Account credibility inference based on news-sharing networks

TL;DR

This paper introduces methods to infer social media account credibility by analyzing information diffusion networks, utilizing trust signals from reshare and source networks to improve misinformation detection.

Contribution

It extends network centrality and graph embedding techniques to assess account credibility based on diffusion patterns, demonstrating high accuracy across platforms.

Findings

Both trust networks provide useful credibility signals.

Accounts sharing similar sources tend to have similar credibility.

Proposed methods achieve high accuracy in credibility estimation.

Abstract

The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information diffusion patterns, in particular leveraging two networks: the reshare network, capturing an account's trust in other accounts, and the bipartite account-source network, capturing an account's trust in media sources. We extend network centrality measures and graph embedding techniques, systematically comparing these algorithms on data from diverse contexts and social media platforms. We demonstrate that both kinds of trust networks provide useful signals for estimating account credibility. Some of the proposed methods yield high accuracy, providing promising solutions to promote the dissemination of reliable information in online communities. Two kinds of homophily emerge from our results: accounts tend to have similar credibility if they reshare each other's content or share content from similar sources. Our methodology invites further investigation into the relationship between accounts and news sources to better characterize misinformation spreaders.