Empirical evaluation of link deletion methods for limiting information diffusion on social media (original) (raw)

Abstract

Although beneficial information abounds on social media, the dissemination of harmful information such as the so-called fake news has become a serious issue. Therefore, many researchers have devoted considerable effort to limiting the diffusion of harmful information. A promising approach to limiting diffusion of such information is link deletion methods in social networks. Link deletion methods have been shown to be effective in reducing the size of information diffusion cascades generated by synthetic models on a given social network. In this study, we evaluate the effectiveness of link deletion methods by using actual logs of retweet cascades, rather than by using synthetic diffusion models. Our results show that even after deleting 10–50% of links from a social network, the size of cascades after link deletion is estimated to be only 50% the original size under the optimistic estimation, which suggests that the effectiveness of the link deletion strategy for suppressing information diffusion is limited. Moreover, our results also show that there is a considerable number of cascades with many seed users, which renders link deletion methods inefficient.

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Acknowledgments

This work was partly supported by JSPS KAKENHI Grant Number 19K11917.

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Authors and Affiliations

1. Graduate School of Engineering Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan
   Shiori Furukawa
2. Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan
   Sho Tsugawa

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1. Shiori Furukawa
2. Sho Tsugawa

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Correspondence toShiori Furukawa.

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Furukawa, S., Tsugawa, S. Empirical evaluation of link deletion methods for limiting information diffusion on social media.Soc. Netw. Anal. Min. 12, 169 (2022). https://doi.org/10.1007/s13278-022-00994-6

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• Received: 01 February 2022
• Revised: 24 October 2022
• Accepted: 25 October 2022
• Published: 18 November 2022
• Version of record: 18 November 2022
• DOI: https://doi.org/10.1007/s13278-022-00994-6

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