A Unified Propagation Forest-based Framework for Fake News Detection (2022)

Abstract: Fake news’s quick propagation on social media brings severe social ramifications and economic damage. Previous fake news detection usually learn semantic and structural patterns within a single target propagation tree. However, they are usually limited in narrow signals since they do not consider latent information cross other propagation trees. Motivated by a common phenomenon that most fake news is published around a specific hot event/topic, this paper develops a new concept of propagation forest to naturally combine propagation trees in a semantic-aware clustering. We propose a novel Unified Propagation Forest-based framework (UniPF) to fully explore latent correlations between propagation trees to improve fake news detection. Besides, we design a root-induced training strategy, which encourages representations of propagation trees to be closer to their prototypical root nodes. Extensive experiments on four benchmarks consistently suggest the effectiveness and scalability of UniPF.

## introduction recently, social media platforms have facilitated information dissemination greatly. nevertheless, they quicken the proliferation of fake news as well due to the lack of authoritative regulators @xcite . its extensive dissemination would trigger great panic in society and severely impair the public and individuals @xcite @xcite . to keep social media a healthy environment, it is desirable and socially beneficial to detect fake news. among previous works on automatic fake news detection, textual news material is utilized by almost all extant studies on fake news detection @xcite @xcite @xcite . however, because fake news is purposefully designed to deceive readers by imitating actual news, detecting and distinguishing them solely from news material is challenging @xcite . hence, an increasing number of works @xcite @xcite @xcite have been devoted to learning potential propagation patterns by investigating relationships among tweets for each news article. however, most approaches usually consider that propagation trees are independent and ignore latent correlations across different propagation trees, which are supportive for identifying fake news for two-fold reason. 1) most fake news is usually published deliberately around a specific hot event / topic and then widely disseminated in reality @xcite . hence, potential semantical connections may exist across them. 2) some spreaders were social bots that are manipulated by a malicious group @xcite . these deliberate and organized behaviors during propagation may lead to similar structural patterns across two propagation trees. we believe that capturing these vital semantic and structural characteristics across propagation trees is beneficial to understanding the target propagation tree, accordingly to make more accurate detection. @xcite built a global user-tweet heterogeneous graph according to similar participants but ignored semantic relations. @xcite introduced word nodes and constructed a user-tweet-word graph to capture fine-grained semantic relations between source news. however, these fine-grained (e.g., word-level) correlations between news contents may compensate for semantic information to some extent since some fine-grained words may suffer from the polysemy problem @xcite . given two examples, the real news is donald trump: . . . senate i believe really wants to get something done because obamacare is dead,...obamacare is absolutely dead.; the fake news is donald trump was pronounced dead this morning following what some are describing as a violent heart attack.... ## methodology this section offers unified propagation forestbased (unipf) framework to boost fake news detection by examining latent correlations across propagation trees in a semantic-clustering manner. figure 1 depicts an overview of unipf framework. it consists of three key components, propagation forest construction, prototype-aware embedding enhancement, and root-induced training strategy. ## experiments in this section, we experimentally evaluate the performance of our proposed unipf. ## related work some works exploit user information to detect fake news such as user credibility @xcite , user profiles @xcite and user social relations @xcite . however, due to user privacy protection, user's information is not allowed recorded on social media. this paper focuses on detecting fake news based on anonymous propagation. we review related works in two groups: content-and propagation-based detection methods. ## conclusion in this paper, we investigate deep correlations between propagation trees and propose a generic framework unipf for improving fake news detection. unipf builds a propagation forest to naturally combine propagation trees in a semantic-clustering manner. the representation of the target sample is enhanced with shared patterns from similar propagation trees and prototypical root nodes. besides, a node-induced training strategy is designed for guaranteeing the consistency of the representation of propagation trees and newly prototypical root nodes. experiments on four datasets consistently prove the scalability and effectiveness of unipf. for future work, we will explore more available information (such as images) for perfecting propagation forest to enhance detection.