arXiv subjects

Charles Bickham

Publications and source records attributed to Charles Bickham.

7 recordsLinked to original sources

arXiv2026

Tied In on TikTok: Tie Strength and Emotional Dynamics in Algorithmic Communities

Whether genuine communities can form on algorithmically-driven short-form video platforms like TikTok remains an open question, given that user interactions are often brief, dispersed, and difficult to trace. Building on theories of tie strength and online community formation, we examine whether eating disorder (ED) discourse on TikTok exhibits behavioral and emotional signatures of strong ties, including more frequent, reciprocal, and affectively intense interactions. In this paper, we analyze 43,040 ED-related TikTok videos and over 560,000 comments, alongside a Non-ED comparison dataset. We find that at the user-pair level, greater interaction frequency is associated with increasingly positive emotional expression, a pattern that is amplified in ED-related conversations. This trend is also reflected linguistically, with pairs that interact more frequently exhibiting more of a positive tone. At the same time, how a relationship starts matters: pairs that begin with positive exchanges usually stay mostly positive as they continue interacting, while pairs that begin negatively may add some positive exchanges over time but rarely become mostly positive. To contextualize these dynamics, we classify ED videos into three content types (Pro-Recovery, Pro-ED, and ED Experiences) and find that each exhibits distinct emotional interaction patterns. These findings suggest that dense, emotionally structured relationships can emerge within ED discourse on TikTok. More broadly, our work provides one of the first empirical demonstrations of how community-like relational dynamics form and persist on algorithmically driven short-form video platforms.

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arXiv2025

Illusions of Intimacy: How Emotional Dynamics Shape Human-AI Relationships

AI companion chatbots, such as those offered by Replika and CharacterAI, increasingly function as always-available companions that provide empathy, validation, and support. While these systems appear to meet basic needs for connection, mounting safety concerns raise a deeper question: how do processes of emotional bonding and intimacy formation unfold in human-AI relationships? Prior research has relied largely on self-reports, interviews, or clinical assessments, leaving unclear how real-world emotional dynamics develop within ongoing human-AI conversations. We address this gap by analyzing over 17,000 user-shared chats with social chatbots from Reddit forums. We show that AI companions dynamically track and mimic user affect and amplify positive emotions, including when users share explicit or transgressive content. These dynamics suggest how chatbots can engage psychological processes involved in intimacy formation and emotional bonding. Finally, we release an anonymized dataset of emotionally salient human-AI companion dialogues to support future empirical work and discuss implications for redesigning and governing social chatbots as high-risk systems for vulnerable users.

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arXiv2025

EDTok: A Dataset for Eating Disorder Content on TikTok

Eating disorders, which include anorexia nervosa and bulimia nervosa, have been exacerbated by the COVID-19 pandemic, with increased diagnoses linked to heightened exposure to idealized body images online. TikTok, a platform with over a billion predominantly adolescent users, has become a key space where eating disorder content is shared, raising concerns about its impact on vulnerable populations. In response, we present a curated dataset of 43,040 TikTok videos, collected using keywords and hashtags related to eating disorders. Spanning from January 2019 to June 2024, this dataset, offers a comprehensive view of eating disorder-related content on TikTok. Our dataset has the potential to address significant research gaps, enabling analysis of content spread and moderation, user engagement, and the pandemic's influence on eating disorder trends. This work aims to inform strategies for mitigating risks associated with harmful content, contributing valuable insights to the study of digital health and social media's role in shaping mental health.

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arXiv2024

Tracking the 2024 US Presidential Election Chatter on TikTok: A Public Multimodal Dataset

This paper presents the TikTok 2024 U.S. Presidential Election Dataset, a large-scale, resource designed to advance research into political communication and social media dynamics. The dataset comprises 3.14 million videos published on TikTok between November 1, 2023, and October 16, 2024, encompassing video ids and transcripts. Data collection was conducted using the TikTok Research API with a comprehensive set of election-related keywords and hashtags, supplemented by third-party tools to address API limitations and expand content coverage, enabling analysis of hashtag co-occurrence networks that reveal politically aligned hashtags based on ideological affiliations, the evolution of top hashtags over time, and summary statistics that highlight the dataset's scale and richness. This dataset offers insights into TikTok's role in shaping electoral discourse by providing a multimodal view of election-related content. It enables researchers to explore critical topics such as coordinated messaging, misinformation spread, audience engagement, and linguistic trends. The TikTok 2024 U.S. Presidential Election Dataset is publicly available and aims to contribute to the broader understanding of social media's impact on democracy and public opinion.

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arXiv2024

Safe Spaces or Toxic Places? Content Moderation and Social Dynamics of Online Eating Disorder Communities

Social media platforms have become critical spaces for discussing mental health concerns, including eating disorders. While these platforms can provide valuable support networks, they may also amplify harmful content that glorifies disordered cognition and self-destructive behaviors. While social media platforms have implemented various content moderation strategies, from stringent to laissez-faire approaches, we lack a comprehensive understanding of how these different moderation practices interact with user engagement in online communities around these sensitive mental health topics. This study addresses this knowledge gap through a comparative analysis of eating disorder discussions across Twitter/X, Reddit, and TikTok. Our findings reveal that while users across all platforms engage similarly in expressing concerns and seeking support, platforms with weaker moderation (like Twitter/X) enable the formation of toxic echo chambers that amplify pro-anorexia rhetoric. These results demonstrate how moderation strategies significantly influence the development and impact of online communities, particularly in contexts involving mental health and self-harm.

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arXiv2024

Tracking the 2024 US Presidential Election Chatter on Tiktok: A Public Multimodal Dataset

This paper documents our release of a large-scale data collection of TikTok posts related to the upcoming 2024 U.S. Presidential Election. Our current data comprises 1.8 million videos published between November 1, 2023, and May 26, 2024. Its exploratory analysis identifies the most common keywords, hashtags, and bigrams in both Spanish and English posts, focusing on the election and the two main Presidential candidates, President Joe Biden and Donald Trump. We utilized the TikTok Research API, incorporating various election-related keywords and hashtags, to capture the full scope of relevant content. To address the limitations of the TikTok Research API, we also employed third-party scrapers to expand our dataset. The dataset is publicly available at https://github.com/gabbypinto/US2024PresElectionTikToks

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arXiv2024

Hidden in Plain Sight: Exploring the Intersections of Mental Health, Eating Disorders, and Content Moderation on TikTok

Social media platforms actively moderate content glorifying harmful behaviors like eating disorders, which include anorexia and bulimia. However, users have adapted to evade moderation by using coded hashtags. Our study investigates the prevalence of moderation evaders on the popular social media platform TikTok and contrasts their use and emotional valence with mainstream hashtags. We notice that moderation evaders and mainstream hashtags appear together, indicating that vulnerable users might inadvertently encounter harmful content even when searching for mainstream terms. Additionally, through an analysis of emotional expressions in video descriptions and comments, we find that mainstream hashtags generally promote positive engagement, while moderation evaders evoke a wider range of emotions, including heightened negativity. These findings provide valuable insights for content creators, platform moderation efforts, and interventions aimed at cultivating a supportive online environment for discussions on mental health and eating disorders.

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