Finding Fake News Websites in the Wild

• URL: http://arxiv.org/abs/2407.07159v2
• Date: Mon, 15 Jul 2024 11:18:44 GMT
• Title: Finding Fake News Websites in the Wild
• Authors: Leandro Araujo, Joao M. M. Couto, Luiz Felipe Nery, Isadora C. Rodrigues, Jussara M. Almeida, Julio C. S. Reis, Fabricio Benevenuto,
• Abstract summary: We propose a novel methodology for identifying websites responsible for creating and disseminating misinformation content. We validate our approach on Twitter by examining various execution modes and contexts.
• Score: 0.0860395700487494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The battle against the spread of misinformation on the Internet is a daunting task faced by modern society. Fake news content is primarily distributed through digital platforms, with websites dedicated to producing and disseminating such content playing a pivotal role in this complex ecosystem. Therefore, these websites are of great interest to misinformation researchers. However, obtaining a comprehensive list of websites labeled as producers and/or spreaders of misinformation can be challenging, particularly in developing countries. In this study, we propose a novel methodology for identifying websites responsible for creating and disseminating misinformation content, which are closely linked to users who share confirmed instances of fake news on social media. We validate our approach on Twitter by examining various execution modes and contexts. Our findings demonstrate the effectiveness of the proposed methodology in identifying misinformation websites, which can aid in gaining a better understanding of this phenomenon and enabling competent entities to tackle the problem in various areas of society.

Related papers

• News and Misinformation Consumption in Europe: A Longitudinal Cross-Country Perspective [49.1574468325115]
  This study investigated information consumption in four European countries. It analyzed three years of Twitter activity from news outlet accounts in France, Germany, Italy, and the UK. Results indicate that reliable sources dominate the information landscape, although unreliable content is still present across all countries.
  arXiv  Detail & Related papers  (2023-11-09T16:22:10Z)
• 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)
• Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection [7.732570307576947]
  The Internet and social media have altered how individuals access news in the age of instantaneous information distribution. Fake news is rapidly spreading on digital platforms, which has a negative impact on the media ecosystem. Natural Language Processing has emerged as a potent weapon in the growing war against disinformation.
  arXiv  Detail & Related papers  (2023-08-30T21:25:31Z)
• 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)
• A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [3.42658286826597]
  Fake news detection is one of the most analyzed and prominent areas of research. Facebook, Reddit, WhatsApp, YouTube, and other social applications are gradually gaining attention in this emerging field. This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text.
  arXiv  Detail & Related papers  (2021-11-23T16:02:28Z)
• A Study of Fake News Reading and Annotating in Social Media Context [1.0499611180329804]
  We present an eye-tracking study, in which we let 44 lay participants to casually read through a social media feed containing posts with news articles, some of which were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We also describe a follow-up qualitative study with a similar scenario but this time with 7 expert fake news annotators.
  arXiv  Detail & Related papers  (2021-09-26T08:11:17Z)
• SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread? [5.64512235559998]
  Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. Unlike traditional media channels, social networks facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviors, attitudes and beliefs of the public.
  arXiv  Detail & Related papers  (2021-05-22T09:26:13Z)
• Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media [1.7616042687330642]
  We propose a model for detecting fake news using stance as one of the features along with the content of the article. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.
  arXiv  Detail & Related papers  (2021-05-21T10:46:43Z)
• 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)
• 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)
• Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements [55.33496599723126]
  disinformation including fake news has become a global phenomenon due to its explosive growth. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation.
  arXiv  Detail & Related papers  (2020-01-02T21:01:02Z)

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.