Cohesion-6K: An Arabic Dataset for Analyzing Social Cohesion and Conflict in Online Discourse
arXiv:2605.22447v1 Announce Type: new Abstract: The study of online discourse has become central to understanding societal polarization. While much research has focused on detecting overt toxicity, the subtle dynamics of social cohesion, meaning the interaction between divisive and unifying narratives, remain computationally underexplored (Bail, 2021; Gonzalez-Bailon and Lelkes, 2023). This paper presents Cohesion-6K, a manually and ChatGPT-assisted annotated dataset of six thousand Arabic public Facebook posts related to the Israeli Occupation of Palestine. Each post is assigned to one of fiv
The increasing geopolitical tensions and the prevalence of online discourse necessitate better tools for understanding societal polarization and cohesion, particularly in under-explored linguistic contexts.
This dataset provides a crucial resource for developing AI models to analyze social cohesion and conflict in Arabic online discourse, offering new insights into a highly volatile region.
The availability of Cohesion-6K will enable more nuanced computational analysis of online narratives concerning critical geopolitical events, moving beyond simple toxicity detection.
- · AI researchers (NLP)
- · Social scientists
- · Conflict resolution organizations
- · Arabic-speaking communities
- · Platforms struggling with nuanced content moderation
- · Entities promoting divisive narratives
Researchers gain a specialized dataset for training models on Arabic social cohesion and conflict.
Improved AI models lead to better detection and understanding of narrative dynamics in online Arabic content, potentially aiding early warning systems for conflict.
Enhanced analytical capabilities could inform more effective policy interventions or counter-narrative strategies in conflict zones.
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