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Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis
Scientific Programming ( IF 1.672 ) Pub Date : 2020-09-01 , DOI: 10.1155/2020/9353120
Asif Khan 1 , Huaping Zhang 1 , Jianyun Shang 1 , Nada Boudjellal 1 , Arshad Ahmad 2 , Asmat Ali 1, 3 , Lin Dai 1
Affiliation  

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.

中文翻译:

使用社交网络分析和语义分析预测 Twitter 上的政治家支持者网络

政治是当今社交媒体网络上最热门、最常提及和浏览的话题之一。Twitter 和微博等微博平台被许多政客广泛使用,这些平台上拥有大量追随者和支持者。研究支持者的政治领导人网络至关重要,因为它可以在预测他们的政治未来时帮助决策。本研究重点关注巴基斯坦三位著名政治领导人的支持者网络,即伊姆兰·汗 (IK)、玛丽亚姆·纳瓦兹·谢里夫 (MNS) 和比拉瓦尔·布托·扎尔达里 (BBZ)。这是使用社交网络分析和语义分析完成的。所提出的方法(1)检测并移除假支持者,(2)挖掘政治家社交网络中的社区,(3) 调查支持者之间关于每个领导人的对话的支持者回复网络,最后,(4) 分析每个政治领导人的信息传播的转发网络。此外,政治家支持者的情绪分析是使用机器学习技术完成的,最终预测并揭示了三位政治领导人中最强大的支持者网络。对这些数据的分析显示,截至 2017 年 10 月,(1) IK 是三位政治家中最著名的,拥有最强大的支持者社区,同时以非常可控的方式使用 Twitter,(2) BBZ 在 Twitter 上拥有最弱的支持者网络, (3) 巴基斯坦政治领导人的支持者在推特上灵活,相互交流,任何支持者群体的孤立程度都较低。
更新日期:2020-09-01
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