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Register a new userGraph Neural Networks with Continual Learning for Fake News Detection from Social Media
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Added by
Yi Han
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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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.
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.
Fake audio attack becomes a major threat to the speaker verification system. Although current detection approaches have achieved promising results on dataset-specific scenarios, they encounter difficulties on unseen spoofing data. Fine-tuning and retraining from scratch have been applied to incorporate new data. However, fine-tuning leads to performance degradation on previous data. Retraining takes a lot of time and computation resources. Besides, previous data are unavailable due to privacy in some situations. To solve the above problems, this paper proposes detecting fake without forgetting, a continual-learning-based method, to make the model learn new spoofing attacks incrementally. A knowledge distillation loss is introduced to loss function to preserve the memory of original model. Supposing the distribution of genuine voice is consistent among different scenarios, an extra embedding similarity loss is used as another constraint to further do a positive sample alignment. Experiments are conducted on the ASVspoof2019 dataset. The results show that our proposed method outperforms fine-tuning by the relative reduction of average equal error rate up to 81.62%.
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.
Todays social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such fake news based on their content, but it is difficult to agree on universal criteria of authenticity (which can be bypassed by adversaries once known). Besides, it is obviously impossible to have each news item checked by a human. In this paper, we a mechanism to limit the spread of fake news which is not based on content. It can be implemented as a plugin on a social media platform. The principle is as follows: a team of fact-checkers reviews a small number of news items (the most popular ones), which enables to have an estimation of each users inclination to share fake news items. Then, using a Bayesian approach, we estimate the trustworthiness of future news items, and treat accordingly those of them that pass a certain untrustworthiness threshold. We then evaluate the effectiveness and overhead of this technique on a large Twitter graph. We show that having a few thousands users exposed to one given news item enables to reach a very precise estimation of its reliability. We thus identify more than 99% of fake news items with no false positives. The performance impact is very small: the induced overhead on the 90th percentile latency is less than 3%, and less than 8% on the throughput of user operations.
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