"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
William Yang Wang
- Vol. 2, pp 422-426
Reads0
Chats0
TLDR
Li et al. as discussed by the authors designed a hybrid convolutional neural network to integrate meta-data with text and showed that this hybrid approach can improve a text-only deep learning model.Abstract:
Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.read more
Citations
More filters
Book ChapterDOI
Fake News Detection Based on Subjective Opinions
TL;DR: This study focuses on the user spreading news behaviors on social media platforms and aims to detect fake news more effectively with more accurate data reliability assessment, introducing Subjective Opinions into reliability evaluation and proposed two new methods.
Posted Content
Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion
Dimitri Ognibene,Davide Taibi,Udo Kruschwitz,Rodrigo Souza Wilkens,Davinia Hernández-Leo,Emily Theophilou,Lidia Scifo,Rene Alejandro Lobo Quintero,Francesco Lomonaco,Sabrina C. Eimler,Heinz Ulrich Hoppe,Nils Malzahn +11 more
TL;DR: In this article, the authors propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with social media (SM).
Journal ArticleDOI
Misreporting and Fake News Detection Techniques on the Social Media Platform
TL;DR: This review paper surveys several distinct deep learning techniques and provides a comprehensive review of automatic fake news detection classification tasks and the datasets and models used, demonstrating the performance evaluation on different approaches.
Proceedings Article
Suspicious News Detection Using Micro Blog Text
Tsubasa Tagami,Hiroki Ouchi,Hiroki Asano,Kazuaki Hanawa,Kaori Uchiyama,Kaito Suzuki,Kentaro Inui,Atsushi Komiya,Atsuo Fujimura,Hitofumi Yanai,Ryo Yamashita,Akinori Machino +11 more
TL;DR: In this paper, the authors presented a new task, suspicious news detection using micro blog text, which aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles.
Posted Content
Explainable Rumor Detection using Inter and Intra-feature Attention Networks.
TL;DR: This paper designs a modular explainable architecture that uses both latent and handcrafted features and can be expanded to as many new classes of features as desired and performs significantly better in terms of F-score and accuracy while also being interpretable.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Journal Article
Natural Language Processing (Almost) from Scratch
TL;DR: A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.
Proceedings Article
Character-level convolutional networks for text classification
TL;DR: In this paper, the use of character-level convolutional networks (ConvNets) for text classification has been explored and compared with traditional models such as bag of words, n-grams and their TFIDF variants.