How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution

• URL: http://arxiv.org/abs/2103.05944v1
• Date: Wed, 10 Mar 2021 09:01:34 GMT
• Title: How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution
• Authors: Mingfei Guo, Xiuying Chen, Juntao Li, Dongyan Zhao, Rui Yan
• Abstract summary: We present the Fake News Evolution dataset: a new dataset tracking the fake news evolution process. Our dataset is composed of 950 paired data, each of which consists of articles representing the truth, the fake news, and the evolved fake news. We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.
• Score: 55.27685924751459
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks. Online news is modified constantly during its propagation, e.g., malicious users distort the original truth and make up fake news. However, the continuous evolution process would generate unprecedented fake news and cheat the original model. We present the Fake News Evolution (FNE) dataset: a new dataset tracking the fake news evolution process. Our dataset is composed of 950 paired data, each of which consists of articles representing the three significant phases of the evolution process, which are the truth, the fake news, and the evolved fake news. We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.

Related papers

• From a Tiny Slip to a Giant Leap: An LLM-Based Simulation for Fake News Evolution [35.82418316346851]
  We propose a Fake News evolUtion Simulation framEwork based on large language models (LLMs) We define four types of agents commonly observed in daily interactions: spreaders, who propagate information; commentators, who provide opinions and interpretations; verifiers, who check the accuracy of information and bystanders, who passively observe without engaging. Given the lack of prior work in this area, we developed a FUSE-EVAL evaluation framework to measure the deviation from true news during the fake news evolution process.
  arXiv  Detail & Related papers  (2024-10-24T18:17:16Z)
• 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)
• Fake News Detection and Behavioral Analysis: Case of COVID-19 [0.22940141855172028]
  "Infodemic" due to spread of fake news regarding the pandemic has been a global issue. Readers could mistake fake news for real news, and consequently have less access to authentic information. It is challenging to accurately identify fake news data in social media posts.
  arXiv  Detail & Related papers  (2023-05-25T13:42:08Z)
• Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks [49.29141811578359]
  We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
  arXiv  Detail & Related papers  (2022-12-24T00:19:32Z)
• FALSE: Fake News Automatic and Lightweight Solution [0.20999222360659603]
  In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data.
  arXiv  Detail & Related papers  (2022-08-16T11:53:30Z)
• 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)
• 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)
• 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)
• The Rise and Fall of Fake News sites: A Traffic Analysis [62.51737815926007]
  We investigate the online presence of fake news websites and characterize their behavior in comparison to real news websites. Based on our findings, we build a content-agnostic ML for automatic detection of fake news websites.
  arXiv  Detail & Related papers  (2021-03-16T18:10:22Z)
• Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News [9.68145635795782]
  We propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets.
  arXiv  Detail & Related papers  (2020-10-07T04:55:34Z)

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.