Abstract
This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., ‘false’, ‘partly false’, ‘misleading’). The CMTA pipeline was experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. We performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts.
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Notes
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Embeddings are helpful for keyword/search expansion, semantic search and information retrieval. They help accurately retrieve results matching a keyword query intent and contextual meaning, even in the absence of keyword or phrase overlap.
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This vocabulary contains whole words, subwords occurring at the front of a word or in isolation (e.g., “em” as in the word “embeddings” is assigned the same vector as the standalone sequence of characters “em” as in “go get em”), subwords not at the front of a word, which are preceded by ‘##’ to denote this case, and individual characters [18].
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It is 13 because the first element is the input embeddings, the rest is the outputs of each of BERT’s 12 layers.
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That is 219,648 unique values to represent our one sentence!.
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NLTK https://www.nltk.org/ is a Python library for natural language processing.
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English - 1,472,448, Spanish - 353,294, Indonesian - 80,764, French - 71,722, Japanese - 71,418, Thai - 36,824, Hindi - 27,320 and German - 23,316.
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Python module is available at http://www.tweepy.org.
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Pretrained model available at https://huggingface.co/models.
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ThaiBERT is available at https://github.com/ThAIKeras/bert.
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Please refer https://cloud.google.com/translate/docs.
References
Alam, F., et al.: Fighting the COVID-19 infodemic: modeling the perspective of journalists, fact-checkers, social media platforms, policy makers, and the society (2020)
Brennen, J.S., Simon, F., Howard, P.N., Nielsen, R.K.: Types, sources, and claims of COVID-19 misinformation. Reuters Institute 7 (2020)
Brindha, M.D., Jayaseelan, R., Kadeswara, S.: Social media reigned by information or misinformation about COVID-19: a phenomenological study (2020)
Chen, E., Lerman, K., Ferrara, E.: Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus twitter data set. JMIR Public Health Surveill. 6(2), e19273 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dharawat, A.R., Lourentzou, I., Morales, A., Zhai, C.: Drink bleach or do what now? covid-hera: a dataset for risk-informed health decision making in the presence of covid19 misinformation (2020)
Frenkel, S., Alba, D., Zhong, R.: Surge of virus misinformation stumps Facebook and Twitter. The New York Times (2020)
Gencoglu, O., Gruber, M.: Causal modeling of twitter activity during COVID-19. arXiv preprint arXiv:2005.07952 (2020)
Huang, B., Carley, K.M.: Disinformation and misinformation on twitter during the novel coronavirus outbreak. arXiv preprint arXiv:2006.04278 (2020)
Kouzy, R., et al.: Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on twitter. Cureus 12(3) (2020)
Pennycook, G., McPhetres, J., Zhang, Y., Lu, J.G., Rand, D.G.: Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol. Sci. 31(7), 770–780 (2020)
Poynter Institute: The international fact-checking network (2020). https://www.poynter.org/ifcn/
Saire, J.E.C., Navarro, R.C.: What is the people posting about symptoms related to coronavirus in Bogota, Colombia? arXiv preprint arXiv:2003.11159 (2020)
Sharma, K., Seo, S., Meng, C., Rambhatla, S., Liu, Y.: Covid-19 on social media: analyzing misinformation in twitter conversations. arXiv preprint arXiv:2003.12309 (2020)
Singh, L., et al.: A first look at COVID-19 information and misinformation sharing on twitter. arXiv preprint arXiv:2003.13907 (2020)
Wani, A., Joshi, I., Khandve, S., Wagh, V., Joshi, R.: Evaluating deep learning approaches for covid19 fake news detection. arXiv preprint arXiv:2101.04012 (2021)
Phatthiyaphaibun, W., Korakot Chaovavanich, C.P.A.S.L.L.P.C.: PyThaiNLP: Thai natural language processing in python (2016). https://doi.org/10.5281/zenodo.3519354
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., Tolmie, P.: Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS ONE 11(3), e0150989 (2016)
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Pranesh, R.R., Farokhnejad, M., Shekhar, A., Vargas-Solar, G. (2021). Looking for COVID-19 Misinformation in Multilingual Social Media Texts. In: Bellatreche, L., et al. New Trends in Database and Information Systems. ADBIS 2021. Communications in Computer and Information Science, vol 1450. Springer, Cham. https://doi.org/10.1007/978-3-030-85082-1_7
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