Rumor detection using dual embeddings and text-based graph convolutional network
Abstract Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-024-00193-6 |
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| _version_ | 1849221068110168064 |
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| author | Barsha Pattanaik Sourav Mandal Rudra M. Tripathy Arif Ahmed Sekh |
| author_facet | Barsha Pattanaik Sourav Mandal Rudra M. Tripathy Arif Ahmed Sekh |
| author_sort | Barsha Pattanaik |
| collection | DOAJ |
| description | Abstract Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively. |
| format | Article |
| id | doaj-art-492535560a3e4baf930d83f906f7cefc |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-492535560a3e4baf930d83f906f7cefc2024-11-24T12:35:33ZengSpringerDiscover Artificial Intelligence2731-08092024-11-014111510.1007/s44163-024-00193-6Rumor detection using dual embeddings and text-based graph convolutional networkBarsha Pattanaik0Sourav Mandal1Rudra M. Tripathy2Arif Ahmed Sekh3School of Computer Science and Engineering, XIM UniversitySchool of Computer Science and Engineering, XIM UniversitySchool of Computer Science and Engineering, XIM UniversityDepartment of Computer Science, UiT The Arctic UniversityAbstract Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively.https://doi.org/10.1007/s44163-024-00193-6Rumor detectionRumor classificationDual word embeddingGraph convolution network (GCN)TextGCN |
| spellingShingle | Barsha Pattanaik Sourav Mandal Rudra M. Tripathy Arif Ahmed Sekh Rumor detection using dual embeddings and text-based graph convolutional network Discover Artificial Intelligence Rumor detection Rumor classification Dual word embedding Graph convolution network (GCN) TextGCN |
| title | Rumor detection using dual embeddings and text-based graph convolutional network |
| title_full | Rumor detection using dual embeddings and text-based graph convolutional network |
| title_fullStr | Rumor detection using dual embeddings and text-based graph convolutional network |
| title_full_unstemmed | Rumor detection using dual embeddings and text-based graph convolutional network |
| title_short | Rumor detection using dual embeddings and text-based graph convolutional network |
| title_sort | rumor detection using dual embeddings and text based graph convolutional network |
| topic | Rumor detection Rumor classification Dual word embedding Graph convolution network (GCN) TextGCN |
| url | https://doi.org/10.1007/s44163-024-00193-6 |
| work_keys_str_mv | AT barshapattanaik rumordetectionusingdualembeddingsandtextbasedgraphconvolutionalnetwork AT souravmandal rumordetectionusingdualembeddingsandtextbasedgraphconvolutionalnetwork AT rudramtripathy rumordetectionusingdualembeddingsandtextbasedgraphconvolutionalnetwork AT arifahmedsekh rumordetectionusingdualembeddingsandtextbasedgraphconvolutionalnetwork |