Exploring the Effect of Spreading Fake News Debunking Based on Social Relationship Networks

Abstract

Fake news spreads rapidly on social networks; the aim of this study is to compare the characteristics of the social relationship networks (SRNs) of refuters and non-refuters to provide a scientific basis for developing effective strategies for debunking fake news. First, based on six types of fake news published on Sina Weibo (a Chinese microblogging website) during 2015–2019 in China, a deep learning method was used to build text classifiers for identifying debunked posts (DPs) and non-debunked posts (NDPs). Refuters and non-refuters were filtered out, and their follower–followee relationships on social media were obtained. Second, the differences between DPs and NDPs were compared in terms of the volume and growth rate of the posts across various types of fake news. The SRNs of refuters and non-refuters and the k-core decompositions of these SRNs were constructed, and the differences in the growth rates between DPs and NDPs were explored. Business-related fake news was revealed to be debunked better; society-related fake news, the most widely spread in China, was debunked poorly; and science- and politics-related fake news was debunked the worst. Additionally, more celebrity accounts, larger node sizes with follower-followee relationships in the SRNs, and more weakly connected components were found to lead to a faster growth rate in the dissemination of posts, regardless of whether the posts were DPs or NDPs. This study can help practitioners develop more effective strategies for debunking fake news on social media in China.