Journal ArticleDOI
FNED: A Deep Network for Fake News Early Detection on Social Media
Yang Liu,Yi-Fang Brook Wu +1 more
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TLDR
Zhang et al. as discussed by the authors proposed a novel deep neural network to detect fake news early using a status-sensitive crowd response feature extractor that extracts both text features and user features from combinations of users' text response and their corresponding user profiles, and a position-aware attention mechanism that highlights important user responses at specific ranking positions.Abstract:
The fast spreading of fake news stories on social media can cause inestimable social harm. Developing effective methods to detect them early is of paramount importance. A major challenge of fake news early detection is fully utilizing the limited data observed at the early stage of news propagation and then learning useful patterns from it for identifying fake news. In this article, we propose a novel deep neural network to detect fake news early. It has three novel components: (1) a status-sensitive crowd response feature extractor that extracts both text features and user features from combinations of users’ text response and their corresponding user profiles, (2) a position-aware attention mechanism that highlights important user responses at specific ranking positions, and (3) a multi-region mean-pooling mechanism to perform feature aggregation based on multiple window sizes. Experimental results on two real-world datasets demonstrate that our proposed model can detect fake news with greater than 90% accuracy within 5 minutes after it starts to spread and before it is retweeted 50 times, which is significantly faster than state-of-the-art baselines. Most importantly, our approach requires only 10% labeled fake news samples to achieve this effectiveness under PU-Learning settings.read more
Citations
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Journal ArticleDOI
An ensemble machine learning approach through effective feature extraction to classify fake news
Saqib Hakak,Mamoun Alazab,Suleman Khan,Thippa Reddy Gadekallu,Praveen Kumar Reddy Maddikunta,Wazir Zada Khan +5 more
TL;DR: This article has proposed an ensemble classification model for detection of the fake news that has achieved a better accuracy compared to the state-of-the-art.
Journal ArticleDOI
Deep learning for misinformation detection on online social networks: a survey and new perspectives
TL;DR: A state-of-the-art review of automated misinformation detection in social networks where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results.
Journal ArticleDOI
Fake news detection based on news content and social contexts: a transformer-based approach
Shaina Raza,Chen Ding +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a model based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behaviour based on past observations.
Journal ArticleDOI
Temporally evolving graph neural network for fake news detection
Chenguang Song,Kai Shu,Bin Wu +2 more
TL;DR: Wang et al. as discussed by the authors introduced a novel temporal propagation-based fake news detection framework, which could fuse structure, content semantics, and temporal information, and model temporal evolution patterns of real-world news as the graph evolving under the setting of dynamic diffusion networks.
Journal ArticleDOI
Trends in combating fake news on social media – a survey
TL;DR: Revelation in this study holds that the application of hybrid-machine learning techniques and the collective effort of humans could stand a higher chance of fighting misinformation on social media.
References
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Posted Content
Neural Machine Translation by Jointly Learning to Align and Translate
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Distributed Representations of Words and Phrases and their Compositionality
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