The paper introduces BenCoDiC, a large Bengali cyberbullying corpus focused on political news discourse. It contains 50,250 textual instances from 100 YouTube videos, preserving caption–comment–reply relationships and organized via a nine‑dimensional star schema. A two‑tier human annotation process achieved high reliability (Fleiss' κ = 0.86, Krippendorff's α = 0.86), and transformer experiments show that adding conversational context modestly improves classification over isolated text.
By Md. Nesarul Hoque, Rudra Pratap Deb Nath, Umme Salma, Shima Chakraborty, Md. Hanif Seddiqui
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah
arXiv:2608. 05259v1 Announce Type: cross Abstract: Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content.
By Francesco Benedetti, Antonio Pellicani, Gianvito Pio, Michelangelo Ceci
The paper investigates whether large language models (LLMs) can generate synthetic cyberbullying conversations that replicate the social dynamics of real interactions. Using a comprehensive framework, the authors compare authentic dialogues with synthetic ones from GPT, Grok, and LLaMA across structural, linguistic, affective, and temporal dimensions, and conduct human evaluations of realism. Results show that while LLMs preserve high‑level interaction patterns, they systematically distort finer‑grained social phenomena, with model‑specific biases such as GPT’s suppression of harmful content and Grok’s amplification of aggression.
By Arefeh Kazemi, Hamza Qadeer, Sinan Asci, Joachim Wagner, Brian Davis
arXiv:2510. 14207v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm.
By Trilok Padhi, Pinxian Lu, Abdulkadir Erol, Tanmay Sutar, Gauri Sharma, Mina Sonmez, Munmun De Choudhury, Ugur Kursuncu
arXiv:2608.28394v1 Announce Type: cross
Abstract: Cyber threat intelligence (CTI) is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneit...
By Changze Li, Yutong Cheng, Tsania Camila Finnisa, Qian Cui, Wei Ding, Peng Gao
The paper analyzes how malicious actors create and share synthetic non-consensual intimate images (SNCII) across online forums, focusing on 4chan and Reddit. By examining over 280,000 4chan comments and 78,000 Reddit posts, the study identifies a wide array of resources—including 156 LoRA files targeting 141 female celebrities and 54 commercial nudification apps—used by both highly technical and less sophisticated users. The findings show that knowledge transfer between experts and newcomers fuels the spread of these illicit resources, revealing gaps in current regulatory frameworks and suggesting intervention points to deter such activity.
By Bernardo B. P. Medeiros (University of Florida), Malvika Jadhav (University of Florida), Allison Lu (University of Florida), Tadayoshi Kohno (Georgetown University), Vincent Bindschaedler (University of Florida), Kevin R. B. Butler (University of Florida)
arXiv:2607. 16076v1 Announce Type: cross Abstract: Multimodal sarcasm and cyberbullying detection remain challenging because the intended meaning often emerges from incongruity between textual and visual information rather than from either modality alone.
By Bhavana Verma, Priyanka Meel, Dinesh Kumar Vishwakarma
The paper presents a new taxonomy for online harm on video platforms, expanding existing frameworks to multimodal contexts and defining six content-based categories: Information, Hate and harassment, Addictive, Clickbait, Sexual, and Physical harms. It also evaluates multimodal large language models (MLLMs) as alternative annotators, comparing GPT‑4‑Turbo, non‑trained crowdworkers, and trained human annotators on a dataset of 19,422 potentially harmful YouTube videos. The study finds that MLLMs can perform comparably to human annotators in both binary and multi‑label classification tasks, suggesting potential applications in content moderation and social science research.
By Claire Wonjeong Jo, Miki Weso{\l}owska, Magdalena Wojcieszak
The study audits hate‑speech moderation on Twitter (now X) using 540,000 annotated tweets from a full day. Eighty percent of hateful tweets, including violent content, remained online after five months, and removal was only slightly more likely than for non‑hateful tweets, far below the rates for scams or adult content. Automated detection could not reliably classify hate but ranked it highly, allowing human triage; however, current staffing curbed little exposure, while substantial reductions were financially feasible and far below applicable regulatory fines.
By Manuel Tonneau, Dylan Thurgood, Diyi Liu, Niyati Malhotra, Victor Orozco-Olvera, Ralph Schroeder, Scott A. Hale, Manoel Horta Ribeiro, Paul R\"ottger, Samuel P. Fraiberger
arXiv:2602. 02838v2 Announce Type: replace-cross Abstract: The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features.
By Philipp J. Schneider, Lanqin Yuan, Marian-Andrei Rizoiu
The paper presents a method for building a network map of the artworld using tweets from a manually selected set of exemplar actors and NLP techniques. It identifies significant artworld actors, even those without Twitter accounts, and groups them into communities via the Louvain method while assessing their authority with HITS centrality. The resulting network demonstrates that starting from a small set of exemplars can uncover new actors and yield data that would otherwise be difficult to gather.
By Amalia Foka