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

2026-03-22

AI Summary

This study investigates effective strategies for mitigating the spread of harmful content—such as misinformation—on social media. Departing from prior approaches that rely on synthetic diffusion models, we present the first systematic evaluation of link-removal interventions using real-world retweet logs from actual social networks. By integrating cascade-level analysis with diffusion-scale estimation techniques, our empirical findings reveal that even removing 10%–50% of critical links reduces cascade size to only about half of its original scale. Moreover, we observe that multi-source diffusion—where information originates from multiple seed users—is pervasive and substantially diminishes the efficacy of link-removal strategies. These results highlight the inherent limitations of current intervention methods in complex, real-world scenarios and provide crucial empirical grounding for the development of more robust information control mechanisms.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Misinformation & Fake News

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Mitigating misinformation and disinformation

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.

Problem

• link deletion
• information diffusion
• social media
• fake news
• cascade suppression

Innovation

• link deletion
• information diffusion
• empirical evaluation
• social media
• cascade suppression