"To Target or Not to Target": Identification and Analysis of Abusive Text Using Ensemble of Classifiers
This work addresses the issue of abusive content detection for social media platforms, but it is incremental as it builds on existing methods without major breakthroughs.
The authors tackled the problem of identifying abusive text on social media by developing an ensemble learning method that analyzes linguistic properties, achieving comparable results to state-of-the-art on the Twitter Abusive Behavior dataset without using user or network data.
With rising concern around abusive and hateful behavior on social media platforms, we present an ensemble learning method to identify and analyze the linguistic properties of such content. Our stacked ensemble comprises of three machine learning models that capture different aspects of language and provide diverse and coherent insights about inappropriate language. The proposed approach provides comparable results to the existing state-of-the-art on the Twitter Abusive Behavior dataset (Founta et al. 2018) without using any user or network-related information; solely relying on textual properties. We believe that the presented insights and discussion of shortcomings of current approaches will highlight potential directions for future research.