A Deep Learning Approach for Automatic Detection of Fake News
- URL: http://arxiv.org/abs/2005.04938v1
- Date: Mon, 11 May 2020 09:07:46 GMT
- Title: A Deep Learning Approach for Automatic Detection of Fake News
- Authors: Tanik Saikh, Arkadipta De, Asif Ekbal, Pushpak Bhattacharyya
- Abstract summary: We propose two models based on deep learning for solving fake news detection problem in online news contents of multiple domains.
We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection.
- Score: 47.00462375817434
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Fake news detection is a very prominent and essential task in the field of
journalism. This challenging problem is seen so far in the field of politics,
but it could be even more challenging when it is to be determined in the
multi-domain platform. In this paper, we propose two effective models based on
deep learning for solving fake news detection problem in online news contents
of multiple domains. We evaluate our techniques on the two recently released
datasets, namely FakeNews AMT and Celebrity for fake news detection. The
proposed systems yield encouraging performance, outperforming the current
handcrafted feature engineering based state-of-the-art system with a
significant margin of 3.08% and 9.3% by the two models, respectively. In order
to exploit the datasets, available for the related tasks, we perform
cross-domain analysis (i.e. model trained on FakeNews AMT and tested on
Celebrity and vice versa) to explore the applicability of our systems across
the domains.
Related papers
- A Regularized LSTM Method for Detecting Fake News Articles [0.0]
This paper develops an advanced machine learning solution for detecting fake news articles.
We leverage a comprehensive dataset of news articles, including 23,502 fake news articles and 21,417 accurate news articles.
Our work highlights the potential for deploying such models in real-world applications.
arXiv Detail & Related papers (2024-11-16T05:54:36Z) - How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models [95.44559524735308]
Large language or multimodal model based verification has been proposed to scale up online policing mechanisms for mitigating spread of false and harmful content.
We test the limits of improving foundation model performance without continual updating through an initial study of knowledge transfer.
Our results on two recent multi-modal fact-checking benchmarks, Mocheg and Fakeddit, indicate that knowledge transfer strategies can improve Fakeddit performance over the state-of-the-art by up to 1.7% and Mocheg performance by up to 2.9%.
arXiv Detail & Related papers (2024-06-29T08:39:07Z) - ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.
To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles.
Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
arXiv Detail & Related papers (2023-05-23T16:40:07Z) - Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak
Signals [19.22829945777267]
This work proposes an effective framework for unsupervised fake news detection, which first embeds the knowledge available in four modalities in news records.
Also, we propose a novel technique to construct news datasets minimizing the latent biases in existing news datasets.
We trained the proposed unsupervised framework using LUND-COVID to exploit the potential of large datasets.
arXiv Detail & Related papers (2023-05-18T23:49:31Z) - FNDaaS: Content-agnostic Detection of Fake News sites [3.936965297430477]
We propose FND, the first automatic, content-agnostic fake news detection method.
It considers new and unstudied features such as network and structural characteristics per news website.
It can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.
arXiv Detail & Related papers (2022-12-13T11:17:32Z) - Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
Multiverse is a new feature based on multilingual evidence that can be used for fake news detection.
The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
arXiv Detail & Related papers (2022-11-25T18:24:17Z) - Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020 [62.6928395368204]
Task was posed as a binary classification task, in which the goal is to differentiate between real and fake news.
We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing.
42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task.
arXiv Detail & Related papers (2022-07-25T03:41:32Z) - Faking Fake News for Real Fake News Detection: Propaganda-loaded
Training Data Generation [105.20743048379387]
We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda.
Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles.
Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
arXiv Detail & Related papers (2022-03-10T14:24:19Z) - DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection [0.04834203844100679]
We propose a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News.
Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph.
We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare.
arXiv Detail & Related papers (2021-07-04T07:09:59Z) - Embracing Domain Differences in Fake News: Cross-domain Fake News
Detection using Multi-modal Data [18.66426327152407]
We propose a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains.
Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets.
arXiv Detail & Related papers (2021-02-11T23:31:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.