PESTO : A Post-User Fusion Network for Rumour Detection on Social Media (2023)

Abstract: Rumour detection on social media is an important topic due to the challenges of misinformation propagation and slow verification of misleading information. Most previous work focus on the response posts on social media, ignoring the useful characteristics of involved users and their relations. In this paper, we propose a novel framework, Post-User Fusion Network (PESTO), which models the patterns of rumours from both post diffusion and user social networks. Specifically, we propose a novel Chronologically-masked Transformer architecture to model both temporal sequence and diffusion structure of rumours, and apply a Relational Graph Convolutional Network to model the social relations of involved users, with a fusion network based on self-attention mechanism to incorporate the two aspects. Additionally, two data augmentation techniques are leveraged to improve the robustness and accuracy of our models. Empirical results on four datasets of English tweets show the superiority of the proposed method.

## introduction rumours, are unverified statements found in social media platforms, which can be damaging if they spread false information with social, economic and political impact @xcite . for instance: during the period of the u.s. 2016 presidential election, almost 529 different rumours about candidates were propagated on facebook and twitter which influenced voters' attitudes @xcite . to this end, it is important for social network platforms to develop effective strategies to combat against fake news and rumours. recognising misinformation from social media is challenging due to different sources of information required to gather in order to conduct an extensive analysis and reasoning on these sources. early efforts to tackle rumour detection and misinformation in social network platforms relied on manual verification from users or experts, however, this kind of approach is inefficient due to the substantial human effort and time to recognise a rumour after it has emerged. in recent years, automatic social context based rumour detection has attracted increasing attention. this area of research utilizes the collective wisdom of the social platforms by extracting signals from comments and/or replies towards a source claim @xcite @xcite @xcite @xcite @xcite . the key idea behind these work is that users from social media would contribute opinions, clues and evidence for distinguishing between false and valid information for rumour detection. therefore, the content of communication threads and the interaction between posts would be useful for rumour detection. however, apart from the threads of responses, the characteristics of the social network of users can also provide important clues for inferring news veracity. for example, eye-catching rumours usually attract mostly bot accounts to spread, who tend to follow many accounts but with few or no followers @xcite , such implicit patterns can also support the veracity of a claim. therefore, in this paper, we aim to propose a method which can model the post diffusion and the user social network jointly to detect social rumours. in terms of post diffusion modeling, a typical line of methods have exploited the characteristics of diffusion structure, such as tree-structured rvnn @xcite , bi-gcn @xcite and dsl @xcite , but ignore the temporal information and the implicit connections between posts. sequence-based models such recurrent neural networks (rnns) @xcite , @xcite and dcm @xcite flatten the tree structure and arrange posts in chronological order. they overcome some limitations of tree models but underexploit the diffusion 1 structure. for this sake, in the paper, we propose a chronologically-masked transformer architecture, which integrates both temporal and structural information to effectively model the rumour diffusion patterns. in terms of user network modeling, many off-the-shelf graph neural networks such as graph convolutional network (gcn) @xcite , @xcite , graph attention network (gat) @xcite , relational graph convolutional network (rgcn) @xcite ) can be leveraged. considering that a-follow-b and a-followed-by-b are different relations, we adopt rgcn for user network representation. in order to fuse the information in two aspects, we propose to use a self-attention layer for final information aggregation. since many existing rumour detection datasets are in small scale, we propose two data augmentation techniques: connection dropping and sub-conversation training to assist model training. we name the entire architecture as post-user fusion network (pesto). our experimental evaluation shows pesto improves performance over previous approaches. the contributions of our work are as follows: connection dropping and sub-conversation training, to reduce overfitting, making our model more robust and stable. ## related work existing detection approaches of fake claims can be generally categories into three groups based on the information utilized: (i) the content of the claim, (ii) knowledge from trustworthy sources and (iii) social response to the claim. our work in this paper falls into the last group, which exploits social replies and the involved user network to detect rumours. in this section, we briefly introduce each group of work. content-based detection: this line of studies studied specific linguistic cues such as verb quantity, word classes, word length, pronouns, nonobjectivity @xcite @xcite . these features are useful to detect satires or onion news, but might be unique to domains or topics. knowledge-based detection: fact checking websites such as politifact.com and snope.com leverage manual verification to debunk fake news or rumours, but fail to match the rapid emergence rate of misinformation nowadays. automated fact checking techniques rely on truthworthy sources such as wikipedia, but they might not work for latest news without evidences. social response-based detection social response information such as reply contents and propagation structures have been shown to be particularly useful for classifying rumours. ma et al. @xcite uses tree kernel to capture the similarity of propagation trees by counting their similar sub-structures in order to identify different types of rumours on twitter. ma et al. @xcite ) make use of tree-structured recursive neural network to model the propagation tree, and information from different nodes is aggregated recursively in either a bottom-up or a top-down manner. bian et al. @xcite also propose a bi-directional graph model named bi-gcn to explore both propagation and aggregation patterns by operating on both top-down and bottom-up propagation of rumours. however, the focus in these works is on using the static tree structure of tweet propagation, ignoring the temporal order and implicit connections between posts. for this sake, veyseh et al. @xcite and @xcite propose to apply self-attention mechanism @xcite to model implicit connections, but their direct usage of self-attention does not consider the propagation and aggregation characteristic of news conversation and underexploit the explicit diffusion structure. all of previous work do not take user networks into consideration, which provides important evidences for detection @xcite . ## experimental results in this section, we first compare the performance of our proposed pesto method with several baseline models. then, ablation studies are conducted to illustrate the impacts of each module. afterwards, early detection performance is evaluated. empirical results show the superiority of the proposed method. ## conclusion in this paper, we address the task of rumour detection with social contexts. a novel post-user fusion network (pesto) is proposed to learn both post propagation patterns and user network patterns in a rumour event. to be specific, we model the post diffusion patterns using a novel chronologicallymasked transformer, and use rgcn to represent the user social network, then a fusion module based on self-attention is applied to integrate the two aspects. experiments show that pesto outperforms state-of-the-art baselines significantly.