Abstract
In recent years, the rise of the Internet has made it more and more convenient for people to obtain news, but it not only brings convenience to people, but also promotes the spread of rumors. Nowadays, many rumor detection methods are based only on textual content information. The traditional neural network model treats all the words and sentences of the text equally in text feature extraction, and can not pay attention to the important content in the news text. Therefore, it is impossible to capture the characteristics of the hierarchical structure of news documents. And under the trend of rich media of social media, false news gradually changes from a single text form to a multimodal form. In view of the above situation, a multimodal rumor detection method based on hierarchical attention network is proposed. For textual features, Glove pre-trained model is used, and it is able to capture the semantic features of the news document hierarchy by applying a two-level attention mechanism at the word and sentence levels respectively. For image features, we use the pre-trained VGG19 to obtain visual information, and finally combine the text and image features to comprehensively detect rumors. Experimental results of FakeNewsNet dataset from Twitter show that this method can effectively improve the performance of multimodal rumor detection.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Xu, G., Zhang, Q., Lu, Y., Zhang, Y., Wu, C. (2023). Research on Multimodal Rumor Detection Based on Hierarchical Attention Network. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1811. Springer, Singapore. https://doi.org/10.1007/978-981-99-2443-1_53
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DOI: https://doi.org/10.1007/978-981-99-2443-1_53
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