Towards Smart Fake News Detection Through Explainable AI

• URL: http://arxiv.org/abs/2207.11490v1
• Date: Sat, 23 Jul 2022 10:48:45 GMT
• Title: Towards Smart Fake News Detection Through Explainable AI
• Authors: Athira A B, S D Madhu Kumar, Anu Mary Chacko
• Abstract summary: People now see social media sites as their sole source of information due to their popularity. We discuss the pitfalls in the current explainable AI-based fake news detection models and present our ongoing research on multi-modal explainable fake news detection model.
• Score: 1.160208922584163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People now see social media sites as their sole source of information due to their popularity. The Majority of people get their news through social media. At the same time, fake news has grown exponentially on social media platforms in recent years. Several artificial intelligence-based solutions for detecting fake news have shown promising results. On the other hand, these detection systems lack explanation capabilities, i.e., the ability to explain why they made a prediction. This paper highlights the current state of the art in explainable fake news detection. We discuss the pitfalls in the current explainable AI-based fake news detection models and present our ongoing research on multi-modal explainable fake news detection model.

Related papers

• Blessing or curse? A survey on the Impact of Generative AI on Fake News [44.99833362998488]
  It is now possible to automate the creation of masses of high-quality individually targeted Fake News. This survey provides a comprehensive examination of the research and practical use of Generative AI for Fake News detection and creation in 2024.
  arXiv  Detail & Related papers  (2024-04-03T19:14:45Z)
• 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)
• 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)
• Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey [0.7120858995754653]
  In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue.
  arXiv  Detail & Related papers  (2022-01-20T07:43:15Z)
• Fake News Detection Tools and Methods -- A Review [0.0]
  We discuss the recent literature about different approaches to detect fake news over the Internet. We highlight the various publicly available datasets and various online tools that are available and can debunk Fake News in real-time.
  arXiv  Detail & Related papers  (2021-11-21T13:19:23Z)
• 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)
• How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution [55.27685924751459]
  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.
  arXiv  Detail & Related papers  (2021-03-10T09:01:34Z)
• 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.