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Communication Dans Un Congrès Année : 2020

Aggression Identification in Posts - two machine learning approaches

Résumé

Social media have changed the way people communicate. One of the aspects is cyber-aggression and interpersonal aggression that can be catalyzed by perceived anonymity. Automatically monitoring user-generated content in order to help moderating it is thus a hot topic. In this paper, we present and evaluate two supervised machine learning models to identify aggressive content and the level of aggressiveness. The first model uses random forest and linear regression while the second model uses deep learning techniques.
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Dates et versions

hal-03116190 , version 1 (20-01-2021)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

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  • HAL Id : hal-03116190 , version 1

Citer

Faneva Ramiandrisoa. Aggression Identification in Posts - two machine learning approaches. Detection Machine Learning for Trend and Weak Signal Detection in Social Networks and Social Media (TWS 2020), Feb 2020, Toulouse, France. pp.40-49. ⟨hal-03116190⟩
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