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Register a new userHetero-SCAN: Towards Social Context Aware Fake News Detection via Heterogeneous Graph Neural Network
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Added by
Jian Cui
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Fake news, false or misleading information presented as news, has a great impact on many aspects of society, such as politics and healthcare. To handle this emerging problem, many fake news detection methods have been proposed, applying Natural Language Processing (NLP) techniques on the article text. Considering that even people cannot easily distinguish fake news by news content, these text-based solutions are insufficient. To further improve fake news detection, researchers suggested graph-based solutions, utilizing the social context information such as user engagement or publishers information. However, existing graph-based methods still suffer from the following four major drawbacks: 1) expensive computational cost due to a large number of user nodes in the graph, 2) the error in sub-tasks, such as textual encoding or stance detection, 3) loss of rich social context due to homogeneous representation of news graphs, and 4) the absence of temporal information utilization. In order to overcome the aforementioned issues, we propose a novel social context aware fake news detection method, Hetero-SCAN, based on a heterogeneous graph neural network. Hetero-SCAN learns the news representation from the heterogeneous graph of news in an end-to-end manner. We demonstrate that Hetero-SCAN yields significant improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency.
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Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non-Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g., tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text-based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re-training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.
Community detection, aiming to group the graph nodes into clusters with dense inner-connection, is a fundamental graph mining task. Recently, it has been studied on the heterogeneous graph, which contains multiple types of nodes and edges, posing great challenges for modeling the high-order relationship between nodes. With the surge of graph embedding mechanism, it has also been adopted to community detection. A remarkable group of works use the meta-path to capture the high-order relationship between nodes and embed them into nodes embedding to facilitate community detection. However, defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. To alleviate this issue, in this paper, we propose to exploit the context path to capture the high-order relationship between nodes, and build a Context Path-based Graph Neural Network (CP-GNN) model. It recursively embeds the high-order relationship between nodes into the node embedding with attention mechanisms to discriminate the importance of different relationships. By maximizing the expectation of the co-occurrence of nodes connected by context paths, the model can learn the nodes embeddings that both well preserve the high-order relationship between nodes and are helpful for community detection. Extensive experimental results on four real-world datasets show that CP-GNN outperforms the state-of-the-art community detection methods.
Disinformation has long been regarded as a severe social problem, where fake news is one of the most representative issues. What is worse, todays highly developed social media makes fake news widely spread at incredible speed, bringing in substantial harm to various aspects of human life. Yet, the popularity of social media also provides opportunities to better detect fake news. Unlike conventional means which merely focus on either content or user comments, effective collaboration of heterogeneous social media information, including content and context factors of news, users comments and the engagement of social media with users, will hopefully give rise to better detection of fake news. Motivated by the above observations, a novel detection framework, namely graph comment-user advanced learning framework (GCAL) is proposed in this paper. User-comment information is crucial but not well studied in fake news detection. Thus, we model user-comment context through network representation learning based on heterogeneous graph neural network. We conduct experiments on two real-world datasets, which demonstrate that the proposed joint model outperforms 8 state-of-the-art baseline methods for fake news detection (at least 4% in Accuracy, 7% in Recall and 5% in F1). Moreover, the proposed method is also explainable.
Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detectors prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.
Recent years have witnessed the significant damage caused by various types of fake news. Although considerable effort has been applied to address this issue and much progress has been made on detecting fake news, most existing approaches mainly rely on the textual content and/or social context, while knowledge-level information---entities extracted from the news content and the relations between them---is much less explored. Within the limited work on knowledge-based fake news detection, an external knowledge graph is often required, which may introduce additional problems: it is quite common for entities and relations, especially with respect to new concepts, to be missing in existing knowledge graphs, and both entity prediction and link prediction are open research questions themselves. Therefore, in this work, we investigate textbf{knowledge-based fake news detection that does not require any external knowledge graph.} Specifically, our contributions include: (1) transforming the problem of detecting fake news into a subgraph classification task---entities and relations are extracted from each news item to form a single knowledge graph, where a news item is represented by a subgraph. Then a graph neural network (GNN) model is trained to classify each subgraph/news item. (2) Further improving the performance of this model through a simple but effective multi-modal technique that combines extracted knowledge, textual content and social context. Experiments on multiple datasets with thousands of labelled news items demonstrate that our knowledge-based algorithm outperforms existing counterpart methods, and its performance can be further boosted by the multi-modal approach.
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