Social network analysis of information diffusion on Sina Weibo micro-blog system

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.

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Exploring Crisis Communication and Information Dissemination on Social Media: Social Network Analysis of Hurricane Irma Tweets

Journal of International Crisis and Risk Communication Research ◽

10.30658/jicrcr.3.2.3 ◽

2020 ◽

Vol 3 (2) ◽

pp. 179-210 ◽

Cited By ~ 1

Author(s):

Xianlin Jin

Keyword(s):

Social Media ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Crisis Communication ◽

Information Diffusion ◽

Information Dissemination ◽

Opinion Leader ◽

Communication Model ◽

Hurricane Irma

This study utilized social network analysis to identify the top 10 Twitter influentials during the Hurricane Irma crisis period and examined the relationship between social media attributes and the bridge influence of controlling information flow. The number of a user’s followers and tweets significantly predicted one’s control of information. Crisis information tended to be shared in scattered subgroups. Social network boundaries impeded information diffusion, and the communication pattern was largely one-way. The findings partially supported the opinion leader argument while indicating that influentials can directly generate information, which is consistent with the social-mediated crisis communication model. Such findings will contribute to crisis literature and help emergency management professionals advance social media usage to disseminate crisis information, build effective communication, and provide immediate disaster relief responses

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Social Network Analysis

Advances in Computer and Electrical Engineering - Handbook of Research on Contemporary Perspectives on Web-Based Systems ◽

10.4018/978-1-5225-5384-7.ch017 ◽

2018 ◽

pp. 388-415

Author(s):

Soufiana Mekouar

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Communication Systems ◽

Information Diffusion ◽

Social Systems ◽

Critical Role ◽

Network Applications ◽

Graph Measure ◽

Statistical Graph

The study of social network analysis has grown in popularity in the past decades and has been used in many areas. It is an interesting and useful field that gained an increasing popularity due to the explosion of social media that has emerged with advances in communication systems, which play a critical role in forming human activities and interactions in social systems. The authors present some techniques from a data mining perspective and statistical graph measure that can be used in various applications such as to perform community detection, clustering in a social network, identify spurious and anomalous users, predict links between vertices in a social network, model and improve the information diffusion, design trust models, and improve other applications. Then, the authors provide a recent literature review of such applications and thus outline challenges of social network applications.

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

Scientific Programming ◽

10.1155/2020/9353120 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Asif Khan ◽

Huaping Zhang ◽

Jianyun Shang ◽

Nada Boudjellal ◽

Arshad Ahmad ◽

...

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Information Diffusion ◽

Semantic Analysis ◽

Machine Learning Techniques ◽

Political Leader ◽

Political Leaders ◽

Social Media Networks ◽

Learning Techniques

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.

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