Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation, and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from the source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.Comment: 8 pages, 4 figures, AAAI 202

Rumor detection technology based on ubiquitous relationship

This paper addresses the limitations of existing rumor detection methods that heavily rely on single or local features, which restrict their ability to capture comprehensive and detailed characteristics of rumors. The main objective of this study is to enhance the efficiency of rumor detection. To achieve this, we propose a novel approach that integrates user attributes, comment structure, and propagation models, introducing the concept of ubiquitous relationships for messages in social networks. We construct a Tweet-word-user ubiquitous relationship network using a propagation model and further leverage the Graph Convolutional Neural Network (GCN) to enhance semantic features. Consequently, we present a novel rumor detection model, the Ubiquitous Relationship-based Graph Convolutional Neural Network (U-GCN), which effectively combines user, text, and comment features within a unified framework, while also enhancing semantic features from the source post for comprehensive detection. Extensive experiments are conducted on two publicly available Twitter Datasets. The results demonstrate that our proposed U-GCN model achieves an accuracy rate of above 0.9, outperforming methods that solely consider single or local features. Our findings highlight the effectiveness of leveraging ubiquitous relationships in rumor detection

소셜 미디어 속 루머 탐지를 위한 그래프 합성곱 신경망과 어텐션 메커니즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. 김종권.Social media has been a great disseminator for new information and thoughts. Due to its accessibility of sharing information, however, social media has also become an ideal platform for propagations of rumors, fake news, and misinformation. Rumors on social media not only mislead the users of online but also affects the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Previous learning-based rumor detection methods adopted to use contents, users, or propagation features of rumors. However, the methods are limited to represent rumor propagation as static graphs, which arent optimal for capturing the dynamic information of the rumors. In this study, we propose a novel graph convolutional networks with attention mechanism model named, Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on two real-world datasets demonstrate that our model, Dynamic GCN, achieves superior results over the state-of-the-art models in the rumor detection task.소셜 미디어는 강력한 정보 전달력을 가진 매체로 새로운 정보와 생각의 전파 창구이다. 소셜 미디어의 특징인 접근성은 때론 루머, 가짜 뉴스, 잘못된 정보의 전파에서도 이상적인 플랫폼이 된다. 소셜 미디어 속 루머는 온라인 사용자를 오도할 뿐만 아니라 때론 현실 세계에도 큰 영향을 미친다. 따라서, 루머를 탐지하고 그 전파를 막는 노력이 요구된다. 기존의 루머 탐지 방법은 루머의 내용, 사용자, 또는 전파 과정의 정보를 특성으로 이용한다. 이러한 방법은 루머의 전파를 정적 그래프로 표현하며 그 구조적 특성을 이용한다. 하지만 이는 루머의 동적 특성을 포착하지 못한다는 한계를 가지고 있다. 본 연구에서는 그래프 합성곱 신경망 (graph convolutional networks: GCN)과 어텐션 메커니즘 (attention mechanism)을 활용한 동적 그래프 합성곱 신경망 (Dynamic GCN) 루머 탐지 모델을 제시한다. 먼저, 소셜 미디어 속 루머 게시글들 (posts) 과 그의 답장이 되는 글들(responsive posts)을 이용하여 루머의 전파 과정을 정적 그래프로 표현한다. 시간 정보를 통해 전파 과정을 포함하고 있는 정적 그래프의 집합인 그래프 스냅숏 (graph snapshot) 시퀀스 (sequence)를 만들게 된다. 어텐션 메커니즘을 활용한 그래프 스냅숏 표현 학습은 루머 전파의 구조적 시간적 정보를 모두 효과적으로 반영한다. 실제 트위터 데이터를 이용한 실험을 통하여 제시된 모델의 성능이 다른 비교 모델들보다 높음을 확인할 수 있었다.Chapter I Introduction 1 Chapter II Related Work 5 2.1 Rumor Detection 5 2.2 Graph Convolutional Networks 6 2.3 Learning Sequences & Attention Mechanism 7 Chapter III Problem Definition 9 Chapter IV Dynamic GCN with Attention Mechanism 11 4.1 Snapshot Generation 13 4.2 Graph Convolutional Networks 14 4.3 Readout Layer 15 4.4 Attention Mechanism 16 4.5 Training & Prediction 17 Chapter V Experiments 18 5.1 Datasets 18 5.2 Baselines 20 5.3 Experimental Setup & Implementation Details 21 5.4 Performance Evaluations 24 5.5 Ablation Study 25 Chapter VI Conclusion 30 Bibliography 31 초 록 40Maste

Evaluating the generalisability of neural rumour verification models

Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem

Combating Fake News on Social Media: A Framework, Review, and Future Opportunities

Social media platforms facilitate the sharing of a vast magnitude of information in split seconds among users. However, some false information is also widely spread, generally referred to as “fake news”. This can have major negative impacts on individuals and societies. Unfortunately, people are often not able to correctly identify fake news from truth. Therefore, there is an urgent need to find effective mechanisms to fight fake news on social media. To this end, this paper adapts the Straub Model of Security Action Cycle to the context of combating fake news on social media. It uses the adapted framework to classify the vast literature on fake news to action cycle phases (i.e., deterrence, prevention, detection, and mitigation/remedy). Based on a systematic and inter-disciplinary review of the relevant literature, we analyze the status and challenges in each stage of combating fake news, followed by introducing future research directions. These efforts allow the development of a holistic view of the research frontier on fighting fake news online. We conclude that this is a multidisciplinary issue; and as such, a collaborative effort from different fields is needed to effectively address this problem