{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T13:26:42Z","timestamp":1792848402133,"version":"4.5.0"},"reference-count":66,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Riset Disertasi Doktor Universitas Padjadjaran (RDDU), Indonesia","award":["5879\/UN6.D\/LT\/2019"],"award-info":[{"award-number":["5879\/UN6.D\/LT\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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\u2013exclusion criteria. At this stage, we mapped the authors\u2019 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.<\/jats:p>","DOI":"10.3390\/info13010013","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T02:31:27Z","timestamp":1640745087000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Information Diffusion Model in Twitter: A Systematic Literature Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2942-6414","authenticated-orcid":false,"given":"Firdaniza","family":"Firdaniza","sequence":"first","affiliation":[{"name":"Department of Mathematics, Universitas Padjadjaran, Sumedang 45363, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7580-604X","authenticated-orcid":false,"given":"Budi","family":"Ruchjana","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Universitas Padjadjaran, Sumedang 45363, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2073-1754","authenticated-orcid":false,"given":"Diah","family":"Chaerani","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Universitas Padjadjaran, Sumedang 45363, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaziar","family":"Radianti","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Agder, 4630 Kristiansand, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.chb.2018.08.039","article-title":"Social media big data analytics: A survey","volume":"101","author":"Ghani","year":"2019","journal-title":"Comput. Hum. Behav."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1016\/j.future.2019.10.013","article-title":"Towards a large-scale twitter observatory for political events","volume":"110","author":"Fernando","year":"2019","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.future.2020.01.005","article-title":"Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers","volume":"106","author":"Ruz","year":"2020","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1108\/OIR-02-2015-0067","article-title":"Visual Twitter Analytics (Vista): Temporally changing sentiment and the discovery of emergent themes within sport event tweets","volume":"40","author":"Hoeber","year":"2016","journal-title":"Online Inf. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.dss.2019.03.002","article-title":"A text analytics approach for online retailing service improvement: Evidence from Twitter","volume":"121","author":"Ibrahim","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Radianti, J., Hiltz, S.R., and Labaka, L. (2016, January 5\u20138). An overview of public concerns during the recovery period after a major earthquake: Nepal twitter analysis. Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA.","DOI":"10.1109\/HICSS.2016.25"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102107","DOI":"10.1016\/j.ipm.2019.102107","article-title":"Automatic identification of eyewitness messages on twitter during disasters","volume":"57","author":"Zahra","year":"2020","journal-title":"Inf. Processing Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sakaki, T., Okazaki, M., and Matsuo, Y. (2010, January 26\u201330). Earthquake shakes Twitter users: Real-time event detection by social sensors. Proceedings of the 19th International World Wide Web Conference, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772777"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.automatica.2018.11.023","article-title":"Opinion influence and evolution in social networks: A Markovian agents model","volume":"100","author":"Bolzern","year":"2019","journal-title":"Automatica"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Barnaghi, P., Ghaffari, P., and Breslin, J.G. (April, January 29). Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment. Proceedings of the 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, UK.","DOI":"10.1109\/BigDataService.2016.36"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5115","DOI":"10.1016\/j.eswa.2014.02.038","article-title":"Social network user influence sense-making and dynamics prediction","volume":"41","author":"Li","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.knosys.2017.07.003","article-title":"Predicting information diffusion probabilities in social networks: A Bayesian networks based approach","volume":"133","author":"Varshney","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1080\/08961530.2020.1818158","article-title":"Information Diffusion in Halal Food Social Media: A Social Network Approach","volume":"33","author":"Mostafa","year":"2021","journal-title":"J. Int. Consum. Mark."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.procs.2020.04.073","article-title":"Modeling Information Diffusion in Online Social Networks Using SEI Epidemic Model","volume":"171","author":"Kumar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","first-page":"456","article-title":"Predicting information diffusion on twitter a deep learning neural network model using custom weighted word features","volume":"12066","author":"Kushwaha","year":"2020","journal-title":"Responsib. Des. Implement. Use Inf. Commun. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jnca.2018.01.005","article-title":"Influence analysis in social networks: A survey","volume":"106","author":"Peng","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cha, M., Haddai, H., Benevenuto, F., and Gummadi, K.P. (2010, January 23\u201326). Measuring user influence in Twitter: The million follower fallacy. Proceedings of the International AAAI conference on web and social media, Washington, DC, USA.","DOI":"10.1609\/icwsm.v4i1.14033"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100614","DOI":"10.1016\/j.swevo.2019.100614","article-title":"Identifying influential nodes based on ant colony optimization to maximize profit in social networks","volume":"51","author":"Salavati","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"121536","DOI":"10.1016\/j.physa.2019.121536","article-title":"User recommendation for promoting information diffusion in social networks","volume":"534","author":"Li","year":"2019","journal-title":"Phys. Stat. Mech. Its Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3481","DOI":"10.1007\/s10115-020-01459-y","article-title":"Measuring time-sensitive user influence in Twitter","volume":"62","author":"Rezaie","year":"2020","journal-title":"Knowl. Inf. Syst."},{"key":"ref_21","first-page":"178","article-title":"Detecting Influential Users in a Trending Topic Community Using Link Analysis Approach","volume":"13","author":"Oo","year":"2020","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.ins.2016.07.012","article-title":"Influence maximization in social networks based on discrete particle swarm optimization","volume":"367","author":"Gong","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.knosys.2017.01.014","article-title":"Two evidential data based models for influence maximization in Twitter","volume":"121","author":"Jendoubi","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"189786","DOI":"10.1109\/ACCESS.2020.3029442","article-title":"Potential-driven model for influence maximization in social networks","volume":"8","author":"Felfli","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jretconser.2019.03.012","article-title":"Measuring social media influencer index-insights from facebook, Twitter and Instagram","volume":"49","author":"Arora","year":"2019","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e01677","DOI":"10.1016\/j.heliyon.2019.e01677","article-title":"How to quantify social media in fl uencers: An empirical application at the Teatro alla Scala","volume":"5","author":"Deborah","year":"2019","journal-title":"Heliyon"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s10844-020-00623-8","article-title":"Modeling information diffusion in online social networks using a modified forest-fire model","volume":"56","author":"Kumar","year":"2020","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_28","first-page":"383","article-title":"A review of critical research areas under information diffusion in social networks","volume":"11","author":"Kakar","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_29","first-page":"1275","article-title":"State-of-art review of information diffusion models and their impact on social network vulnerabilities","volume":"34","author":"Razaque","year":"2019","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_30","unstructured":"Alamsyah, A., and Rahardjo, B. (2021). Social Network Analysis Taxonomy Based on Graph Representation. arXiv."},{"key":"ref_31","unstructured":"Hamzah, M. (2018, January 15\u201316). A Taxonomy of Twitter Data Analytics Techniques. Proceedings of the 32nd IBIMA Conference, Seville, Spain."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, M., Wang, X., Gao, K., and Zhang, S. (2017). A survey on information diffusion in online social networks: Models and methods. Information, 8.","DOI":"10.3390\/info8040118"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1007\/978-981-13-3143-5_35","article-title":"A Survey on Information Diffusion Models in Social Networks","volume":"Volume 956","author":"Singh","year":"2018","journal-title":"International Conference on Advanced Informatics for Computing Research"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.osnem.2018.04.001","article-title":"Retweet: A popular information diffusion mechanism\u2014A survey paper","volume":"6","author":"Firdaus","year":"2018","journal-title":"Online Soc. Netw. Media"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.ipm.2016.04.003","article-title":"Measuring user influence on Twitter: A survey","volume":"52","author":"Riquelme","year":"2016","journal-title":"Inf. Processing Manag."},{"key":"ref_36","unstructured":"Kitchenham, B. (2004). Procedures for Performing Systematic Reviews, Keele University. Joint Technical Report."},{"key":"ref_37","unstructured":"Silva, R.L.S., and Neiva, F.W. (2016). Systematic Literature Review in Computer Science\u2014A Practical Guide, Federal University of Juiz de Fora. Technical Report."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s11192-015-1645-z","article-title":"The bibliometric analysis of scholarly production: How great is the impact?","volume":"105","author":"Ellegaard","year":"2015","journal-title":"Scientometrics"},{"key":"ref_39","unstructured":"van Eck, N.J., and Waltman, L. (2013). VOSviewer Manual, Univeristeit Leiden."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.joi.2017.08.007","article-title":"Bibliometrix: An R-tool for comprehensive science mapping analysis","volume":"11","author":"Aria","year":"2017","journal-title":"J. Informetr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/BF02019280","article-title":"Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry","volume":"22","author":"Callon","year":"1991","journal-title":"Scientometrics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/JSYST.2020.2993542","article-title":"Modeling Social Contagion and Information Diffusion in Complex Socio-Technical Systems","volume":"14","author":"Muhlmeyer","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"39847","DOI":"10.1109\/ACCESS.2020.2976126","article-title":"Detection of Rapidly Spreading Hashtags via Social Networks","volume":"8","author":"Kim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"062306","DOI":"10.1103\/PhysRevE.97.062306","article-title":"Factors influencing message dissemination through social media","volume":"97","author":"Zheng","year":"2018","journal-title":"Phys. Rev. E"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.eswa.2019.05.047","article-title":"Anomalous information diffusion in social networks: Twitter and Digg","volume":"134","author":"Foroozani","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3470","DOI":"10.1016\/j.physa.2013.03.048","article-title":"A stochastic model of tweet diffusion on the Twitter network","volume":"392","author":"Kawamoto","year":"2013","journal-title":"Phys. Stat. Mech. Its Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jom.2016.05.007","article-title":"Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises","volume":"45","author":"Yoo","year":"2016","journal-title":"J. Oper."},{"key":"ref_48","first-page":"217","article-title":"Emotions and information diffusion in social media\u2014Sentiment of microblogs and sharing behavior","volume":"29","author":"Stieglitz","year":"2013","journal-title":"J. Manag. Inf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1108\/IMDS-07-2016-0290","article-title":"Effects of source influence and peer referrals on information diffusion in Twitter","volume":"117","author":"Kwon","year":"2017","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102321","DOI":"10.1016\/j.ipm.2020.102321","article-title":"Effective influence estimation in twitter using temporal, profile, structural and interaction characteristics","volume":"57","author":"Agarwal","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.1109\/TSP.2014.2339799","article-title":"Evolutionary dynamics of information diffusion over social networks","volume":"62","author":"Jiang","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"20","DOI":"10.4018\/jswis.2012100102","article-title":"Modeling indirect influence on Twitter","volume":"8","author":"Shuai","year":"2012","journal-title":"Int. J. Semant. Web Inf. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1142\/S2196888819500155","article-title":"Information Diffusion on Complex Networks: A Novel Approach Based on Topic Modeling and Pretopology Theory","volume":"6","author":"Ho","year":"2019","journal-title":"Vietnam J. Comput. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"10275","DOI":"10.1007\/s00521-019-04568-0","article-title":"Consensus-based aggregation for identification and ranking of top-k influential nodes","volume":"32","author":"Tidke","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1177\/0165551519837190","article-title":"Multimodal ensemble approach to identify and rank top-k influential nodes of scholarly literature using Twitter network","volume":"46","author":"Tidke","year":"2020","journal-title":"J. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10660-018-9316-9","article-title":"Product information diffusion in a social network","volume":"20","author":"Zhang","year":"2020","journal-title":"Electron. Commer. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/TCSS.2019.2907553","article-title":"Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks","volume":"6","author":"Bhowmick","year":"2019","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1016\/j.knosys.2018.10.020","article-title":"Influence Factorization for identifying authorities in Twitter","volume":"163","author":"Alp","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.knosys.2017.11.021","article-title":"Identifying topical influencers on twitter based on user behavior and network topology","volume":"141","author":"Alp","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40649-017-0040-8","article-title":"Using attractiveness model for actors ranking in social media networks","volume":"4","author":"Qasem","year":"2017","journal-title":"Comput. Soc. Networks"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hamzehei, A., Jiang, S., Koutra, D., Wong, R., and Chen, F. (2017). Topic-based social influence measurement for social networks. Australas. J. Inf. Syst., 21.","DOI":"10.3127\/ajis.v21i0.1552"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1093\/comnet\/cnu024","article-title":"Ranking twitter influence by combining network centrality and influence observables in an evolutionary model","volume":"2","author":"Simmie","year":"2014","journal-title":"J. Complex Netw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1016\/j.procs.2020.03.205","article-title":"Social Network Influencer Rank Recommender Using Diverse Features from Topical Graph","volume":"167","author":"Mittal","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1960010","DOI":"10.1142\/S0218213019600108","article-title":"Estimating Twitter Influential Users by Using Cluster-Based Fusion Methods","volume":"28","author":"Kanavos","year":"2019","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Sankar, C.P., Asharaf, S., and Kumar, K.S. (2016). Learning from bees: An approach for influence maximization on viral campaigns. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0168125"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40649-015-0013-8","article-title":"A study on the influential neighbors to maximize information diffusion in online social networks","volume":"2","author":"Kim","year":"2015","journal-title":"Comput. Soc. Netw."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/1\/13\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:55:02Z","timestamp":1760169302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/1\/13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,28]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["info13010013"],"URL":"https:\/\/doi.org\/10.3390\/info13010013","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,28]]}}}