Technology-Assisted Social Reforms and Online Hate Content: Insights from India

Abstract

In the current scenario while everything seems digitalized, we often spend more time scrolling across various social platforms as compared to what we spend in any other real life activities. It becomes a matter of great concern when it comes to analyze what we see and how it is interpreted. Through this paper we aims to identify the influence of technology assisted social-reform initiatives on gender-based hate content generation. With the help of Twitter API, 112577 government-initiated and 58370 citizen-initiated movement(s) tweets have been extracted. This collected data is examined for hatred nature content in terms of emotions using a software programmed in R programming language, the scores for each emotion is counted and a comparison between both the moments is made. The study clearly shows that the Citizeninitiated moments shares comparatively more hate content than the Government-initiated movements as the scores particular to specific emotions like anger, disgust, and sadness is more. This cognitive study can be helpful in policy making, promoting gender based equality, defining strategies to rebuild citizen initiatives in a hate-free environment and controlling hate content generation.