2026-05-19T10:44:24Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-2016512025-05-12T19:35:22Zoai_pmh:alloai_pmh:repo_items201651
https://hdl.handle.net/2286/R.2.N.201651
http://rightsstatements.org/vocab/InC/1.0/
All Rights Reserved
2025
70 pages
Masters Thesis
Academic theses
en
McLellan, Bradley
Proferes, Nicholas
Rachlin, Seth
Walker, Shawn
Arizona State University
Partial requirement for: M.A., Arizona State University, 2025
Field of study: Computing Studies
Automated accounts increasingly populate social platforms, distorting conversation and eroding trust. Yet scholars know little about how everyday users dedicated to bot mitigation recognize and theorize about these non-human actors on Reddit. This study interrogates two ‘bot hunting’ subreddits—r/TheseFuckingAccounts and r/RedditBotHunters—to illuminate grassroots detection efforts and user-generated explanatory frameworks. Using iterative thematic inquiry on 2,417 posts and comments (January–October 2024), I identify five categories of bot detection indicators: profile anomalies, content plagiarism, behavioral regularities, network coordination, and interactional oddities. I further distill five folk theories that portray bots as social capital profiteers, parasitic content pirates, adaptive AI adversaries, symptoms of lax platform governance, and instruments of coordinated manipulation. These community insights parallel and extend academic models: hunters combine pattern recognition with contextual knowledge to expose both low-effort spam and sophisticated AI-driven campaigns. Their folk vernacular exposes tensions between bottom-up vigilance and platform incentives, which suggests that user expertise constitutes an untapped asset in automated account mitigation. Integrating crowd-based cues into detection processes, along with transparent feedback loops, can enhance platform integrity while maintaining user autonomy. Moreover, this thesis posits that crowd-sourced insights can expedite the identification of deceptive bots. Increased transparency emerges as a promising method to strengthen platform integrity without compromising user agency. It concludes with a forward-looking roadmap for human-in-the-loop defenses against an evolving bot ecosystem, where user vigilance and active computational detection work in tandem to counter automated threats.
Social Research
Artificial Intelligence
"The Bot Problem Has Only Gotten Worse": Signals, Sensemaking, and Folk Theories of Reddit Bot Hunters