An analysis of hateful contents detection techniques on social media - Maw, Maw (2016)
Background: Detecting hateful contents on social media becomes a broad and important research area along with the popularity of social media. Objective: This paper aims primarily to understand the different techniques applied within the scope of detecting the use of hateful language on social media, their strengths and challenges to provide a solid and concrete reference to future researchers and practitioners. Methodology: In this paper, we investigated previous researches done in the domain of hateful contents detection on social media. We selected relevant published journal articles and conference proceedings from 2010 to 2015. Results: We observed that Support Vector Machine (SVM) algorithm is the most frequently applied for text classification. Data ambiguity problem, classification of sarcastic sentences and lack of necessary resources are identified as the difficulties for researchers in detecting the use of hateful contents. Conclusion: Future researchers must pay more attention on developing techniques to perform a deep analysis of sentences in order to detect the hateful contents.