Analysis and mining of an election-based network using large-scale twitter data: a retrospective study.

Abstract

The user-generated Twitter data are a rich source of study and research that reflects the various social, economic, political, and other issues affecting people across the world. Analysis of the social interactions among users, who express themselves online, reveals different internal dynamics and provides detailed insights into real-world phenomena. In this paper, the structure and dynamics of the state assembly election-based tweet-reply network have been studied, as generated by Twitter users across the country of India for a period of 6-weeks. We study the flow of Twitter activity pertaining to the West Bengal assembly elections, along with the identification of the hashtags used by the three main political contenders. This information is used to identify the cluster-level dominance in the Twitter network over the 6-weeks of study. It is observed that this cluster dominance information is representative of the actual outcome of the elections, and can be effectively used as a forecasting tool. The collected tweets are used for lexicon-based emotion detection and further analysis. This highlights the reaction of the social media users in response to the events related to the election. It is observed that fear is the dominant emotion, while happiness is scarce in the opinions expressed during the studied duration. Next, the study and analysis of the complete reply-based social networks during weeks 1, 4, and 6 are undertaken. Important political and media actors are identified with standard network-level measures toward determining the efforts put in by the different clusters and individual actors involved in the election to control the network dominance.