COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News

• URL: http://arxiv.org/abs/2310.13890v1
• Date: Sat, 21 Oct 2023 02:11:39 GMT
• Title: COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News
• Authors: Dylan Warman and Muhammad Ashad Kabir
• Abstract summary: This paper establishes BERT as the superior model for fake news detection. We have implemented a browser extension, enhanced with explainability features, enabling real-time identification of fake news. Our experiments affirm BERT's exceptional accuracy in detecting COVID-19-related fake news.
• Score: 1.257018053967058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fake news has emerged as a critical global issue, magnified by the COVID-19 pandemic, underscoring the need for effective preventive tools. Leveraging machine learning, including deep learning techniques, offers promise in combatting fake news. This paper goes beyond by establishing BERT as the superior model for fake news detection and demonstrates its utility as a tool to empower the general populace. We have implemented a browser extension, enhanced with explainability features, enabling real-time identification of fake news and delivering easily interpretable explanations. To achieve this, we have employed two publicly available datasets and created seven distinct data configurations to evaluate three prominent machine learning architectures. Our comprehensive experiments affirm BERT's exceptional accuracy in detecting COVID-19-related fake news. Furthermore, we have integrated an explainability component into the BERT model and deployed it as a service through Amazon's cloud API hosting (AWS). We have developed a browser extension that interfaces with the API, allowing users to select and transmit data from web pages, receiving an intelligible classification in return. This paper presents a practical end-to-end solution, highlighting the feasibility of constructing a holistic system for fake news detection, which can significantly benefit society.

Related papers

• FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection [54.37159298632628]
  FineFake is a multi-domain knowledge-enhanced benchmark for fake news detection. FineFake encompasses 16,909 data samples spanning six semantic topics and eight platforms. The entire FineFake project is publicly accessible as an open-source repository.
  arXiv  Detail & Related papers  (2024-03-30T14:39:09Z)
• 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)
• 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)
• Development of Fake News Model using Machine Learning through Natural Language Processing [0.7120858995754653]
  We use machine learning algorithms and for identification of fake news. Simple classification is not completely correct in fake news detection. With the integration of machine learning and text-based processing, we can detect fake news.
  arXiv  Detail & Related papers  (2022-01-19T09:26:15Z)
• Automated Evidence Collection for Fake News Detection [11.324403127916877]
  We propose a novel approach that improves over the current automatic fake news detection approaches. Our approach extracts supporting evidence from the web articles and then selects appropriate text to be treated as evidence sets. Our experiments, using both machine learning and deep learning-based methods, help perform an extensive evaluation of our approach.
  arXiv  Detail & Related papers  (2021-12-13T09:38:41Z)
• FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference [61.068947982746224]
  FacTeR-Check enables retrieving fact-checked information, unchecked claims verification and tracking dangerous information over social media. The architecture is validated using a new dataset called NLI19-SP that is publicly released with COVID-19 related hoaxes and tweets from Spanish social media. Our results show state-of-the-art performance on the individual benchmarks, as well as producing useful analysis of the evolution over time of 61 different hoaxes.
  arXiv  Detail & Related papers  (2021-10-27T15:44:54Z)
• Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection [18.847254074201953]
  We propose a novel fake news detection framework, namely Adversarial Active Learning-based Heterogeneous Graph Neural Network (AA-HGNN) AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data. Experiments with two real-world fake news datasets show that our model can outperform text-based models and other graph-based models.
  arXiv  Detail & Related papers  (2021-01-27T05:05:25Z)
• Linked Credibility Reviews for Explainable Misinformation Detection [1.713291434132985]
  We propose an architecture based on a core concept of Credibility Reviews (CRs) that can be used to build networks of distributed bots that collaborate for misinformation detection. CRs serve as building blocks to compose graphs of (i) web content, (ii) existing credibility signals --fact-checked claims and reputation reviews of websites--, and (iii) automatically computed reviews. We implement this architecture on top of lightweight extensions to.org and services providing generic NLP tasks for semantic similarity and stance detection.
  arXiv  Detail & Related papers  (2020-08-28T16:55:43Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)
• Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
  This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
  arXiv  Detail & Related papers  (2020-07-24T05:42:29Z)

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.