Polarization and its Life on Social Media: A Case Study on Sabarimala and Demonetisation (2020)

Abstract: This paper is an attempt to study polarisation on social media data. We focus on two hugely controversial and talked about events in the Indian diaspora, namely 1) the Sabarimala Temple (located in Kerala, India) incident which became a nationwide controversy when two women under the age of 50 secretly entered the temple breaking a long standing temple rule that disallowed women of menstruating age (10-50) to enter the temple and 2) the Indian government’s move to demonetise all existing 500 and 1000 denomination banknotes, comprising of 86% of the currency in circulation, in November 2016. We gather tweets around these two events in various time periods, preprocess and annotate them with their sentiment polarity and emotional category, and analyse trends to help us understand changing polarity over time around controversial events. The tweets collected are in English, Hindi and code-mixed Hindi-English. Apart from the analysis on the annotated data, we also present the twitter data comprising a total of around 1.5 million tweets.

## introduction social media, over the past few years, has become the fastest growing medium for expressing opinions for billions of people worldwide. people have always had opinions in history, some extreme and some moderate. what has changed today, is the way people can make their opinions reach masses (read millions) of people in seconds to minutes to hours, depending on the popularity of the person and the general controversiality (for lack of a better word) of the opinion. earlier people could influence a bunch of people in their vicinity and sometimes, very rarely after going through multiple filters of sanity, they would be able to bring their idea to the world map. today any random person is able to express their ideas to millions of people, and thereby has the potential to influence masses much faster. india is no different. easy access to internet has brought millions of indians to twitter, where they express their opinions on topics that interest, affect them. leaders use social media to make their opinions reach the masses as well. in a recent survey by pew research center it was revealed that a significant percentage of people get their news from social media @xcite . this activity on twitter consequently helps people form opinions or change their already existing opinions on different topics. this paper attempts to study this change in opinion of the masses of people by observing and analysing their tweets on social media. we study the life of polarization on social media in the indian context. we decide to focus our efforts on two pressing, controversial and divisive issues -1) sabarimala 2) demonetisation. we study how the polarity of opinions of people change with respect to these issues over time. the complete context of the two events is described next. ## literature review stephen hawkins and dixon (2018) published a study entitled "the hidden tribes of usa". the study, through a survey, categorises the people of usa in 7 major political categories from extreme left to extreme right. they classify four of these seven non-extreme classes as the exhausted majority. the concept of exhausted majority implies that the majority of people lying in the middle of the polarity spectrum are tired from the extremist and tribal stands of the extreme left and extreme right people. they want them to strike a balance in their views and agree on a compromise instead of being engaged in political tribalism. in our work, we see a huge proportion of people keeping neutral opinions which aligns with the above mentioned work. ## data collection to analyse how people reacted to these two events, we scraped tweets from twitter using keyword search with the help of the api created by jefferson @xcite . the tweets scraped are collected according to the timeperiods of interest, as mentioned below. ## data preprocessing to make preprocessing easier, the tweets were separated into three groups based on the language of content, -1) english 2) hindi 3) hindi-english code-mixed. tweets containing words from any other language were removed. this separation was done with the help of the language identifier by @xcite . all three groups of tweets were preprocessed separately as follows: