Analysis Report for Statistics in the Twitter Network

Analysis Report for Statistics in the Twitter Network

Parvathi R., Yamani Sai Asish, Pattabiraman V.
Copyright: © 2021 |Pages: 9
DOI: 10.4018/978-1-7998-7728-8.ch003
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Abstract

Twitter is the most popular social networking service across the world. In Twitter, the messages are known as tweets. Tweets are mainly text-based posts that can be up to 140 characters long which can reach the author's subscribers. These subscribers are also known as followers. Such subscriptions form a direct connection. But these connections are not always symmetric. In this study, the authors have assumed that if two nodes are connected, then the tweet is propagated between them without any other conditions. But using sentiment analysis, the general opinion of people about various things can be figured. The Twitter data set analyzed includes almost 20k nodes and 33k edges, where the visualization is done with software called Gephi. Later a deep dive analysis is done by calculating some of the metrics such as degree centrality and closeness centrality for the obtained Twitter network. Using this analysis, it is easy to find the influencers in the Twitter network and also the various groups involved in the network.
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Literature Survey

Petrovic (Petrovic et al., 2011) discussed how the tweets are propagated using the Twitter network. They analyzed three categories a) By following users b) By tweeting c) By re-tweeting. The authors argued that the re-tweeting is potentially the best, and proves why it is so by taking examples and by proving using algorithms. And he concludes as follows. The most important task in understanding retweeting is to say if a particular tweet is going to be Re-tweeted or not, which is the main motive of this work. Analysis was performed on humans which has shown a positive result. Machine Learning approach based on passive-aggressive algorithm have substantially outperformed the previous known baseline.

Carlos (Castillo et al., 2011) analyzed the Information credibility on Twitter by analysing the credibility of the twitter information is and how humans react to it.

Information Propagation(Speed and its Patterns) German Tweets has shown that the information travels through a certain pattern in the network. It has also proves that the friend relationships also significantly influence the information propagation speed on the Twitter network. Two approaches was used to evaluate technically as well as based on how a certain topic matches a pattern and how prominent the friends are compared to the rest of the users (Tareaf, et al., 2018).

Twitter presence of (HSS) journal, Titles which are obtained from mainstream citation indices, by analysing how the interaction and communication patterns in them (Raamkumar, et al., 2018). Their research utilizes webometric collection, descriptive analysis, and also the social network analysis. So the results indicate the presence of the HSS journals on Twitter. Sharing of general websites is seen as the key activity that is performed by (HSS) journals on Twitter. Among these the web content from the news portals and magazines are highly different. Sharing of articles and retweeting isn’t the most common thing to be observed. Inter-journal communication is also witnessed, But it is less with journals from other indexes.

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