COVID-19 and Arabic Twitter:How can Arab World Governments and Public Health Organizations Learn from Social Media?

Alsudias, Lama and Rayson, Paul (2020) COVID-19 and Arabic Twitter:How can Arab World Governments and Public Health Organizations Learn from Social Media? In: NLP COVID-19 Workshop. Association for Computational Linguistics.

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Abstract

In March 2020, the World Health Organization announced the COVID-19 outbreak as a pandemic. Most previous social media related research has been on English tweets and COVID-19. In this study, we collect approximately 1 million Arabic tweets from the Twitter streaming API related to COVID-19. Focussing on outcomes that we believe will be useful for Public Health Organizations, we analyse them in three different ways: identifying the topics discussed during the period, detecting rumours, and predicting the source of the tweets. We use the k-means algorithm for the first goal with k=5. The topics discussed can be grouped as follows: COVID-19 statistics, prayers for God, COVID-19 locations, advise and education for prevention, and advertising. We sample 2000 tweets and label them manually for false information, correct information, and unrelated. Then, we apply three different machine learning algorithms, Logistic Regression, Support Vector Classification, and Naïve Bayes with two sets of features, word frequency approach and word embeddings. We find that Machine Learning classifiers are able to correctly identify the rumour related tweets with 84% accuracy. We also try to predict the source of the rumour related tweets depending on our previous model which is about classifying tweets into five categories: academic, media, government, health professional, and public. Around (60%) of the rumour related tweets are classified as written by health professionals and academics.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
145847
Deposited By:
Deposited On:
27 Oct 2020 14:25
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Nov 2020 08:38