FNDaaS: Content-agnostic Detection of Fake News sites

• URL: http://arxiv.org/abs/2212.06492v1
• Date: Tue, 13 Dec 2022 11:17:32 GMT
• Title: FNDaaS: Content-agnostic Detection of Fake News sites
• Authors: Panagiotis Papadopoulos, Dimitris Spithouris, Evangelos P. Markatos, Nicolas Kourtellis
• Abstract summary: 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.
• Score: 3.936965297430477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic fake news detection is a challenging problem in misinformation spreading, and it has tremendous real-world political and social impacts. Past studies have proposed machine learning-based methods for detecting such fake news, focusing on different properties of the published news articles, such as linguistic characteristics of the actual content, which however have limitations due to the apparent language barriers. Departing from such efforts, we propose FNDaaS, the first automatic, content-agnostic fake news detection method, that considers new and unstudied features such as network and structural characteristics per news website. This method can be enforced as-a-Service, either at the ISP-side for easier scalability and maintenance, or user-side for better end-user privacy. We demonstrate the efficacy of our method using data crawled from existing lists of 637 fake and 1183 real news websites, and by building and testing a proof of concept system that materializes our proposal. Our analysis of data collected from these websites shows that the vast majority of fake news domains are very young and appear to have lower time periods of an IP associated with their domain than real news ones. By conducting various experiments with machine learning classifiers, we demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.

Related papers

• Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
  We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
  arXiv  Detail & Related papers  (2023-11-02T08:39:45Z)
• fakenewsbr: A Fake News Detection Platform for Brazilian Portuguese [0.6775616141339018]
  This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec. We develop a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity.
  arXiv  Detail & Related papers  (2023-09-20T04:10:03Z)
• 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)
• User Preference-aware Fake News Detection [61.86175081368782]
  Existing fake news detection algorithms focus on mining news content for deceptive signals. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
  arXiv  Detail & Related papers  (2021-04-25T21:19:24Z)
• A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles [5.979726271522835]
  We describe a novel Fake News Detection system that automatically identifies whether a news item is "real" or "fake" We have used an ensemble model consisting of pre-trained models followed by a statistical feature fusion network. Our proposed framework have also quantified reliable predictive uncertainty along with proper class output confidence level for the classification task.
  arXiv  Detail & Related papers  (2021-04-05T06:35:30Z)
• Supporting verification of news articles with automated search for semantically similar articles [0.0]
  We propose an evidence retrieval approach to handle fake news. The learning task is formulated as an unsupervised machine learning problem. We find that our approach is agnostic to concept drifts, i.e. the machine learning task is independent of the hypotheses in a text.
  arXiv  Detail & Related papers  (2021-03-29T12:56:59Z)
• SGG: Spinbot, Grammarly and GloVe based Fake News Detection [6.193231258199234]
  Online news portals inadvertently become the cause of spreading false information across the web. Such malpractices call for a robust automatic fake news detection system. We propose a robust yet simple fake news detection system, leveraging the tools for paraphrasing, grammar-checking, and word-embedding.
  arXiv  Detail & Related papers  (2020-08-16T08:06:52Z)
• Fake News Detection using Temporal Features Extracted via Point Process [2.105564340986074]
  We attempt to use temporal features generated from SNS posts by using a point process algorithm to identify fake news from real news. We propose a novel multi-modal attention-based method, which includes linguistic and user features alongside temporal features.
  arXiv  Detail & Related papers  (2020-07-28T06:34:54Z)
• 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)
• A Deep Learning Approach for Automatic Detection of Fake News [47.00462375817434]
  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.
  arXiv  Detail & Related papers  (2020-05-11T09:07:46Z)
• Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News [67.53424807783414]
  Social media has greatly enabled people to participate in online activities at an unprecedented rate. This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
  arXiv  Detail & Related papers  (2020-04-03T18:26:33Z)

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.