Journal ArticleDOI
Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities
TLDR
A holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm is put forward.Abstract:
Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.read more
Citations
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Journal ArticleDOI
Fake news detection: A hybrid CNN-RNN based deep learning approach
TL;DR: In this article, a hybrid deep learning model that combines convolutional and recurrent neural networks for fake news classification was proposed, achieving detection results that are significantly better than other non-hybrid baseline methods.
Journal ArticleDOI
A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
TL;DR: An effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech is presented.
Journal ArticleDOI
Linguistic feature based learning model for fake news detection and classification
Anshika Choudhary,Anuja Arora +1 more
TL;DR: A linguistic model is proposed to find out the properties of content that will generate language-driven features and combined linguistic feature-driven model is able to achieve the average accuracy of 86% for fake news detection and classification.
Journal ArticleDOI
Rumor detection based on propagation graph neural network with attention mechanism.
TL;DR: This work proposes a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter, and proposes a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagate graph.
Journal ArticleDOI
Detecting fake news with capsule neural networks
TL;DR: This paper aims to use capsule neural networks in the fake news detection task, using different embedding models for news items of different lengths and outperforming the state-of-the-art methods on ISOT and LIAR.
References
More filters
Journal ArticleDOI
Twitter mood predicts the stock market.
TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
Proceedings ArticleDOI
Hierarchical Attention Networks for Document Classification
TL;DR: Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin.
Journal ArticleDOI
The spread of true and false news online
TL;DR: A large-scale analysis of tweets reveals that false rumors spread further and faster than the truth, and false news was more novel than true news, which suggests that people were more likely to share novel information.
Proceedings ArticleDOI
Information credibility on twitter
TL;DR: There are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.
Proceedings Article
R-MAT: A Recursive Model for Graph Mining
TL;DR: A simple, parsimonious model, the “recursive matrix” (R-MAT) model, which can quickly generate realistic graphs, capturing the essence of each graph in only a few parameters is proposed.