skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Overview of Factify5WQA: Fact Verification through 5WQuestion-Answering
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news for many among the younger population. Fact verification, thus, becomes an important task and many media sites contribute to the cause. Manual fact verification is a tedious task, given the volume of fake news online. The Factify5WQA shared task aims to increase research towards automated fake news detection by providing a dataset with an aspect-based question answering based fact verification method. Each claim and its supporting document is associated with 5W questions that help compare the two information sources. The objective performance measure in the task is done by comparing answers using BLEU score to measure the accuracy of the answers, followed by an accuracy measure of the classification. The task had submissions using custom training setup and pre-trained language-models among others. The best performing team posted an accuracy of 69.56%, which is a near 35% improvement over the baseline.  more » « less
Award ID(s):
2350302
PAR ID:
10631980
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Editor(s):
AAAI
Publisher / Repository:
AAAI 2024
Date Published:
Edition / Version:
1
Subject(s) / Keyword(s):
Fact verification
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, Future Internet)
    null (Ed.)
    Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media to ensure its accuracy. In this paper, three studies were conducted at two universities with different regional demographic characteristics to gain a better understanding of respondents’ perception of online media labeling techniques. The first study deals with what fields should appear on a media label. The second study looks into what types of informative labels respondents would use. The third focuses on blocking type labels. Participants’ perceptions, preferences, and results are analyzed by their demographic characteristics. 
    more » « less
  2. ; ; ; ; (, Conference on Empirical Methods in Natural Language Processing)
    We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; (, Nature Human Behaviour)
    Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels. 
    more » « less
  4. ; ; (, International Conference on Autonomous Agents and Multiagent Systems)
    The spread of unwanted or malicious content through social me- dia has become a major challenge. Traditional examples of this include social network spam, but an important new concern is the propagation of fake news through social media. A common ap- proach for mitigating this problem is by using standard statistical classi cation to distinguish malicious (e.g., fake news) instances from benign (e.g., actual news stories). However, such an approach ignores the fact that malicious instances propagate through the network, which is consequential both in quantifying consequences (e.g., fake news di using through the network), and capturing de- tection redundancy (bad content can be detected at di erent nodes). An additional concern is evasion attacks, whereby the generators of malicious instances modify the nature of these to escape detection. We model this problem as a Stackelberg game between the defender who is choosing parameters of the detection model, and an attacker, who is choosing both the node at which to initiate malicious spread, and the nature of malicious entities. We develop a novel bi-level programming approach for this problem, as well as a novel solution approach based on implicit function gradients, and experimentally demonstrate the advantage of our approach over alternatives which ignore network structure. 
    more » « less
  5. ; ; ; (, Cognitive Research: Principles and Implications)
    Abstract The efficacy of fake news corrections in improving memory and belief accuracy may depend on how often adults see false information before it is corrected. Two experiments tested the competing predictions that repeating fake news before corrections will either impair or improve memory and belief accuracy. These experiments also examined whether fake news exposure effects would differ for younger and older adults due to age-related differences in the recollection of contextual details. Younger and older adults read real and fake news headlines that appeared once or thrice. Next, they identified fake news corrections among real news headlines. Later, recognition and cued recall tests assessed memory for real news, fake news, if corrections occurred, and beliefs in retrieved details. Repeating fake news increased detection and remembering of corrections, correct real news retrieval, and erroneous fake news retrieval. No age differences emerged for detection of corrections, but younger adults remembered corrections better than older adults. At test, correct fake news retrieval for earlier-detected corrections was associated with better real news retrieval. This benefit did not differ between age groups in recognition but was greater for younger than older adults in cued recall. When detected corrections were not remembered at test, repeated fake news increased memory errors. Overall, both age groups believed correctly retrieved real news more than erroneously retrieved fake news to a similar degree. These findings suggest that fake news repetition effects on subsequent memory accuracy depended on age differences in recollection-based retrieval of fake news and that it was corrected. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email