Inform or flood: Estimating when retweets duplicate

Amit Tiroshi; Tsvi Kuflik; Shlomo Berkovsky

doi:10.1007/978-3-642-38844-6_22

Inform or flood: Estimating when retweets duplicate

Amit Tiroshi, Tsvi Kuflik, Shlomo Berkovsky

Department of Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The social graphs of Twitter users often overlap, such that retweets may cause duplicate posts is a user's incoming stream of tweets. Hence, it is important for the retweets to strike the balance between sharing information and flooding the recipients with redundant tweets. In this work, we present an exploratory analysis that assesses the degree of duplication caused by a set of real retweets. The results of the analysis show that although the overall duplication is not severe, high degree of duplication is caused by tweets of users with a small number of followers, which are retweeted by users with a small number of followers. We discuss the limitations of this work and propose several enhancements that we intend to pursue in the future.

Original language	English
Title of host publication	User Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings
Pages	267-273
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-642-38844-6_22
State	Published - 2013
Event	21st International Conference on User Modeling, Adaptation and Personalization, UMAP 2013 - Rome, Italy Duration: 10 Jun 2013 → 14 Jun 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7899 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on User Modeling, Adaptation and Personalization, UMAP 2013
Country/Territory	Italy
City	Rome
Period	10/06/13 → 14/06/13

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-38844-6_22

Cite this

Tiroshi, A., Kuflik, T., & Berkovsky, S. (2013). Inform or flood: Estimating when retweets duplicate. In User Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings (pp. 267-273). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7899 LNCS). https://doi.org/10.1007/978-3-642-38844-6_22

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title = "Inform or flood: Estimating when retweets duplicate",

abstract = "The social graphs of Twitter users often overlap, such that retweets may cause duplicate posts is a user's incoming stream of tweets. Hence, it is important for the retweets to strike the balance between sharing information and flooding the recipients with redundant tweets. In this work, we present an exploratory analysis that assesses the degree of duplication caused by a set of real retweets. The results of the analysis show that although the overall duplication is not severe, high degree of duplication is caused by tweets of users with a small number of followers, which are retweeted by users with a small number of followers. We discuss the limitations of this work and propose several enhancements that we intend to pursue in the future.",

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booktitle = "User Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings",

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Tiroshi, A, Kuflik, T & Berkovsky, S 2013, Inform or flood: Estimating when retweets duplicate. in User Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7899 LNCS, pp. 267-273, 21st International Conference on User Modeling, Adaptation and Personalization, UMAP 2013, Rome, Italy, 10/06/13. https://doi.org/10.1007/978-3-642-38844-6_22

Inform or flood: Estimating when retweets duplicate. / Tiroshi, Amit; Kuflik, Tsvi; Berkovsky, Shlomo.
User Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings. 2013. p. 267-273 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7899 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The social graphs of Twitter users often overlap, such that retweets may cause duplicate posts is a user's incoming stream of tweets. Hence, it is important for the retweets to strike the balance between sharing information and flooding the recipients with redundant tweets. In this work, we present an exploratory analysis that assesses the degree of duplication caused by a set of real retweets. The results of the analysis show that although the overall duplication is not severe, high degree of duplication is caused by tweets of users with a small number of followers, which are retweeted by users with a small number of followers. We discuss the limitations of this work and propose several enhancements that we intend to pursue in the future.

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Tiroshi A, Kuflik T, Berkovsky S. Inform or flood: Estimating when retweets duplicate. In User Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings. 2013. p. 267-273. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-38844-6_22

Inform or flood: Estimating when retweets duplicate

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