Detecting Synchronized Hate Speech in Online Social Networks via Social Synchrony and Ant Colony Optimization
محورهای موضوعی : IT Strategy
Shabana Nargis Rasool
1
,
Sarika Jain
2
,
Ajay Vikram Singh
3
1 - Department of Computer Science, Islamic University of Science and Technology, Kashmir, India
2 - Amity Institute of Information Technology, Amity University Noida, Uttar Pradesh, India
3 - Department of Computer Science, Islamic University of Science and Technology, Kashmir, India
کلید واژه: Hate Speech, Social Synchrony, Ant Colony Optimization, Feature Selection, Online Social Networks,
چکیده مقاله :
Online platforms have become fertile grounds for hate speech, often spreading through bursts of coordinated user activity. Detecting such patterns requires more than analyzing individual posts, as it calls for understanding the collective rhythm of online interactions. In the present study, we present SIACO (Social Synchrony Identification using Ant Colony Optimization), a nature-inspired framework that detects hate-speech events by tracing synchrony in user behaviour. SIACO models how hateful expressions emerge and fade collectively, using Ant Colony Optimization to refine linguistic features and improve classification accuracy. Upon evaluation on a Twitter dataset, the framework consistently outperforms both traditional machine learning models and transformer-based baselines, achieving up to a 10% improvement across major evaluation metrics. The framework also offers interpretable insights into the linguistic and temporal cues driving coordinated hate. The performance scores obtained highlight the value of looking at hate speech not just as text, but as a social phenomenon unfolding in synchrony.
Online platforms have become fertile grounds for hate speech, often spreading through bursts of coordinated user activity. Detecting such patterns requires more than analyzing individual posts, as it calls for understanding the collective rhythm of online interactions. In the present study, we present SIACO (Social Synchrony Identification using Ant Colony Optimization), a nature-inspired framework that detects hate-speech events by tracing synchrony in user behaviour. SIACO models how hateful expressions emerge and fade collectively, using Ant Colony Optimization to refine linguistic features and improve classification accuracy. Upon evaluation on a Twitter dataset, the framework consistently outperforms both traditional machine learning models and transformer-based baselines, achieving up to a 10% improvement across major evaluation metrics. The framework also offers interpretable insights into the linguistic and temporal cues driving coordinated hate. The performance scores obtained highlight the value of looking at hate speech not just as text, but as a social phenomenon unfolding in synchrony.
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