Detection of fake news campaigns using graph convolutional networks
The detection of organised disinformation campaigns that spread fake news, by first camouflaging them as real ones is crucial in the battle against misinformation and disinformation in social media. This article presents a method for classifying the diffusion graphs of news formed in social media, b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000477 |
| _version_ | 1828096714429431808 |
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| author | Dimitrios Michail Nikos Kanakaris Iraklis Varlamis |
| author_facet | Dimitrios Michail Nikos Kanakaris Iraklis Varlamis |
| author_sort | Dimitrios Michail |
| collection | DOAJ |
| description | The detection of organised disinformation campaigns that spread fake news, by first camouflaging them as real ones is crucial in the battle against misinformation and disinformation in social media. This article presents a method for classifying the diffusion graphs of news formed in social media, by taking into account the profiles of the users that participate in the graph, the profiles of their social relations and the way the news spread, ignoring the actual text content of the news or the messages that spread it. This increases the robustness of the method and widens its applicability in different contexts. The results of this study show that the proposed method outperforms methods that rely on textual information only and provide a model that can be employed for detecting similar disinformation campaigns on different context in the same social medium. |
| first_indexed | 2024-04-11T07:34:44Z |
| format | Article |
| id | doaj.art-f6dd9cfe02d849d5a6758bdabf445733 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-11T07:34:44Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-f6dd9cfe02d849d5a6758bdabf4457332022-12-22T04:36:47ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-11-0122100104Detection of fake news campaigns using graph convolutional networksDimitrios Michail0Nikos Kanakaris1Iraklis Varlamis2Harokopio University of Athens, Department of Informatics and Telematics, GreeceCorresponding author.; University of Patras, IMIS Lab, Department of Mechanical Engineering and Aeronautics, GreeceHarokopio University of Athens, Department of Informatics and Telematics, GreeceThe detection of organised disinformation campaigns that spread fake news, by first camouflaging them as real ones is crucial in the battle against misinformation and disinformation in social media. This article presents a method for classifying the diffusion graphs of news formed in social media, by taking into account the profiles of the users that participate in the graph, the profiles of their social relations and the way the news spread, ignoring the actual text content of the news or the messages that spread it. This increases the robustness of the method and widens its applicability in different contexts. The results of this study show that the proposed method outperforms methods that rely on textual information only and provide a model that can be employed for detecting similar disinformation campaigns on different context in the same social medium.http://www.sciencedirect.com/science/article/pii/S2667096822000477Fake newsAstroturfingGraph convolutional networksDisinformationGraph attention networks |
| spellingShingle | Dimitrios Michail Nikos Kanakaris Iraklis Varlamis Detection of fake news campaigns using graph convolutional networks International Journal of Information Management Data Insights Fake news Astroturfing Graph convolutional networks Disinformation Graph attention networks |
| title | Detection of fake news campaigns using graph convolutional networks |
| title_full | Detection of fake news campaigns using graph convolutional networks |
| title_fullStr | Detection of fake news campaigns using graph convolutional networks |
| title_full_unstemmed | Detection of fake news campaigns using graph convolutional networks |
| title_short | Detection of fake news campaigns using graph convolutional networks |
| title_sort | detection of fake news campaigns using graph convolutional networks |
| topic | Fake news Astroturfing Graph convolutional networks Disinformation Graph attention networks |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000477 |
| work_keys_str_mv | AT dimitriosmichail detectionoffakenewscampaignsusinggraphconvolutionalnetworks AT nikoskanakaris detectionoffakenewscampaignsusinggraphconvolutionalnetworks AT iraklisvarlamis detectionoffakenewscampaignsusinggraphconvolutionalnetworks |