Semantic graph based topic modelling framework for multilingual fake news detection
Fake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing studies on detecting fake news are written in English, restricting its application outside the Engli...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2023-01-01
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| Series: | AI Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666651023000062 |
| _version_ | 1797378628966678528 |
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| author | Rami Mohawesh Xiao Liu Hilya Mudrika Arini Yutao Wu Hui Yin |
| author_facet | Rami Mohawesh Xiao Liu Hilya Mudrika Arini Yutao Wu Hui Yin |
| author_sort | Rami Mohawesh |
| collection | DOAJ |
| description | Fake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing studies on detecting fake news are written in English, restricting its application outside the English-speaking population. The lack of annotated corpora and technologies makes it difficult to identify false news in the scenario of low-resource languages, despite the growth in multilingual web content. Moreover, existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge up these challenges and deal with the multilingual fake news detection challenge, we develop a new semantic graph attention-based representation learning framework to extract structural and semantic representations of texts. Our experiments on TALLIP fake news datasets showed that the classification performance had been significantly enhanced, ranging from 1% to 7% in terms of accuracy metric, and our proposed framework outperformed the state-of-the-art techniques for the multilingual fake news detection task. |
| first_indexed | 2024-03-08T20:10:31Z |
| format | Article |
| id | doaj.art-c48c947ec377476c97d82da3228ab827 |
| institution | Directory Open Access Journal |
| issn | 2666-6510 |
| language | English |
| last_indexed | 2024-03-08T20:10:31Z |
| publishDate | 2023-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | AI Open |
| spelling | doaj.art-c48c947ec377476c97d82da3228ab8272023-12-23T05:22:52ZengKeAi Communications Co. Ltd.AI Open2666-65102023-01-0143341Semantic graph based topic modelling framework for multilingual fake news detectionRami Mohawesh0Xiao Liu1Hilya Mudrika Arini2Yutao Wu3Hui Yin4School of Information Technology, Deakin University, Geelong, Australia; Cybersecurity Department, College of Engineering, Al Ain University, Al Ain, Abu Dhabi, United Arab Emirates; Corresponding author. School of Information Technology, Deakin University, Geelong, Australia.School of Information Technology, Deakin University, Geelong, AustraliaUniversitas Gadjah Mada, IndonesiaSchool of Information Technology, Deakin University, Geelong, AustraliaSchool of Information Technology, Deakin University, Geelong, AustraliaFake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing studies on detecting fake news are written in English, restricting its application outside the English-speaking population. The lack of annotated corpora and technologies makes it difficult to identify false news in the scenario of low-resource languages, despite the growth in multilingual web content. Moreover, existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge up these challenges and deal with the multilingual fake news detection challenge, we develop a new semantic graph attention-based representation learning framework to extract structural and semantic representations of texts. Our experiments on TALLIP fake news datasets showed that the classification performance had been significantly enhanced, ranging from 1% to 7% in terms of accuracy metric, and our proposed framework outperformed the state-of-the-art techniques for the multilingual fake news detection task.http://www.sciencedirect.com/science/article/pii/S2666651023000062Fake news detectionCross-language studyGraph attention network |
| spellingShingle | Rami Mohawesh Xiao Liu Hilya Mudrika Arini Yutao Wu Hui Yin Semantic graph based topic modelling framework for multilingual fake news detection AI Open Fake news detection Cross-language study Graph attention network |
| title | Semantic graph based topic modelling framework for multilingual fake news detection |
| title_full | Semantic graph based topic modelling framework for multilingual fake news detection |
| title_fullStr | Semantic graph based topic modelling framework for multilingual fake news detection |
| title_full_unstemmed | Semantic graph based topic modelling framework for multilingual fake news detection |
| title_short | Semantic graph based topic modelling framework for multilingual fake news detection |
| title_sort | semantic graph based topic modelling framework for multilingual fake news detection |
| topic | Fake news detection Cross-language study Graph attention network |
| url | http://www.sciencedirect.com/science/article/pii/S2666651023000062 |
| work_keys_str_mv | AT ramimohawesh semanticgraphbasedtopicmodellingframeworkformultilingualfakenewsdetection AT xiaoliu semanticgraphbasedtopicmodellingframeworkformultilingualfakenewsdetection AT hilyamudrikaarini semanticgraphbasedtopicmodellingframeworkformultilingualfakenewsdetection AT yutaowu semanticgraphbasedtopicmodellingframeworkformultilingualfakenewsdetection AT huiyin semanticgraphbasedtopicmodellingframeworkformultilingualfakenewsdetection |