Sep 2026· Proceedings of the ACM on Human-Computer Interaction· 19 references
Hate Speech and Cyberbullying Detection
Abstract
Online community moderators continually adapt their rules to meet changing realities. The dynamics of these micro-level governance actions reflect various community needs and online moderation strategies. Understanding how online communities adapt their moderation rules as they grow and face new threats, such as Generative AI content, could reveal the micro-governance mechanisms that keep user-driven platforms functional and contributes to digital platforms’ resilience. This study analyzes moderation rule changes in response to existing and emerging challenges across over five thousand communities on Reddit over one year. We categorized the rule types by the behaviors they regulate. Our examination shows that rule developments, while prevalent and increasingly extensive overall, are more likely to happen in communities with certain topical interests, and concentrate on rules about issues such as copyright and quality. Rule development is also more associated with how fast a community is growing. We specifically explore how communities adjusted their rules to address new challenges like generative AI, investigating both the development and contents of AI-related policies. We reveal the nuanced relations between the developments of various AI-related rules and community characteristics such as size, age, community interest, timing of rule adoption, and neighboring subreddits. Specifically, larger and faster-growing communities are more likely to adopt AI-related rules. The community topical interests are strongly associated with whether its rules are restrictive or prescriptive. Neighboring communities have similar rule development patterns, a trend driven by shared topical interests and similar growth patterns as well as other contextual patterns. Overall, this study advances our understanding about how online communities dynamically evolve and refine their micro-level governance structures.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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