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.

翻译：尽管社交媒体中充斥着有益信息，但“假新闻”等有害信息的传播已成为严重问题。因此，许多研究者致力于限制有害信息的扩散。社交网络中的链接删除方法是一种具有前景的扩散限制策略。现有研究表明，链接删除方法能有效缩小给定社交网络上由合成模型生成的信息扩散级联规模。本研究通过使用真实转发级联日志而非合成扩散模型，评估了链接删除方法的有效性。结果表明：即使在乐观估计下，从社交网络中删除10%-50%的链接后，级联规模预计仅降低至原始规模的50%，这表明链接删除策略抑制信息扩散的效果有限。此外，研究还发现存在大量具有多个种子用户的级联现象，这导致链接删除方法的低效性。