Early Detection of Fake News with Multi-source Weak Social Supervision

Shu, Kai; Zheng, Guoan; Li, Yichuan; Mukherjee, Subhabrata; Awadallah, Ahmed Hassan; Ruston, Scott W.; Liu, Huan

doi:10.1007/978-3-030-67664-3_39

Cited by 19 publications

(6 citation statements)

References 22 publications

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“…Moreover, several researchers (Shu et al 2020d;Pathak and Srihari 2019;Przybyla 2020) believe that fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news. Consequently, training machine learning models on such sophisticated content requires large-scale annotated fake news data that are difficult to obtain (Shu et al 2020d).…”

Section: Datasets Issuementioning

confidence: 99%

Fake news, disinformation and misinformation in social media: a review

Aïmeur

Amri

Brassard

2023

Soc. Netw. Anal. Min.

176

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Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

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Section: Datasets Issuementioning

confidence: 99%

Fake news, disinformation and misinformation in social media: a review

Aïmeur

Amri

Brassard

2023

Soc. Netw. Anal. Min.

176

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“…Our MCFEND dataset gathers news from 14 fact-checking agencies, categorized into three distinct groups. As previously noted, fake news originating from diverse sources differs in both content and social context [1,19,21,31,37]. To quantify these differences between the three groups, we conducted analyses on these two aspects, which are considered significant clues for fake news detection [9,21].…”

Section: Comparison Of the Three Groupsmentioning

confidence: 99%

“…[3,9,13,14,20]. News, in particular, fake news, from different sources is characterized by diverse dimensions, such as news content, topics, publishing methods, and the utilization of sophisticated linguistic styles intended to mimic real news [1,19,21,31,37]. For example, Fig.…”

Section: Introductionmentioning

confidence: 99%

MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection

Li,

He,

Bai

et al. 2024

Proceedings of the ACM Web Conference 2024

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The prevalence of fake news across various online sources has had a significant influence on the public. Existing Chinese fake news detection datasets are limited to news sourced solely from Weibo. However, fake news originating from multiple sources exhibits diversity in various aspects, including its content and social context. Methods trained on purely one single news source can hardly be applicable to real-world scenarios. Our pilot experiment demonstrates that the F1 score of the state-of-the-art method that learns from a large Chinese fake news detection dataset, Weibo-21, drops significantly from 0.943 to 0.470 when the test data is changed to multi-source news data, failing to identify more than one-third of the multi-source fake news. To address this limitation, we constructed the first multi-source benchmark dataset for Chinese fake news detection, termed MCFEND, which is composed of news we collected from diverse sources such as social platforms, messaging apps, and traditional online news outlets. Notably, such news has been fact-checked by 14 authoritative fact-checking agencies worldwide. In addition, various existing Chinese fake news detection methods are thoroughly evaluated on our proposed dataset in cross-source, multi-source, and unseen source ways. MCFEND, as a benchmark dataset, aims to advance Chinese fake news detection approaches in real-world scenarios.Haorui He and Dacheng Wen are also with

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“…Content-based methods focus on analyzing the content of news articles. Using natural language text [4], [5], [6], [7] recognizes linguistic patterns, and assesses the consistency of information, or using image-text multimodality [8], [9] allows the broader perspective by considering the interplay between textual and visual components in evaluating the credibility of news content. Propagation-based methods identify the patterns of spreading information across networks and consider the dissemination pathways on social networks and online communities.…”

Section: Introductionmentioning

confidence: 99%

Topological and Sequential Neural Network Model for Detecting Fake News

Jung,

Kim,

Cho

2023

IEEE Access

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Fake news can be easily propagated through social media and cause negative societal effects. Since fake news is disinformation with malicious intent, manual fact-checking requires great effort. In order to cope with these challenges, many automatic fake news detection models have been introduced. Recent studies have shown that social network information along with news content can be used effectively for detecting fake news. In this paper, we propose a Topological and Sequential Neural Network model (TSNN) for detecting fake news by capturing the diffusion patterns between source news and users in social networks. We employ the supernode approach instead of simple graph pooling methods to extract representative features in graph topological structure. To better learn the representations in the supernode, we design two-staged graph neural networks reflecting the heterogeneity between news and Twitter users. Our model additionally captures sequential information on news diffusion path by using a transformer. We evaluate our model with two fake news benchmark datasets annotated by fact-checking websites: PolitiFact and GossipCop. TSNN achieves 92.15% accuracy and 92.11% F1-score on PolitiFact, and 97.91% accuracy and 97.88% F1-score on GossipCop. These results demonstrate that our model significantly outperforms other baselines, establishing it as a state-of-the-art solution for fake news detection. To verify the effectiveness following model configuration, we perform ablation studies to demonstrate how each component among our two-stage graph neural networks, and sequential information modules contribute to the performance improvements.

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Early Detection of Fake News with Multi-source Weak Social Supervision

Cited by 19 publications

References 22 publications

Fake news, disinformation and misinformation in social media: a review

Fake news, disinformation and misinformation in social media: a review

MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection

Topological and Sequential Neural Network Model for Detecting Fake News

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