arXiv subjects

Jennifer Golbeck

Publications and source records attributed to Jennifer Golbeck.

9 recordsLinked to original sources

arXiv2026

Red Flags and Cherry Picking: Reading The Scientific Blackpill Wiki

Incels are an online community of men who share a belief in extreme misogyny, the glorification of violence, and biological essentialism. They refer to their core ideology as "The Blackpill", a belief that physical attraction is the only path to romantic success and that women are only attracted to one very specific, hypermasculine archetype. This is not only a belief system; incels believe their ideology grounded in hard science. The research that incels use as evidence of their belief system is collected in an extensive online document, the Scientific Blackpill wiki page. In this research, we analyze the claims made on the wiki against the research cited to assess how the wiki authors are using or misusing science in support of their ideology. We find that the page largely cites legitimate science and describes it partly or mostly accurately. However, in discussing it, the results are often overgeneralized, stripped of context, or otherwise distorted to support the preexisting incel viewpoint. This echoes previous findings about motivated reasoning and borrowing scientific legitimacy in other misinformation and conspiracy-minded ideologies. We discuss the implications this has for understanding online radicalization and information quality.

cs.HC↗

arXiv2026

In Bad Faith: Assessing Discussion Quality on Social Media

The quality of a user's social media experience is determined both by the content they see and by the quality of the conversation and interaction around it. In this paper, we look at replies to tweets from mainstream media outlets and official government agencies and assess if they are good faith, engaging honestly and constructively with the original post, or bad faith, attacking the author or derailing the conversation. We assess automated approaches that may help in making this determination and then show that within our dataset of replies to mainstream media outlets and government agencies, bad faith interactions constitute 68.3% of all replies we studied, suggesting potential concerns about the quality of discourse in these specific conversational contexts. This is particularly true from verified accounts, where 91.7% of replies were bad faith. Given that verified accounts are algorithmically amplified, we discuss the implications of our work for understanding the user experience on social media.

cs.SI↗

arXiv2026

"Why I Took the Blackpill": A Thematic Analysis of the Radicalization Process in Incel Communities

Incels, or "involuntary celibates", are an extreme, misogynistic hate group that exists entirely online. Members of the community have been linked to acts of offline violence, including mass shootings. Previous research has engaged with the ideologies and beliefs of incels, but none has looked specifically at the radicalization process. In this paper, we perform a thematic analysis on social media posts where incels describe their own radicalization process. We identified six major themes grouped into four chronological steps: Pre-radicalization (themes of Appearance, Social Isolation, and Psychological issues), Searching for Blame, Radicalization, and Post Radicalization. These results align closely with existing work on radicalization among other extremist groups, bringing incel radicalization inline with a growing body of research on understanding and managing radicalization.

cs.SI↗

arXiv2025

Cross-Platform Violence Detection on Social Media: A Dataset and Analysis

Violent threats remain a significant problem across social media platforms. Useful, high-quality data facilitates research into the understanding and detection of malicious content, including violence. In this paper, we introduce a cross-platform dataset of 30,000 posts hand-coded for violent threats and sub-types of violence, including political and sexual violence. To evaluate the signal present in this dataset, we perform a machine learning analysis with an existing dataset of violent comments from YouTube. We find that, despite originating from different platforms and using different coding criteria, we achieve high classification accuracy both by training on one dataset and testing on the other, and in a merged dataset condition. These results have implications for content-classification strategies and for understanding violent content across social media.

cs.CL↗

arXiv2024

A Lexicon for Studying Radicalization in Incel Communities

Incels are an extremist online community of men who believe in an ideology rooted in misogyny, racism, the glorification of violence, and dehumanization. In their online forums, they use an extensive, evolving cryptolect - a set of ingroup terms that have meaning within the group, reflect the ideology, demonstrate membership in the community, and are difficult for outsiders to understand. This paper presents a lexicon with terms and definitions for common incel root words, prefixes, and affixes. The lexicon is text-based for use in automated analysis and is derived via a Qualitative Content Analysis of the most frequent incel words, their structure, and their meaning on five of the most active incel communities from 2016 to 2023. This lexicon will support future work examining radicalization and deradicalization/disengagement within the community.

cs.CL↗

arXiv2018

Automatically Identifying Fake News in Popular Twitter Threads

Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or `fake news,' present in these platforms. This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter, and PHEME, a dataset of potential rumors in Twitter and journalistic assessments of their accuracies. We apply this method to Twitter content sourced from BuzzFeed's fake news dataset and show models trained against crowdsourced workers outperform models based on journalists' assessment and models trained on a pooled dataset of both crowdsourced workers and journalists. All three datasets, aligned into a uniform format, are also publicly available. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We close with a discussion contrasting accuracy and credibility and why models of non-experts outperform models of journalists for fake news detection in Twitter.

cs.SI↗

arXiv2015

Learning to Discover Key Moments in Social Media Streams

This paper introduces LABurst, a general technique for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst performs competitively with existing burst detection techniques while simultaneously providing insight into and detection of unanticipated moments. To demonstrate our approach's potential, we compare two baseline event-detection algorithms with our language-agnostic algorithm to detect key moments across three major sporting competitions: 2013 World Series, 2014 Super Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series analysis baseline and is competitive with a domain-specific baseline even though we operate without any domain knowledge. We then go further by transferring LABurst's models learned in the sports domain to the task of identifying earthquakes in Japan and show our method detects large spikes in earthquake-related tokens within two minutes of the actual event.

cs.SI↗

arXiv2015

Benford's Law Applies To Online Social Networks

Benford's Law states that the frequency of first digits of numbers in naturally occurring systems is not evenly distributed. Numbers beginning with a 1 occur roughly 30\% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. We consider social data from five major social networks: Facebook, Twitter, Google Plus, Pinterest, and Live Journal. We show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same holds for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follow the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

cs.SI↗

arXiv2011

Network Clustering Approximation Algorithm Using One Pass Black Box Sampling

Finding a good clustering of vertices in a network, where vertices in the same cluster are more tightly connected than those in different clusters, is a useful, important, and well-studied task. Many clustering algorithms scale well, however they are not designed to operate upon internet-scale networks with billions of nodes or more. We study one of the fastest and most memory efficient algorithms possible - clustering based on the connected components in a random edge-induced subgraph. When defining the cost of a clustering to be its distance from such a random clustering, we show that this surprisingly simple algorithm gives a solution that is within an expected factor of two or three of optimal with either of two natural distance functions. In fact, this approximation guarantee works for any problem where there is a probability distribution on clusterings. We then examine the behavior of this algorithm in the context of social network trust inference.

cs.SI↗