Empirical Evaluation of Link Deletion Methods for Limiting Information Diffusion on Social Media

TL;DR

This study empirically evaluates link deletion strategies on real social media data, revealing their limited effectiveness in reducing harmful information spread and highlighting challenges due to multiple seed users.

Contribution

It provides the first empirical assessment of link deletion methods on actual retweet data, contrasting with prior synthetic model-based evaluations.

Findings

Deleting 10-50% of links reduces cascade size to about 50% of original

Link deletion effectiveness is limited in real-world scenarios

Many cascades originate from multiple seed users, reducing strategy efficiency

Abstract

Although beneficial information abounds on social media, the dissemination of harmful information such as 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.