Open AccessInformation diffusion model using continuous time Markov chain on social media
Author(s) -
Firdaniza,
Budi Nurani Ruchjana,
Diah Chaerani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1722/1/012091
Subject(s) - markov chain , social media , computer science , information flow , diffusion , markov model , social network (sociolinguistics) , markov process , world wide web , statistics , mathematics , machine learning , philosophy , linguistics , physics , thermodynamics
On social media, information spreads quickly and can affect other users. In order for information to spread to many users, it is important to know how the information diffusion model or the flow of information is and who is the influencer on social media. In this paper, the Information Diffusion Model in social media was developed using the Continuous Time Markov Chain (CTMC). The tweet-retweet activity of social media users such as on Twitter can be seen as the CTMC model, because it fulfills the nature of Markov, i.e. retweets from subsequent users depend only on the current user’s retweet and do not depend on the retweet history of previous users. Users engaged in retweet tweets are state of CTMC. By using the tweet-retweet network simulation data including 10 users and 4 topics, the influencer rankings were obtained. An influencer rating is determined not only by the number of retweets, but also by the time it takes to get those retweets.