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Looking for COVID-19 Misinformation in Multilingual Social Media Texts

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New Trends in Database and Information Systems (ADBIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1450))

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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

  1. 1.

    https://github.com/google-research/bert/blob/master/multilingual.md.

  2. 2.

    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.

  3. 3.

    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].

  4. 4.

    It is 13 because the first element is the input embeddings, the rest is the outputs of each of BERT’s 12 layers.

  5. 5.

    That is 219,648 unique values to represent our one sentence!.

  6. 6.

    https://huggingface.co/.

  7. 7.

    https://www.poynter.org/covid-19-poynter-resources/.

  8. 8.

    https://pypi.org/project/beautifulsoup4/.

  9. 9.

    https://chequeado.com/latamcoronavirus/.

  10. 10.

    https://github.com/firojalam/COVID-19-tweets-for-check-worthiness.

  11. 11.

    NLTK https://www.nltk.org/ is a Python library for natural language processing.

  12. 12.

    https://docs.cltk.org/en/latest/index.html.

  13. 13.

    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.

  14. 14.

    Python module is available at http://www.tweepy.org.

  15. 15.

    Pretrained model available at https://huggingface.co/models.

  16. 16.

    ThaiBERT is available at https://github.com/ThAIKeras/bert.

  17. 17.

    Please refer https://cloud.google.com/translate/docs.

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Correspondence to Genoveva Vargas-Solar .

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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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  • DOI: https://doi.org/10.1007/978-3-030-85082-1_7

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  • Online ISBN: 978-3-030-85082-1

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