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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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A Twitter Political Corpus of the 2019 10N Spanish Election
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Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
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Multimodal Fake News Detection with Textual, Visual and Semantic Information
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Abstract:
[EN] Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news. ; Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31) ; Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3 ; S ; 30 ; 38 ; Boididou, C., et al.: Verifying multimedia use at MediaEval 2015. In: MediaEval 2015 Workshop, pp. 235–237 (2015) ; Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: WWW 2011, pp. 675–684 (2011) ; Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR 2017, pp. 1251–1258 (2017) ; Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: ICWSM 2017 (2017) ; Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248–255 (2009) ; Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020) ; Giachanou, A., Gonzalo, J., Mele, I., Crestani, F.: Sentiment propagation for predicting reputation polarity. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 226–238. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_18 ; Giachanou, A., Ríssola, E.A., Ghanem, B., Crestani, F., Rosso, P.: The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: Métais, E., Meziane, F., Horacek, H., Cimiano, P. (eds.) NLDB 2020. LNCS, vol. 12089, pp. 181–192. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51310-8_17 ; Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: SIGIR 2019, pp. 877–880 (2019) ; He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770–778 (2016) ; Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C 41(6), 765–781 (2011) ; Khattar, D., Goud, J.S., Gupta, M., Varma, V.: MVAE: multimodal variational autoencoder for fake news detection. In: WWW 2019, pp. 2915–2921 (2019) ; Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002) ; Popat, K., Mukherjee, S., Yates, A., Weikum, G.: DeClarE: debunking fake news and false claims using evidence-aware deep learning. In: EMNLP 2018, pp. 22–32 (2018) ; Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: EMNLP 2017, pp. 2931–2937 (2017) ; Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: MIPR 2018, pp. 430–435 (2018) ; Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv:1809.01286 (2018) ; Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014) ; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR 2016, pp. 2818–2826 (2016) ; Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010) ; Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) ; Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: KDD 2018, pp. 849–857 (2018) ; Zhao, Z., et al.: An image-text consistency driven multimodal sentiment analysis approach for social media. Inf. Process. Manag. 56(6), 102097 (2019) ; Zlatkova, D., Nakov, P., Koychev, I.: Fact-checking meets fauxtography: verifying claims about images. In: EMNLP-IJCNLP 2019, pp. 2099–2108 (2019)
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Keyword:
Image-text similarity; LENGUAJES Y SISTEMAS INFORMATICOS; Multimodal fake news detection; Textual features; Visual features
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URL: https://doi.org/10.1007/978-3-030-58323-1_3
http://hdl.handle.net/10251/178911
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| 25 |
An Emotional Analysis of False Information in Social Media and News Articles
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| 26 |
Irony Detection in Twitter with Imbalanced Class Distributions
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| 27 |
#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
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Fine-Grained Analysis of Language Varieties and Demographics
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Multilingual Stance Detection in Social Media Political Debates
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| 31 |
Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase
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Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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