Characterizing and detecting social outrage on Twitter: Patel reservation in Gujarat

Abstract

Social Media is a platform to share ideas, opinions and discussions. This provides scope to study social behavior and perform analysis around events discussed over it. The idea behind this study is to analyze the social characteristics during unrest in society. The analysis further can be used to identify the trend of social behavior and utilize for decision making and anticipatory governance. For this paper recent social outrage in Indian context related to caste based reservation has been studied using social media platform Twitter. A number of analytical methodologies have been used to understand the variations in opinions over social media during unrest. This paper researches the potential of tension during social outrage and the factors affecting it. Sentiment analysis and different machine learning methods used to detect level of tension and compared the results against manual annotation. To improve the performance of classification results, a rule based algorithm has been developed to detect tension during social outrage.