Keywords
Hate Speech
Social Network Analysis
Communities Detection
How to Cite
Abstract
The boycott campaign against Facebook #StopHate-ForProfit, launched in June 2020, emerged as a key phenomenon in the fight against hate speech on social media. This study addresses the detection and characterization of communities in the #StopHateForProfit campaign, employing theoretical and methodological approaches from Social Network Analysis (SNA) and Natural Language Processing (NLP) to examine the social structure of the campaign on Twitter (now X). We used the software Gephi for community detection, employing centrality, modularity, connected components, and clustering coefficient measures. The analysis disclosed a complex and cohesive network composed of 5,556 communities with a high modularity that indicated dense internal interactions. We identified the strongest and weakest connected actors in the communities, which hinted at the closest and most direct relationships. The classification of actors according to their position provided insight into node influence and cohesion in the network. This interdisciplinary line of action contributes to understanding the diversity of approaches within the #StopHateForProfit campaign, highlighting its relevance regarding mass participation and impact. The analysis of communities revealed an effective collaboration among actors, demonstrating the comprehensiveness of the coordinated strategy to counter hate speech.
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