FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections

• URL: http://arxiv.org/abs/2312.03730v2
• Date: Fri, 8 Dec 2023 19:42:35 GMT
• Title: FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
• Authors: Tahniat Khan, Mizanur Rahman, Veronica Chatrath, Oluwanifemi Bamgbose, Shaina Raza
• Abstract summary: We introduce FakeWatch ElectionShield, an innovative framework carefully designed to detect fake news. We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification. Our goal is to provide the research community with adaptable and accurate classification models in recognizing the dynamic nature of misinformation.
• Score: 5.861836496977495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of information. To address this challenge, we introduce FakeWatch ElectionShield, an innovative framework carefully designed to detect fake news. We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification, for precision and relevance. We propose a model hub of LMs for identifying fake news. Our goal is to provide the research community with adaptable and accurate classification models in recognizing the dynamic nature of misinformation. Extensive evaluation of fake news classifiers on our dataset and a benchmark dataset shows our that while state-of-the-art LMs slightly outperform the traditional ML models, classical models are still competitive with their balance of accuracy, explainability, and computational efficiency. This research sets the foundation for future studies to address misinformation related to elections.

Related papers

• ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents [70.17229548653852]
  We introduce ElectionSim, an innovative election simulation framework based on large language models. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
  arXiv  Detail & Related papers  (2024-10-28T05:25:50Z)
• Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection [47.01850264003063]
  Few-Shot Fake News Detection aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. We propose a Dual-perspective Augmented Fake News Detection model, designed to enhance Large Language Models.
  arXiv  Detail & Related papers  (2024-07-12T03:15:01Z)
• FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections [5.15641542196944]
  We introduce FakeWatch, a comprehensive framework carefully designed to detect fake news. Our framework integrates a model hub comprising of both traditional machine learning (ML) techniques, and state-of-the-art Language Models (LMs) Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda.
  arXiv  Detail & Related papers  (2024-03-14T20:39:26Z)
• 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)
• 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)
• Multimodal Emergent Fake News Detection via Meta Neural Process Networks [36.52739834391597]
  We propose an end-to-end fake news detection framework named MetaFEND. Specifically, the proposed model integrates meta-learning and neural process methods together. Extensive experiments are conducted on multimedia datasets collected from Twitter and Weibo.
  arXiv  Detail & Related papers  (2021-06-22T21:21:29Z)
• Explainable Tsetlin Machine framework for fake news detection with credibility score assessment [16.457778420360537]
  We propose a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) We use the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least $5%$ in terms of accuracy.
  arXiv  Detail & Related papers  (2021-05-19T13:18:02Z)
• 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 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)
• Weak Supervision for Fake News Detection via Reinforcement Learning [34.448503443582396]
  We propose a weakly-supervised fake news detection framework, i.e., WeFEND. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports.
  arXiv  Detail & Related papers  (2019-12-28T21:20:25Z)

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.