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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| Vydané v: | Knowledge and information systems Ročník 65; číslo 10; s. 3967 - 3994 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Springer London
01.10.2023
Springer Nature B.V |
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| ISSN: | 0219-1377, 0219-3116 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Belussi, Alberto Migliorini, Sara Gambini, Mauro Quintarelli, Elisa |
| Author_xml | – sequence: 1 givenname: Sara orcidid: 0000-0003-3675-7243 surname: Migliorini fullname: Migliorini, Sara email: sara.migliorini@univr.it organization: Department of Computer Science, University of Verona – sequence: 2 givenname: Mauro orcidid: 0000-0002-1832-1684 surname: Gambini fullname: Gambini, Mauro organization: Department of Computer Science, University of Verona – sequence: 3 givenname: Elisa orcidid: 0000-0001-6092-6831 surname: Quintarelli fullname: Quintarelli, Elisa organization: Department of Computer Science, University of Verona – sequence: 4 givenname: Alberto orcidid: 0000-0003-3023-8020 surname: Belussi fullname: Belussi, Alberto organization: Department of Computer Science, University of Verona |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37361376$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s00778-017-0486-1 10.3390/ijgi9040201 10.1145/2503792.2503797 10.1016/0004-3702(77)90007-8 10.3390/app10238394 10.1186/s40537-021-00410-4 10.1109/TIT.2011.2158885 10.1145/1084805.1084812 10.1145/3480956 10.1109/TKDE.2018.2889664 10.1109/TKDE.2016.2553667 10.1109/JSTSP.2015.2389191 10.1140/epjds18 10.1016/0004-3702(91)90006-6 10.1145/1639950.1640064 10.1145/3132847.3132973 10.1109/ISIT.2013.6620711 10.1609/icwsm.v10i1.14717 10.1145/2740908.2742475 10.1145/3430984.3431014 10.5281/zenodo.4445835 10.1145/2309996.2310017 10.1007/978-1-4614-6170-8_388 10.1145/2835776.2835800 10.1007/978-3-319-31232-3_83 10.1145/2567948.2580050 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 The Author(s) 2023. The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Issue | 10 |
| Keywords | Information discovery Constraint propagation Path consistency algorithm Data provenance |
| Language | English |
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| SubjectTerms | 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 |
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| Title | Tracking social provenance in chains of retweets |
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