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

Fake News Detection Through Graph-based Neural Networks: A Survey

The popularity of online social networks has enabled rapid dissemination of information. People now can share and consume information much more rapidly than ever before. However, low-quality and/or accidentally/deliberately fake information can also spread rapidly. This can lead to considerable and negative impacts on society. Identifying, labelling and debunking online misinformation as early as possible has become an increasingly urgent problem. Many methods have been proposed to detect fake news including many deep learning and graph-based approaches. In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news. In this paper, we present a systematic review of fake news detection studies based on graph-based and deep learning-based techniques. We classify existing graph-based methods into knowledge-driven methods, propagation-based methods, and heterogeneous social context-based methods, depending on how a graph structure is constructed to model news related information flows. We further discuss the challenges and open problems in graph-based fake news detection and identify future research directions.Comment: 18 pages, 3 tables, 7 figure

Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks

As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets. Despite being widely used, we find that these datasets suffer from spurious correlations, which are ignored by existing studies and lead to severe overestimation of existing rumor detection performance. The spurious correlations stem from three causes: (1) event-based data collection and labeling schemes assign the same veracity label to multiple highly similar posts from the same underlying event; (2) merging multiple data sources spuriously relates source identities to veracity labels; and (3) labeling bias. In this paper, we closely investigate three of the most popular rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and propose event-separated rumor detection as a solution to eliminate spurious cues. Under the event-separated setting, we observe that the accuracy of existing state-of-the-art models drops significantly by over 40%, becoming only comparable to a simple neural classifier. To better address this task, we propose Publisher Style Aggregation (PSA), a generalizable approach that aggregates publisher posting records to learn writing style and veracity stance. Extensive experiments demonstrate that our method outperforms existing baselines in terms of effectiveness, efficiency and generalizability.Comment: Accepted to ECML-PKDD 202

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field

False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news