Information Diffusion Model in Twitter: A Systematic Literature Review
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). T...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/1/13 |
| _version_ | 1797493142897819648 |
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| author | Firdaniza Firdaniza Budi Nurani Ruchjana Diah Chaerani Jaziar Radianti |
| author_facet | Firdaniza Firdaniza Budi Nurani Ruchjana Diah Chaerani Jaziar Radianti |
| author_sort | Firdaniza Firdaniza |
| collection | DOAJ |
| description | 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 <i>R-bibliometrix</i> 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. |
| first_indexed | 2024-03-10T01:15:46Z |
| format | Article |
| id | doaj.art-0d886204c3bf44acb8e6341623148c37 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T01:15:46Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-0d886204c3bf44acb8e6341623148c372023-11-23T14:08:21ZengMDPI AGInformation2078-24892021-12-011311310.3390/info13010013Information Diffusion Model in Twitter: A Systematic Literature ReviewFirdaniza Firdaniza0Budi Nurani Ruchjana1Diah Chaerani2Jaziar Radianti3Department of Mathematics, Universitas Padjadjaran, Sumedang 45363, IndonesiaDepartment of Mathematics, Universitas Padjadjaran, Sumedang 45363, IndonesiaDepartment of Mathematics, Universitas Padjadjaran, Sumedang 45363, IndonesiaDepartment of Information Systems, University of Agder, 4630 Kristiansand, NorwayInformation 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 <i>R-bibliometrix</i> 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.https://www.mdpi.com/2078-2489/13/1/13information diffusionsocial mediaTwittersystematic literature reviewbibliometriccontinuous time Markov chain |
| spellingShingle | Firdaniza Firdaniza Budi Nurani Ruchjana Diah Chaerani Jaziar Radianti Information Diffusion Model in Twitter: A Systematic Literature Review Information information diffusion social media systematic literature review bibliometric continuous time Markov chain |
| title | Information Diffusion Model in Twitter: A Systematic Literature Review |
| title_full | Information Diffusion Model in Twitter: A Systematic Literature Review |
| title_fullStr | Information Diffusion Model in Twitter: A Systematic Literature Review |
| title_full_unstemmed | Information Diffusion Model in Twitter: A Systematic Literature Review |
| title_short | Information Diffusion Model in Twitter: A Systematic Literature Review |
| title_sort | information diffusion model in twitter a systematic literature review |
| topic | information diffusion social media systematic literature review bibliometric continuous time Markov chain |
| url | https://www.mdpi.com/2078-2489/13/1/13 |
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