Tracking social provenance in chains of retweets

In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has been propagated through social media. Tracking the provenance of information is becoming increasingly important as social platforms acquire m...

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Veröffentlicht in:Knowledge and information systems Jg. 65; H. 10; S. 3967 - 3994
Hauptverfasser: Migliorini, Sara, Gambini, Mauro, Quintarelli, Elisa, Belussi, Alberto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.10.2023
Springer Nature B.V
Schlagworte:
Algorithms
Application programming interface
Computer Science
Data Mining and Knowledge Discovery
Database Management
Information dissemination
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Provenance
Regular Paper
Social networks
Tracking
Information discovery
Constraint propagation
Path consistency algorithm
Data provenance
ISSN:0219-1377, 0219-3116
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Zusammenfassung:In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has been propagated through social media. Tracking the provenance of information is becoming increasingly important as social platforms acquire more relevance as source of news. In this scenario, Twitter is considered one of the most important social networks for information sharing and dissemination which can be accelerated through the use of retweets and quotes. However, the Twitter API does not provide a complete tracking of the retweet chains, since only the connection between a retweet and the original post is stored, while all the intermediate connections are lost. This can limit the ability to track the diffusion of information as well as the estimation of the importance of specific users, who can rapidly become influencers, in the news dissemination. This paper proposes an innovative approach for rebuilding the possible chains of retweets and also providing an estimation of the contributions given by each user in the information spread. For this purpose, we define the concept of Provenance Constraint Network and a modified version of the Path Consistency Algorithm. An application of the proposed technique to a real-world dataset is presented at the end of the paper.
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-023-01878-7