Journal ArticleDOI
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media
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TLDR
A fake news data repository FakeNewsNet is presented, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information, and is discussed for potential applications on fake news study on social media.Abstract:
Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of fake news, that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received a lot of attention. Despite several existing computational solutions on the detection of fake news, the lack of comprehensive and community-driven fake news data sets has become one of major roadblocks. Not only existing data sets are scarce, they do not contain a myriad of features often required in the study such as news content, social context, and spatiotemporal information. Therefore, in this article, to facilitate fake news-related research, we present a fake news data repository FakeNewsNet, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information. We present a comprehensive description of the FakeNewsNet, demonstrate an exploratory analysis of two data sets from different perspectives, and discuss the benefits of the FakeNewsNet for potential applications on fake news study on social media.read more
Citations
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Proceedings ArticleDOI
Multimodal Multi-image Fake News Detection
TL;DR: This paper proposes a multimodal multi-image system that combines information from different modalities in order to detect fake news posted online and significantly outperforms the BERT baseline by 4.19% and SpotFake by 5.39%.
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Fake news outbreak 2021: Can we stop the viral spread?
TL;DR: This survey paper extensively analyse a wide range of different solutions for the early detection of fake news in the existing literature and examines Machine Learning (ML) models for the identification and classification offake news, online fake news detection competitions, statistical outputs as well as the advantages and disadvantages of some of the available data sets.
Proceedings ArticleDOI
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model
TL;DR: The proposed solution relies on a unified neural network, which consists of several deep leaning modules, namely BERT, BiLSTM and Capsule, to solve the sentencelevel propaganda classification problem and takes a pre-training approach on a somewhat similar task (i.e., emotion classification) improving results against the cold-start model.
Journal ArticleDOI
nFake News Classification using transformer based enhanced LSTM and BERT
TL;DR: In this paper , the authors proposed a model for fake news classification based on news titles, following the content-based classification approach, which uses a BERT model with its outputs connected to an LSTM layer.
Proceedings ArticleDOI
Fake News Detection on Social Media: A Systematic Survey
TL;DR: A systematic survey on the process of fake news detection on social media is introduced and the types of data and the categories of features used in the detection model, as well as benchmark datasets are discussed.
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