Fact checking: detection of check worthy statements through support vector machine and feed forward neural network
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Author (Corporation)
Publication date
2021
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
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Editor (Corporation)
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Parent work
Advances in information and communication. Proceedings of the 2021 Future of Information and Communication Conference (FICC)
Special issue
DOI of the original publication
Link
Series
Advances in Intelligent Systems and Computing
Series number
1364
Volume
2
Issue / Number
Pages / Duration
520-535
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
Cham
Edition
Version
Programming language
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Practice partner / Client
Abstract
Detection of check-worthy statements is a subtask in the fact-checking process, automation of which would decrease the time and burden required to fact-check a statement. This paper proposes an approach focused on the classification of statements into check-worthy and not check-worthy. For the current paper, a dataset is constructed by consulting different fact-checking organizations. It contains debates and speeches in the domain of politics. Thus, even the ability of check worthy approach is evaluated on this domain. It starts with extracting sentence-level and context features from the sentences, and classifying them based on these features. The feature set and context were chosen after several experiments, based on how well they differentiate check-worthy statements. The findings indicated that the context in the approach gives considerable contribution in the classification, while also using more general features to capture information from the sentences. The results were analyzed by examining all features used, assessing their contribution in classification, and how well the approach performs in speeches and debates separately to detect the check worthy statements to reduce the time and burden of fact checking process.
Keywords
Subject (DDC)
Event
Future of Information and Communications Conference (FICC) 2021
Exhibition start date
Exhibition end date
Conference start date
29.04.2021
Conference end date
30.04.2021
Date of the last check
ISBN
978-3-030-73102-1
978-3-030-73103-8
978-3-030-73103-8
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Ahmed, S., Balla, K., Hinkelmann, K., & Corradini, F. (2021). Fact checking: detection of check worthy statements through support vector machine and feed forward neural network. In K. Arai (Ed.), Advances in information and communication. Proceedings of the 2021 Future of Information and Communication Conference (FICC) (Vol. 2, pp. 520–535). Springer. https://doi.org/10.1007/978-3-030-73103-8_37