Improving Rumor Detection Performance by Using Bias Attributes
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A5C7PYXB4" target="_blank" >RIV/00216208:11320/26:5C7PYXB4 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TCSS.2025.3550170" target="_blank" >http://dx.doi.org/10.1109/TCSS.2025.3550170</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCSS.2025.3550170" target="_blank" >10.1109/TCSS.2025.3550170</a>
Alternative languages
Result language
angličtina
Original language name
Improving Rumor Detection Performance by Using Bias Attributes
Original language description
With the rapid advancement of social media, the barriers to sharing information have significantly decreased, leading to the rampant spread of rumors across various platforms. Current natural language processing (NLP) techniques use models trained on datasets to detect rumors. However, these datasets often contain inherent biases, which, as has been demonstrated in other NLP tasks, can negatively impact the accuracy of the results. This study confirms the presence of significant bias in rumor detection datasets. Given the intertwined and complex nature of bias and rumors, both of which play a crucial role in assessing the trustworthiness of online content, the article argues that improving detection models should not only focus on performance but also prioritize detecting biased rumors. Addressing this dual challenge is essential to prevent rumor creators from exploiting biases to enhance the spread of false information. To tackle this issue at the data level, this study proposes integrating bias attributes into the training datasets, thereby improving the ability of models to identify biased rumors. Through experiments conducted across multiple rumor detection models, the approach has been shown to enhance the detection of biased rumors in both Chinese and English datasets without compromising overall detection accuracy. This method might be helpful in reducing the spread of biased rumors online, contributing to a healthier information ecosystem. © 2014 IEEE.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Computational Social Systems
ISSN
2329-924X
e-ISSN
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Volume of the periodical
2025
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
1-16
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105001234098