Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis

Abstract

Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters’ network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters’ network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This is done using social network analysis and semantic analysis. The proposed method (1) detects and removes fake supporter(s), (2) mines communities in the politicians’ social network(s), (3) investigates the supporters’ reply network for conversations between supporters about each leader, and, finally, (4) analyses the retweet network for information diffusion of each political leader. Furthermore, sentiment analysis of the supporters of politicians is done using machine learning techniques, which ultimately predicted and revealed the strongest supporter network(s) among the three political leaders. Analysis of this data reveals that as of October 2017 (1) IK was the most renowned of the three politicians and had the strongest supporter’s community while using Twitter in a very controlled manner, (2) BBZ had the weakest supporters’ network on Twitter, and (3) the supporters of the political leaders in Pakistan are flexible on Twitter, communicating with each other, and that any group of supporters has a low level of isolation.

Twitter dialogue: an analysis of Pakistani politicians’ information sharing

“…The tweeting behaviour of politicians was generally in line with the results of previous studies (Graham et al, 2013;Larsson and Moe, 2014;Khan et al, 2020). However, out of 14, six (Condemning terrorism acts and/or country's situation/ link with country's safety, Own position, Religious, Energizing party workers, targeting youth and Political report/Highlighting efforts for developments) categories were introduced due to the dominance of content.…”

“…It is interesting to note that the winning party's account was more active, Tweeted more and retweeted than the opposing parties' accounts. Previous studies also evidenced that there is a significant relationship between increased tweeting behaviour and their winning position (Ahmed, Jaidka and Cho, 2016;Khan et al, 2020). In contrast (Bright et al, 2020) researchers found Twitter campaigns did not impact election outcomes during the 2015 and 2017 UK elections.…”

“…And his party, Pakistan Tehreek-e-Insaaf (PTI) involved more in writing blogs and using social media during the 2013 elections (Ahmed and Skoric, 2013;Eijaz, 2013). Another study confirmed these facts by investigating supporters' networks through Social Network and Semantic analysis (Khan et al, 2020). The study used the pre-election 2017 data set and found that Imran Khan (IK) is more vigilant in posting his comments and popular leader and sentiments of supporters indicate more positive comments for IK and neutral and negative for other politicians.…”

Election Prediction on Twitter: A Systematic Mapping Study

“…As of October 2020, Twitter had over 300 million users worldwide; 91% of them are over the age of 18. is platform attracts many politicians and enables them to interact and use it as a tool in their campaigns [4]. Offering an API that allows extracting public tweets and user's public information and interconnections, it is considered a treasure for researchers aiming for election predictions.…”

“…Many researchers have analyzed and predicted different countries' elections on different social media platforms such as Facebook and Twitter [4][5][6][7][8]. Few studies surveyed this topic [9][10][11].…”