Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks

By Yang Liu, Yi-fang Brook Wu. AAAI Conference on Artificial Intelligence, 2018

681 citations recorded by the source corpus.

In the midst of today's pervasive influence of social media, automatically detecting fake news is drawing significant attention from both the academic communities and the general public. Existing detection approaches rely on machine learning algorithms with a variety of news characteristics to detect fake news. However, such approaches have a major limitation on detecting fake news early, i.e., the information required for detecting fake news is often unavailable or inadequate at the early stage of news propagation. As a result, the accuracy of early detection of fake news is low. To address this limitation, in this paper, we propose a novel model for early detection of fake news on social media through classifying news propagation paths. We first model the propagation path of each news story as a multivariate time series in which each tuple is a numerical vector representing characteristics of a user who engaged in spreading the news. Then, we build a time series classifier that incorporates both recurrent and convolutional networks which capture the global and local variations of user characteristics along the propagation path respectively, to detect fake news. Experimental results on three real-world datasets demonstrate that our proposed model can detect fake news with accuracy 85% and 92% on Twitter and Sina Weibo respectively in 5 minutes after it starts to spread, which is significantly faster than state-of-the-art baselines.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d124b561ea719ab3bca419336d5d9c8a6c5f7ae8

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Citations in the corpus

Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “Liu et al. (2018) use user proﬁles on the news propagation path to determine the truth of the news.”
MVAE: Multimodal Variational Autoencoder for Fake News Detection (2019, background): “Note that fair comparisons with methods proposed in [13, 15] are not possible since they use addition information like tweet propagation data.”
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach (2021, methodology): “In many existing studies [13, 23, 28, 42], authors have explored the problem of fake news employing a real-world fake news dataset: Fake-News.”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, methodology): “The reason is that how user proﬁles in the retweet sequence look like is also important, as suggested by CRNN (Liu and Wu, 2018), and should”
The Limitations of Stylometry for Detecting Machine-Generated Fake News (2019, background): “(c) In the article extension case (Section 5), the bold red text is the generation of GPT-2 Medium to extend the preﬁx. incomplete data, especially given the need to detect fake news early (Liu and Wu 2018) (before propagation and user engagement patterns are fully formed).”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, background): “network where nodes and edges represent the social entities and the interactions between them, respectively. Network representations have several advantages over some existing Euclidean-based methods [23, 35] in terms of structural modeling capability for several phenomena such as echo chambers of u”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, methodology): “Since it is difﬁcult for the PPC RNN+CNN method to process the data of variational lengths, we cannot get the accurate results of PPC RNN+CNN at each deadline in this task, so it is not compared in this experiment.”
Fake news, disinformation and misinformation in social media: a review (2023, background): “Therefore, it is considered to be challenging, if not impossible, to identify useful features (Wu and Liu 2018) and consequently tell the nature of such news solely from the content.”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, background): “Third, early fake news detection aims to give early alerts of fake news during the dissemination process before it reaches a broad audience.(23) Therefore, early fake FAKENEWSNET: A DATA REPOSITORY FOR STUDYING FAKE NEWS 185”
A Survey of Fake News (2020, background): “…writing style across topics, domains, languages, and the like, as well as the evolution of deceptive writing style; and (3) verification efficiency [Liu and Wu 2018; Zhou et al. 2019a], such as by improving the efficiency of fake news detection by effectively using a small amount of information in…”