  Citation: Firdaniza, F.; Ruchjana, B.N.; Chaerani, D.; Radianti, J. Information Diffusion Model in Twitter: A Systematic Literature Review. Information 2022, 13, 13. https://doi.org/10.3390/ info13010013 Academic Editor: Arkaitz Zubiaga Received: 4 December 2021 Accepted: 24 December 2021 Published: 28 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). information Review Information Diffusion Model in Twitter: A Systematic Literature Review Firdaniza Firdaniza 1, * , Budi Nurani Ruchjana 1 , Diah Chaerani 1 and Jaziar Radianti 2 1 Department of Mathematics, Universitas Padjadjaran, Sumedang 45363, Indonesia; budi.nurani@unpad.ac.id (B.N.R.); d.chaerani@unpad.ac.id (D.C.) 2 Department of Information Systems, University of Agder, 4630 Kristiansand, Norway; jaziar.radianti@uia.no * Correspondence: firdaniza@unpad.ac.id Abstract: Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion– exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion. Keywords: information diffusion; social media; Twitter; systematic literature review; bibliometric; continuous time Markov chain 1. Introduction Social media such as blogs, forums, chat applications, and social networking are platforms for online interactions regardless of a user’s physical location [1] and have become integrated into daily life. Twitter is a growing microblog that allows users to send text messages composed of up to 280 characters [2]. User activities on social media have been subjected to data collection and monitoring and are considered meaningful data sources for various public and private organizations, including in the industry and academia. The largest use of social media data comes from microblogs, as much as 46% [1]. Twitter data can be used in a remarkably diverse number of research studies, such as sentiment analyses [3,4], text analyses [5–8], opinion analyses [9,10], as well as analyses of influence or information diffusion [11–14]. Studies of information diffusion on Twitter are important as it is a topic that continues to attract researchers’ attention and is a subject useful for scrutinization and various advanced analyses. Information diffusion is defined as the process of information travelling from a sender to a set of receivers through a carrier. In the case of Twitter, the sender is the user who posted the tweet, the carrier is the tweet that was posted, and the recipients are followers of the user who posted the tweet [15]. Information 2022, 13, 13. https://doi.org/10.3390/info13010013 https://www.mdpi.com/journal/information