Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts
Online power-asymmetric conflicts are prevalent, and most platforms rely on human moderators to conduct moderation currently. Previous studies have been continuously focusing on investigating human moderation biases in different scenarios, while moderation biases under power-asymmetric conflicts remain unexplored. Therefore, we aim to investigate the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation (RQ1) and further explore the influence of AI's suggestions on these biases (RQ2). For this goal, we conducted a mixed design experiment with 50 participants by leveraging the real conflicts between consumers and merchants as a scenario. Results suggest several biases towards supporting the powerful party within these two moderation modes. AI assistance alleviates most biases of human moderation, but also amplifies a few. Based on these results, we propose several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models
Large Language Models (LLMs) have emerged as powerful tools for generating human-like text, transforming human-machine interactions. However, their widespread adoption has raised concerns about their potential to influen…
BAN-PL: a Novel Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl web service
Since the Internet is flooded with hate, it is one of the main tasks for NLP experts to master automated online content moderation. However, advancements in this field require improved access to publicly available accura…
SpecificityThe Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit's Quarantine Policy
Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. …
Abusive LanguageEvaluating Online Moderation Via LLM-Powered Counterfactual Simulations
Online Social Networks (OSNs) widely adopt content moderation to mitigate the spread of abusive and toxic discourse. Nonetheless, the real effectiveness of moderation interventions remains unclear due to the high cost of…
Watch Your Language: Investigating Content Moderation with Large Language Models
Large language models (LLMs) have exploded in popularity due to their ability to perform a wide array of natural language tasks. Text-based content moderation is one LLM use case that has received recent enthusiasm, howe…