such as fraud, phishing, and other online abuses. To complicate the problem, content polluters actively manipulate the characteristics of the Internet through establishing links with normal users and blending the malicious information with legitimate content. The manipulated links and content, being used as camouflage, make it very intricate to detect content polluters. Recent work has investigated camouflaged fraud in networks. However, due to the lack of availability of label information for camouflaged content, it is challenging to detect content polluters with traditional approaches. In this paper, we make the first attempt on detecting camouflaged content polluters. In order to evaluate the proposed approach, we conduct experiments on real-world data. The results show that our method achieves better results than existing approaches.
Mood Congruence or Mood Consistency? Examining Aggregated Twitter Sentiment Towards Ads in 2016 Super Bowl
Authors: Hu Yuheng , Nian Tingting , Chen Cheng
Published: November 2022

While the popularity of Social TV has recently attracted significant research efforts from various research communities, little is known about how viewers' Twitter sentiment toward TV advertising is affected by the viewers' mood state affected by the TV program. In this paper, we take a large set of tweets posted during the Super Bowl 2016 to investigate the effect of audience's mood induced by the game on their reactions the Super Bowl commercials. Our results find the support for the mood congruence theory, suggesting that game-induced mood has a positive and significant effect on the viewers' Twitter sentiment towards the commercials. We also discuss both theoretical and practical implications for our study.
Loyalty in Online Communities
Authors: Hamilton William , Zhang Justine , Danescu-Niculescu-Mizil Cristian , Jurafsky Dan , Leskovec Jure
Published: November 2022

Loyalty is an essential component of multi-community engagement. When users have the choice to engage with a variety of different communities, they often become loyal to just one, focusing on that community at the expense of others. However, it is unclear how loyalty is manifested in user behavior, or whether loyalty is encouraged by certain community characteristics. In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring this relation using a large dataset of discussion communities from Reddit, we reveal that loyalty is manifested in remarkably consistent behaviors across a wide spectrum of communities. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user's propensity to become loyal is apparent from their first interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.
Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks
Authors: Liu Zhe , Xu Anbang , Zhang Mengdi , Mahmud Jalal , Sinha Vibha
Published: November 2022

One problem that every presenter faces when delivering a public discourse is how to hold the listeners’ attentions or to keep them involved. Therefore, many studies in conversation analysis work on this issue and suggest qualitatively constructions that can effectively lead to audience’s applause. To investigate these proposals quantitatively, in this study we analyze the transcripts of 2,135 TED Talks, with a particular focus on the rhetorical devices that are used by the presenters for applause elicitation. Through conducting regression analysis, we identify and interpret 24 rhetorical devices as triggers of audience applauding. We further build models that can recognize applause-evoking sentences and conclude this work with potential implications.
This Just In: Fake News Packs A Lot In Title, Uses Simpler, Repetitive Content in Text Body, More Similar To Satire Than Real News
Authors: Horne Benjamin , Adali Sibel
Published: November 2022

The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or the arguments in its content. Through a unique study of three data sets and features that capture the style and the language of articles, we show that this assumption is not true. Fake news in most cases is more similar to satire than to real news, leading us to conclude that persuasion in fake news is achieved through heuristics rather than the strength of arguments. We show overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real. This leads us to conclude that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims.
Online Petitioning Through Data Exploration and What We Found There: A Dataset of Petitions from Avaaz.org
Authors: Aragón Pablo , Sáez-Trumper Diego , Redi Miriam , Hale Scott , Gómez Vicenç , Kaltenbrunner Andreas
Published: November 2022

The Internet has become a fundamental resource for activism as it facilitates political mobilization at a global scale. Petition platforms are a clear example of how thousands of people around the world can contribute to social change. Avaaz.org, with a presence in over 200 countries, is one of the most popular of this type. However, little research has focused on this platform, probably due to a lack of available data. In this work we retrieved more than 350K petitions, standardized their field values, and added new information using language detection and named-entity recognition. To motivate future research with this unique repository of global protest, we present a first exploration of the dataset. In particular, we examine how social media campaigning is related to the success of petitions, as well as some geographic and linguistic findings about the worldwide community of Avaaz.org. We conclude with example research questions that could be addressed with our dataset.
Life in the "Matrix": Human Mobility Patterns in the Cyber Space
Authors: Hu Tianran , Luo Jiebo , Liu Wei
Published: November 2022

With the wide adoption of the multi-community setting in many popular social media platforms, the increasing user engagements across multiple online communities warrant research attention. In this paper, we introduce a novel analogy between the movements in the cyber space and the physical space. This analogy implies a new way of studying human online activities by modelling the activities across online communities in a similar fashion as the movements among locations. First, we quantitatively validate the analogy by comparing several important properties of human online activities and physical movements. Our experiments reveal striking similarities between the cyber space and the physical space. Next, inspired by the established methodology on human mobility in the physical space, we propose a framework to study human "mobility" across online platforms. We discover three interesting patterns of user engagements in online communities. Furthermore, our experiments indicate that people with different mobility patterns also exhibit divergent preferences to online communities. This work not only attempts to achieve a better understanding of human online activities, but also intends to open a promising research direction with rich implications and applications.
Movie Pirates of the Caribbean: Exploring Illegal Streaming Cyberlockers
Authors: Ibosiola Damilola , Steer Benjamin , Garcia-Recuero Alvaro , Stringhini Gianluca , Uhlig Steve , Tyson Gareth
Published: November 2022

Online video piracy (OVP) is a contentious topic, with strong proponents on both sides of the argument. Recently, a number of illegal websites, called streaming cyberlockers, have begun to dominate OVP. These websites specialise in distributing pirated content, underpinned by third party indexing services offering easy-to-access directories of content. This paper performs the first exploration of this new ecosystem. It characterises the content, as well the streaming cyberlockers' individual attributes. We find a remarkably centralised system with just a few networks, countries and cyberlockers underpinning most provisioning. We also investigate the actions of copyright enforcers. We find they tend to target small subsets of the ecosystem, although they appear quite successful. 84% of copyright notices see content removed.
A Social Media Based Examination of the Effects of Counseling Recommendations after Student Deaths on College Campuses
Authors: Saha Koustuv , Weber Ingmar , De Choudhury Munmun
Published: November 2022

Student deaths on college campuses, whether brought about by a suicide or an uncontrollable incident, have serious repercussions for the mental wellbeing of students. Consequently, many campus administrators implement post-crisis intervention measures to promote student-centric mental health support. Information about these measures, which we refer to as "counseling recommendations", are often shared via electronic channels, including social media. However, the current ability to assess the effects of these recommendations on post-crisis psychological states is limited. We propose a causal analysis framework to examine the effects of these counseling recommendations after student deaths. We leverage a dataset from 174 Reddit campus communities and ~400M posts of ~350K users. Then we employ statistical modeling and natural language analysis to quantify the psychosocial shifts in behavioral, cognitive, and affective expression of grief in individuals who are "exposed" to (comment on) the counseling recommendations, compared to that in a matched control cohort. Drawing on crisis and psychology research, we find that the exposed individuals show greater grief, psycholinguistic, and social expressiveness, providing evidence of a healing response to crisis and thereby positive psychological effects of the counseling recommendations. We discuss the implications of our work in supporting post-crisis rehabilitation and intervention efforts on college campuses.
"At Least the Pizzas You Make Are Hot": Norms, Values, and Abrasive Humor on the Subreddit r/RoastMe
Authors: Kasunic Anna , Kaufman Geoff
Published: November 2022

We present a mixed methods study of the online forum r/RoastMe, a comedy-focused subreddit of the parent site reddit.com, wherein members post photos of themselves to be ridiculed by other members; the site generally encourages harsh and offensive forms of humor in these interpersonal exchanges. We conducted semi-structured interviews with sixteen participants (both "roasters" and "roastees") in the online forum to understand their motivations for participating, their experiences in the subreddit, and their perceptions of their and other members' participation. To complement our qualitative analyses, we also analyzed a RoastMe data set of over 9,000 image posts and 230,000 comments from June-August of 2017. From our interviews, we found that, like other deviant online communities, RoastMe relies on a specific set of norms. In RoastMe, roasters rely heavily on perspective-taking rather than dissociation from their targets, roastees highly value the often scathing assessments offered by users on RoastMe, and, despite the salience of norms that enhance feelings of safety, there is lingering concern among participants about the potential for emotional or psychological harm. Our quantitative analyses confirm many of the statements made in our qualitative interviews and provide further insights into the specific nature of interactions on the subreddit. Our study directs us toward different vantage points from which to design online community spaces that account for or leverage users' predilections for baiting behaviors, harsh judgments, and caustic humor.
Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
Authors: Mishra Swapnil , Rizoiu Marian-Andrei , Xie Lexing
Published: November 2022

Understanding and predicting the popularity of online itemsis an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously – e.g. microblogs or traditional media coverage. Here, we propose RNN-MAS, a recurrent neural network for modeling asynchronous streams. It is a sequence generator that connects multiple streams of different granularity via joint inference. We show RNN-MAS not only outperforms the current state-of-the-art Youtube popularity prediction system by 17%, but also captures complex dynamics, such as seasonal trends of unseen influence. We define two new metrics: the promotion score quantifies the gain in popularity from one unit of promotion for a Youtube video; the loudness level captures the effects of a particular user tweeting about the video. We use the loudness level to compare the effects of a video being promoted by a single highly-followed user (in the top 1% most followed users) against being promoted by a group of mid-followed users. We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos. This work provides more accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.
Conspiracies Online: User Discussions in a Conspiracy Community Following Dramatic Events
Authors: Samory Mattia , Mitra Tanushree
Published: November 2022

Online communities play a crucial role in disseminating conspiracy theories. New theories often emerge in the aftermath of catastrophic events. Despite evidence of their widespread appeal, surprisingly little is known about who participates in these event-specific conspiratorial discussions or how do these discussions evolve over time. We study r/conspiracy, an active Reddit community of more than 200,000 users dedicated to conspiratorial discussions. By focusing on four tragic events and 10 years of discussions, we find three distinct user cohorts: joiners, who never participated in Reddit but joined r/conspiracy only after the event; converts who were active Reddit users but joined r/conspiracy only after the event; and veterans, who are longstanding r/conspiracy members. While joiners and converts have a shorter lifespan in the community in comparison to the veterans, joiners are more active during their shorter tenure, becoming increasingly engaged over time. Finally, to investigate how these events affect users’ conspiratorial discussions, we adopted a causal inference approach to analyze user comments around the time of the events. We find that discussions happening after the event exhibit signs of emotional shock, increased language complexity, and simultaneous expressions of certainty and doubtfulness. Our work provides insight on how online communities may detect new conspiracy theories that emerge ensuing dramatic events, and in the process stop them before they spread.
Describing and Classifying Post-Mortem Content on Social Media
Authors: Jiang Jialun , Brubaker Jed
Published: November 2022

As the quantity of user profiles on social media grows, so does the number of post-mortem profiles. In this paper, we present a computational linguistic analysis of post-mortem social media profiles. Specifically, we provide an analysis of pre- vs. post-mortem language use, followed by a description of classifiers we developed that can accurately classify the mortality of social media profiles. These results shed initial lights into the ways in which people speak to the dead, and mark a first step toward accurately identifying mortality on a large scale.
Understanding Self-Narration of Personally Experienced Racism on Reddit
Authors: Yang Diyi , Counts Scott
Published: November 2022

We identify and classify users’ self-narration of racial discrimination and corresponding community support in social media. We developed natural language models first to distinguish self-narration of racial discrimination in Reddit threads, and then to identify which types of support are provided and valued in subsequent replies. Our classifiers can detect the self-narration of personally experienced racism in online textual accounts with 83% accuracy and can recognize four types of supportive actions in replies with up to 88% accuracy. Descriptively, our models identify types of racism experienced and the racist concepts (e.g., sexism, appearance or accent related) most experienced by people of different races. Finally, we show that commiseration is the most valued form of social support.
When Politicians Talk About Politics: Identifying Political Tweets of Brazilian Congressmen
Authors: Oliveira Lucas , Vaz de Melo Pedro , Amaral Marcelo , Pinho José Antônio
Published: November 2022

Since June 2013, when Brazil faced the largest and most significant mass protests in a generation, a political crisis is in course. In midst of this crisis, Brazilian politicians use social media to communicate with the electorate in order to retain or to grow their political capital. The problem is that many controversial topics are in course and deputies may prefer to avoid such themes in their messages. To characterize this behavior, we propose a method to accurately identify political and non-political tweets independently of the deputy who posted it and of the time it was posted. Moreover, we collected tweets of all congressmen who were active on Twitter and worked in the Brazilian parliament from October 2013 to October 2017. To evaluate our method, we used word clouds and a topic model to identify the main political and non-political latent topics in parliamentarian tweets. Both results indicate that our proposal is able to accurately distinguish political from non-political tweets. Moreover, our analyses revealed a striking fact: more than half of the messages posted by Brazilian deputies are non-political.
Does Reciprocal Gratefulness in Twitter Predict Neighborhood Safety?: Comparing 911 Calls Where Users Reside or Use Social Media
Authors: White Ann Marie , Bai Linxiao , Homan Christopher , Funchess Melanie , Cerulli Catherine , Ptah Amen , Pandita Deepak , Kautz Henry
Published: November 2022

Is there a relationship between urban neighborhood safety and helpful or supportive user networks on Twitter? An interdisciplinary, community-partnered team analyzed one year (2013-2014) of geo-tagged tweets from a 16-county region to generate a network of users who expressed gratefulness to one another. Call counts to 911 (2013-2015) around locations in the urban center indicate safety-oriented activity in residential areas. We compared frequencies of 911-related police activity across geographic pockets (200m radius or 200×200 meter2 areas) where mutually helpful or thanked users lived or frequently tweeted, to pockets randomly selected in proximity. The 13 naturally helpful users with predicted home locations in this city lived where fewer 911 calls for police service were initiated over time, on average. Our results show that close neighborhood locations where 79 naturally helpful users had strong local geographic ties in this urban area, as evidenced by Twitter use patterns, functioned as safe zones needing fewer policing services on average than surrounding areas. We discuss the implications of predicting real-world needs for police services based on supportive qualities of one’s social media neighborhoods.
Opinion Context Extraction for Aspect Sentiment Analysis
Authors: Bandhakavi Anil , Wiratunga Nirmalie , Massie Stewart , Luhar Rushi
Published: November 2022

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of a sentence, paragraph, or text window, but without any knowledge about the entities mentioned (e.g. restaurant) and their aspects (e.g. price). Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services, and can also support the formulation of critical action steps to improve performance. In this paper we focus on aspect-level sentiment classification, studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We propose four methods to aspect context extraction using lexical, syntactic and sentiment co-occurrence knowledge. Further, we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context is effective in improving classification performance.Specifically combining syntactical features with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance.
Twits, Twats and Twaddle: Trends in Online Abuse towards UK Politicians
Authors: Gorrell Genevieve , Greenwood Mark , Roberts Ian , Maynard Diana , Bontcheva Kalina
Published: November 2022

Concerns have reached the mainstream about how social media are affecting political outcomes. One trajectory for this is the exposure of politicians to online abuse. In this paper we use 1.4 million tweets from the months before the 2015 and 2017 UK general elections to explore the abuse directed at politicians. Results show that abuse increased substantially in 2017 compared with 2015. Abusive tweets show a strong relationship with total tweets received, indicating for the most part impersonality, but a second pathway targets less prominent individuals, suggesting different kinds of abuse. Accounts that send abuse are more likely to be throwaway. Economy and immigration were major foci of abusive tweets in 2015, whereas terrorism came to the fore in 2017.
EviDense: A Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions
Authors: Balalau Oana , Castillo Carlos , Sozio Mauro
Published: November 2022

Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging. We present EVIDENSE, a graph-based approach for finding high-impact events (such as disaster events) in social media. Our evaluation shows that our method outperforms state-of-the-art approaches for the same problem, in terms of having higher precision, lower number of duplicates, while providing a keyword-based description that is succinct and informative.
Measuring the Impact of Anxiety on Online Social Interactions
Authors: Dutta Sarmistha , Ma Jennifer , De Choudhury Munmun
Published: November 2022

For individuals with anxiety disorders, maladaptive feelings and negative beliefs can interfere with daily activities and importantly, social relationships. Literature has examined both direct and indirect influences of an individual's anxiety on their social interactions, however, how they co-vary temporally remains less explored. As individuals appropriate social media platforms more pervasively, can anxiety play an equally significant role in impacting one's \textit{online} social interactions? This paper seeks to answer this question. Employing a dataset of 200 Twitter users, their timeline, and social network data, we examine the relationship between manifested anxiety and various attributes of social interaction of a user by employing Granger causality and time series forecasting approaches. We observe that increases in anxiety levels of an individual result in increased future interaction with weak ties, indicating a tendency to seek support from the broader online community. We discuss how our findings provide novel insights and practical lessons around the impact of an individual's mental health state on their online social interactions.
The News We Like Are Not the News We Visit: News Categories Popularity in Usage Data
Authors: Ben Houidi Zied , Scavo Giuseppe , Traverso Stefano , Teixeira Renata , Mellia Marco , Ganguly Soumen
Published: November 2022

Most of our knowledge about online news consumption comes from survey-based news market reports, partial usage data from a single editor, or what people publicly share on social networks. This paper complements these sources by presenting the first holistic study of visits across online news outlets that a population uses to read news. We monitor the entire network traffic generated by Internet users in four locations in Italy. Together these users generated 80 million visits to 5.4 million news articles in about one year and a half. This unique view allows us to evaluate how usage data complements existing data sources. We find for instance that only 16% of news visits in our datasets came from online social networks. In addition, the popularity of news categories when considering all visits is quite different from the one when considering only news discovered on social media, or visits to a single major news outlet. Interestingly, a substantial mismatch emerges between self-reported news-category preferences (as measured by Reuters Institute in the same year and same country) and their actual popularity in terms of visits in our datasets. In particular, unlike self-reported preferences expressed by users in surveys that put “Politics”, “Science” and “International” as the most appreciated categories, “Tragedies and Weird news”’ and “Sport” are by far the most visited. We discuss two possible causes of this mismatch and conjecture that the most plausible reason is the disassociation that may occur between individuals’ cognitive values and their cue-triggered attraction.
A Generative Model for Discovering Action-Based Roles and Community Role Compositions on Community Question Answering Platforms
Authors: Geigle Chase , Dev Himel , Sundaram Hari , Zhai ChengXiang
Published: November 2022

This paper proposes a generative model for discovering user roles and community role compositions in Community Question Answering (CQA) platforms. While past research shows that participants play different roles in online communities, automatically discovering these roles and providing a summary of user behavior that is readily interpretable remains an important challenge. Furthermore, there has been relatively little insight into the distribution of these roles between communities. Does a community’s composition over user roles vary as a function of topic? How does it relate to the health of the underlying community? Does role composition evolve over time? The generative model proposed in this paper, the mixture of Dirichlet-multinomial mixtures (MDMM) behavior model can (1) automatically discover interpetable user roles (as probability distributions over atomic actions) directly from log data, and (2) uncover community-level role compositions to facilitate such cross-community studies.

A comprehensive experiment on all 161 non-meta communities on the StackExchange CQA platform demonstrates that our model can be useful for a wide variety of behavioral studies, and we highlight three empirical insights. First, we show interesting distinctions in question-asking behavior on StackExchange (where two distinct types of askers can be identified) and answering behavior (where two distinct roles surrounding answers emerge). Second, we find statistically significant differences in behavior compositions across topical groups of communities on StackExchange, and that those groups that have statistically significant differences in health metrics also have statistically significant differences in behavior compositions, suggesting a relationship between behavior composition and health. Finally, we show that the MDMM behavior model can be used to demonstrate similar but distinct evolutionary patterns between topical groups.
NELA-GT-2018: A Large Multi-Labelled News Dataset for the Study of Misinformation in News Articles
Authors: Nørregaard Jeppe , Horne Benjamin D. , Adalı Sibel
Published: November 2022

In this paper, we present a dataset of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. We incorporate ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. The NELA-GT2018 dataset can be found at https://doi.org/10.7910/DVN/ ULHLCB.
Mastodon Content Warnings: Inappropriate Contents in a Microblogging Platform
Authors: Zignani Matteo , Quadri Christian , Galdeman Alessia , Gaito Sabrina , Rossi Gian Paolo
Published: November 2022

Our social communications and the expression of our beliefs and thoughts are becoming increasingly mediated and diffused by online social media. Beyond countless other advantages, this democratization and freedom of expression is also entailing the transfer of unpleasant offline behaviors to the online life, such as cyberbullying, sexting, hate speech and, in general, any behavior not suitable for the online community people belong to. To mitigate or even remove these threats from their platforms, most of the social media providers are implementing solutions for the automatic detection and filtering of such inappropriate contents. However, the data they use to train their tools are not publicly available.In this context, we release a dataset gathered from Mastodon, a distribute online social network which is formed by communities that impose the rules of publication, and which allows its users to mark their posts inappropriate if they perceived them not suitable for the community they belong to. The dataset consists of all the posts with public visibility published by users hosted on servers which support the English language. These data have been collected by implementing an ad-hoc tool for downloading the public timelines of the servers, namely instances, that form the Mastodon platform, along with the meta-data associated to them. The overall corpus contains over 5 million posts, spanning the entire life of Mastodon. We associate to each post a label indicating whether or not its content is inappropriate, as perceived by the user who wrote it. Moreover, we also provide the full description of each instance. Finally, we present some basic statistics about the production of inappropriate posts and the characteristics of their associated textual content.
Dumping the Closet Skeletons Online: Exploring the Guilty Information Disclosure Behavior on Social Media
Authors: Yang Yukun , Huang Yeman
Published: November 2022

Privacy issues on social media are becoming an increasing area of concern. Paradoxically, some netizens are actively divulging their privacy online. Noticeably, some information is specifically guilt-related, though confession online is considered irrational. This preliminary study strives to understand this guilty information disclosure behavior through a mixed-approach. Analyzing posts and comments in a confession forum on Reddit, we find that sex-related and recreation-related topics prevail. Our qualitative investigation produces a thematic model with 71 codes, 17 concepts, 4 frames, 3 categories, and 9 relationships, capturing the intents, content, influencers of this behavior, and the interactions among users. Our contribution relies on the investigation of this peculiar behavior to better understand people’s privacy behavior. Also, we render a sophisticated framework around guilt-inducing behaviors useful for future work. We also suggest it as a mixture of conformity and counter-conformity, a modern “technology of self” and a variant of Adaptive Cognitive Theory.
Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories
Authors: Field Anjalie , Bhat Gayatri , Tsvetkov Yulia
Published: November 2022

In October 2017, numerous women accused producer Harvey Weinstein of sexual harassment. Their stories encouraged other women to voice allegations of sexual harassment against many high profile men, including politicians, actors, and producers. These events are broadly referred to as the #MeToo movement, named for the use of the hashtag “#metoo” on social media platforms like Twitter and Facebook. The movement has widely been referred to as “empowering” because it has amplified the voices of previously unheard women over those of traditionally powerful men. In this work, we investigate dynamics of sentiment, power and agency in online media coverage of these events. Using a corpus of online media articles about the #MeToo movement, we present a contextual affective analysis—an entity-centric approach that uses contextualized lexicons to examine how people are portrayed in media articles. We show that while these articles are sympathetic towards women who have experienced sexual harassment, they consistently present men as most powerful, even after sexual assault allegations. While we focus on media coverage of the #MeToo movement, our method for contextual affective analysis readily generalizes to other domains.1
When Your Friends Become Sellers: An Empirical Study of Social Commerce Site Beidian
Authors: Cao Hancheng , Chen Zhilong , Xu Fengli , Wang Tao , Xu Yujian , Zhang Lianglun , Li Yong
Published: November 2022

Past few years have witnessed the emergence and phenomenal success of strong-tie based social commerce. Embedded in social networking sites, these E-Commerce platforms transform ordinary people into sellers, where they advertise and sell products to their friends and family in online social networks. These sites can acquire millions of users within a short time, and are growing fast at an accelerated rate. However, little is known about how these social commerce develop as a blend of social relationship and economic transactions. In this paper we present the first measurement study on the full-scale data of Beidian, one of the fastest growing social commerce sites in China, which involves 11.8 million users. We first analyzed the topological structure of the Beidian platform and highlighted its decentralized nature. We then studied the site's rapid growth and its growth mechanism via invitation cascade. Finally, we investigated purchasing behavior on Beidian, where we focused on user proximity and loyalty, which contributes to the site's high conversion rate. As the consequences of interactions between strong ties and economic logics, emerging social commerce demonstrates significant property deviations from all known social networks and E-Commerce in terms of network structure, dynamics and user behavior. To the best of our knowledge, this work is the first quantitative study on the network characteristics and dynamics of emerging social commerce platforms.
Generalized Euclidean Measure to Estimate Network Distances
Authors: Coscia Michele
Published: November 2022

Estimating the distance covered by a propagation phenomenon on a network is an important task: it can help us estimating the infectiousness of a disease or the effectiveness of an online viral marketing campaign. However, so far the only way to make such an estimate relies on solving the optimal transportation problem, or by adapting graph signal processing techniques. Such solutions are either inefficient, because they require solving a complex optimization problem; or fragile, because they were not designed with this problem in mind. In this paper, we propose a new generalized Euclidean approach to estimate distances between weighted groups of nodes in a network. We do so by adapting the Mahalanobis distance, incorporating the graph's topology via the pseudoinverse of its Laplacian. In experiments we see that this measure returns intuitive distances which agree with the ones a human would estimate. We also show that the measure is able to recover the infection parameter in an epidemic model, or the activation threshold in a cascade model. We conclude by showing that the measure can be used in online social media settings to identify fast-spreading behaviors. Our measure is also less computationally expensive.
Identifying and Quantifying Coordinated Manipulation of Upvotes and Downvotes in Naver News Comments
Authors: Jeong Jiwan , Kang Jeong-han , Moon Sue
Published: November 2022

Today, many news sites let users write comments on news articles, rate others' comments by upvoting and downvoting, and order the comments by the rating. Top-rated comments are placed right below the news article and read widely, reaching a large audience and wielding great influence. As their importance grew, upvotes and downvotes are increasingly manipulated by coordinated efforts to hide existing top comments and push certain comments to the top. In this paper, we analyze comment sections of articles targeted by coordinated efforts and identify a trace of vote manipulation. Based on the findings, we propose a parameterized classifier that distinguishes comment threads affected by coordinated voting. The classifier only uses the number of upvotes and downvotes of comments. Therefore it is widely applicable to general vote-based curation systems where contents are sorted by the difference of upvotes and downvotes. Using the classifier and our choice of parameters, we have examined six years of the entire commenting history on a leading news portal in South Korea. Manual inspection in partisan online communities could only identify a few hundreds of targeted articles. With our classifier, we have identified more than ten thousand comment threads with a high likelihood of manipulation. We also observe a significant increase in coordinated manipulation in recent years.
Identity-Based Roles in Rhizomatic Social Justice Movements on Twitter
Authors: Oden Choi Judeth , Herbsleb James , Hammer Jessica , Forlizzi Jodi
Published: November 2022

Contemporary social justice movements can be understood as rhizomatic, growing laterally without a central structure. In this mixed methods study, we investigated the roles that activists develop based on their personal and professional identities and carry with them through the dynamic landscape of rhizomatic social justice movements on Twitter. We conducted interviews with self-identified social justice activists and analyzed seven weeks of their Twitter timeline and retweets. We found three activist roles–organizer, storyteller and advocate–and described the identities, approaches to activism, behaviors on Twitter, and the relationship to social justice movements for each role. We used these roles as a lens to better understand how movement identities are constructed, laid out an agenda for future research on roles in rhizomatic social justice movements and suggested design directions.
The Structure of U.S. College Networks on Facebook
Authors: Overgoor Jan , State Bogdan , Adamic Lada A.
Published: November 2022

Anecdotally, social connections made in university have life-long impact. Yet knowledge of social networks formed in college remains episodic, due in large part to the difficulty and expense involved in collecting a suitable dataset for comprehensive analysis. To advance and systematize insight into college social networks, we describe a dataset of the largest online social network platform used by college students in the United States. We combine de-identified and aggregated Facebook data with College Scorecard data, campus-level information provided by U.S. Department of Education, to produce a dataset covering the 2008-2015 entry year cohorts for 1,159 U.S. colleges and universities, spanning 7.6 million students. To perform the difficult task of comparing these networks of different sizes we develop a new methodology. We compute features over sampled ego-graphs, train binary classifiers for every pair of graphs, and operationalize distance between graphs as predictive accuracy. Social networks of different year cohorts at the same school are structurally more similar to one another than to cohorts at other schools. Networks from similar schools have similar structures, with the public/private and graduation rate dimensions being the most distinguishable. We also relate school types to specific outcomes. For example, students at private schools have larger networks that are more clustered and with higher homophily by year. Our findings may help illuminate the role that colleges play in shaping social networks which partly persist throughout people's lives.
Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
Authors: Shu Kai , Mahudeswaran Deepak , Wang Suhang , Liu Huan
Published: November 2022

Consuming news from social media is becoming increasingly popular. However, social media also enables the wide dissemination of fake news. Because of the detrimental effects of fake news, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection.In an attempt to understand the correlations between news propagation networks and fake news, first, we build hierarchical propagation networks for fake news and true news pieces; second, we perform a comparative analysis of the propagation network features from structural, temporal, and linguistic perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature importance analysis. We conduct extensive experiments on real-world datasets and demonstrate the proposed features can significantly outperform state-of-the-art fake news detection methods by at least 1.7% with an average F1>0.84. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.
“Musicalization of the Culture”: Is Music Becoming Louder, More Repetitive, Monotonous and Simpler?
Authors: Yang Yukun
Published: November 2022

“Musicalization of the culture” is the social science concept proposed by American philosopher George Stainer. He depicted the glooming future of music—it would become omnipresent while having increasing volume, repetitiveness, and monotony, which are ascribed to the debase of literal aesthetics. Although research that relates to one or some of these predictions exists, neither of them encompass all these “musicalization” manifestations, nor do they study the trend of these predictions over time. Therefore, this preliminary research tries to validate whether music has gained acoustic loudness, and lyrical repetitiveness, monotony, and simplicity in a computational fashion. Conducting time-series analysis with trend detection, we confirmed the increasing trends of acoustic loudness and repetitiveness but not monotony and simplicity from 1970 to 2016 using the MetroLyrics dataset and Spotify API. To investigate the simultaneity of these trends, we further conducted synchrony analysis and found little evidence indicating they would influence each other in a lagged fashion. In light of the results, we briefly discussed our findings by relating to the music industry change in reality. Our research made the first attempt to answer this music sociological preposition. On top of this, we also proposed novel metrics to quantify repetitiveness using closed frequent sequential pattern mining, which could be illuminating for future research.
Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?
Authors: Zhou Xinyi , Jin Shengmin , Zafarani Reza
Published: November 2022

Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users are indeed (not just illusively) more positive than the users themselves. This is mostly due to positive users having more friends. We identify five sentiment paradoxes at different network levels ranging from triads to large-scale communities. Empirical and theoretical evidence are provided to validate the existence of such sentiment paradoxes. By investigating the relationships between the sentiment paradox and other well-developed network paradoxes, i.e., friendship paradox and activity paradox, we find that user sentiments are positively correlated to their number of friends but rarely to their social activity. Finally, we demonstrate how sentiment paradoxes can be used to predict user sentiments.
Which Node Attribute Prediction Task Are We Solving? Within-Network, Across-Network, or Across-Layer Tasks
Authors: Altenburger Kristen M. , Ugander Johan
Published: November 2022

Node attribute prediction tasks arise in a wide range of classification tasks on social networks. Examples include detecting spam accounts, identifying compromised accounts, and inferring user demographics for targeted marketing. Despite the prevalence of these types of tasks in machine learning and social science settings, clear problem definitions are lacking. Do all nodes have to be connected in a single network instance? What if there are labels in one network but not another? In this work, we propose a taxonomy that distinguishes between different node attribute prediction tasks; we formalize the existing distinction between within-network and across-network attribute prediction, which have been informally described in prior work, and also introduce a variation we call across-layer attribute prediction. With this framework in place, we observe that methods framed as applicable to across-network tasks have a history of being evaluated on across-layer problem instances. While the methods do well in the across-layer setting, we find that when evaluated in genuine across-network settings, performance can be more limited than previously suggested. We provide a way to analyze and possibly reconcile this predictive performance gap, and highlight why across-network prediction remains an important and open problem domain.