COVID-19 and Arabic Twitter : How can Arab World Governments and Public Health Organizations Learn from Social Media? (2020)

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.

## introduction the current coronavirus disease (covid-19) outbreak is of major global concern and is classified by the world health organization as an international health emergency. governments around the world have taken different decisions in order to stop the spread of the disease. many academic researchers in various fields including natural language processing (nlp) have carried out studies targetting this subject. for example, covid-19 and ai foot_0 is one of the conferences that has been convened virtually to show how artificial intelligence (ai) can contribute in helping the public health organizations during pandemics. people use social media applications such as twitter to find the news related to covid-19 and/or express their opinions and feelings about it. as a result, a vast amount of information could be exploited by nlp researchers for a myriad of analyses despite the informal nature of social media writing style. we hypothesise that public health organizations (phos) may benefit from mining the topics discussed between people during the pandemic. this may help in understanding a population's current and changing concerns related to the disease and help to find the best solutions to protect people. in addition, during an outbreak people themselves will search online for reliable and trusted information related to the disease such as prevention and transmission pathways. covid-19 twitter conversations may not correlate with the actual disease epidemiology. therefore, public health organizations have a vested interest in ensuring that information spread in the population is accurate @xcite . for instance, the ministry of health in saudi arabia 2 presents a daily press conference incorporating the aim of quickly stopping the spread of false rumours. however, there is currently a prolonged period of time until warnings are issued. for example, the first tweet that included false information about hot weather killing the virus was on 10 february 2020, while the press conference which responded to this rumour on 14 april 2020. there is a clearly a need to find false information as quickly as possible. in addition, effort needs to be made in relation to tracking the user accounts that promote rumours. this can be undertaken using a variety of techniques, for example using social network features, geolocation, bot detection or content based approaches such as language style. public health organizations would benefit from speeding up the process of tracking in order to stop rumours and remove the bot networks. the vast majority of the previous research in this area has been on english twitter content but this will not directly assist phos in arabic speaking countries. the arabic language is spoken by 467 million people in the world and has more than 26 dialects foot_2 . of particular importance for nlp is coping with dialectal and/or meaning differences in less formal settings such as social media. as an example in health field, the word ( ) may be understood as vaccination 4 in modern standard arabic or reading supplications in najdi dialect 5 . there has been much recent progress in arabic nlp research yet there is still an urgent need to develop fake news detection for arabic tweets @xcite . in this paper, we have combined qualitative and quantitative studies to analyse arabic tweets aiming to support public health organizations who can learn from social media data along various lines: ## related work there is a vast quantity of research over recent years that analyses social media data related to different pandemics such as h1n1 (2009), ebola (2014), zika fever (2015), and yellow fever (2016). these studies followed a variety of directions for analysis with multiple different goals @xcite . the study of @xcite used a thematic analysis of tweets related to the h1n1 pandemic. eight key themes emerged from the analysis: emotion, health related information, general commentary and resources, media and health organisations, politics, country of origin, food, and humour and/or sarcasm. a survey study @xcite ) reviewed the research relevant to the ebola virus and social media. it compared research questions, study designs, data collection methods, and analytic methods. @xcite used content analysis to identify the topics discussed on twitter at the beginning of the 2014 ebola epidemic in the united states. in @xcite , they determined the geolocation of the ebola tweets and named the accounts that interacted more on twitter related to the 2014 west african ebola outbreak. the main goal of the study by @xcite was to distinguish between credible and fake tweets. it highlighted the problems of manual labeling process with verification needs. the study in @xcite highlighted how the problem of misinformation changed during the disease outbreak and recommended a longitudinal study of information published on social media. moreover, it pointed out the importance of understanding the source of this information and the process of spreading rumours in order to reduce their impact in the future. @xcite tracked zika fever misinformation on social media by comparing them with rumours identified by the world health organization. also, they pointed out the importance of credible information sources and encouraged collaboration between researchers and health organizations to rapidly process the misinformation related to health on social media. the study in (ortiz-martínez and jiménez-arcia, 2017) reviewed the quality of available yellow fever information on twitter. it also showed the significance of the awareness of misleading information during pandemic spread. the study of @xcite summarised other studies related to social media rumours. it illustrated techniques for developing rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. @xcite mentioned the importance of twitter information during the epidemic and how public health organisations can benefit from this. they showed the requirement to monitor false information posted by some accounts and recommended that this was performed in real time to reduce the danger of this information. also, they discussed the lack of available datasets which help in the development of rumour classification systems. researchers have been doing studies on building and analysing covid-19 twitter datasets since the disease appeared in december 2019. so far, there are two different datasets which have been published recently related to arabic and covid-19 @xcite and @xcite . the former collected 3,934,610 million tweets until april 15 2020, and the latter included around 748k tweets until march 31, 2020. these papers contain an initial analysis and statistical results for the collected tweets and some suggestions for future work, which include pandemic response, behavior analysis, emergency management, misinformation detection, and social analytics. on the other hand, there are some datasets in english such as @xcite and @xcite . also, there is a multilingual covid-19 dataset containing location information @xcite . this contains more than 524 million tweets, with 5.5 million arabic tweets, posted over a period of three months since february 1, 2020. it focuses on determining the geolocation of a tweet which can help research with various different challenges, including identifying rumours. although the above studies have produced datasets related to covid-19, they do not analyse them deeply using nlp methods. previous studies representing earlier epidemics present good techniques and results, however none of them are related to arabic tweets. therefore, to assist phos in arabic speaking countries there is an urgent need to analyse tweets related to covid-19 using multiple arabic nlp techniques. ## update arabic infectious disease ontology with the recent appearance of covid-19 as a new disease, there is need to update our arabic infectious disease ontology @xcite , which integrates the scientific and medical vocabularies of infectious diseases with their informal equivalents used in general discourse. we collated covid-19 information from the world health organization foot_5 and ministry of health in saudi arabia. this included symptom, cause, prevention, infection, organ, treatment, diagnosis, place of the disease spread, and slang terms for covid-19 and extended our ontology foot_6 . these terms were then used in our collection process. ## data collection we began collecting arabic tweets about a number of infectious diseases from september 2019. here in this paper, we analysed only the tweets related to covid-19 from december 2019 to april 2020 (there are a few tweets between september and november, these are related to middle east respiratory syndrome coronavirus, mers-cov foot_7 ). we have collected approximately six million tweets in arabic during this period. we obtained the tweets depending on three keywords ( , , ) which mean coronavirus, a misspelling of the name of coronavirus, and covid-19 respectively in english. we collected the tweets weekly using twitter api. next, we pre-processed the tweets through a pipeline of different steps: after pre-processing, the resulting dataset was 1,048,575 unique tweets from the original 6,578,982 collected. figure 1 shows the number of arabic tweets about coronavirus each week with specific dates highlighted to show government decisions on protecting the population from covid-19 and other key dates for context. ## methods we performed three different types of analysis on the collected data. firstly, in order to better understand the topics discussed in the corpus, we carried out a cluster analysis. secondly, taking a sample of the corpus, we performed rumour detection. finally, we extended our previous work to classify the source of tweets into five types of twitter users which aims at helping to determine their veracity. ## (false) the ministry of health: a virus lives and is mainly concentrated in the respiratory system, so it is not likely to be transmitted by insects or by mosquito bites. oh god, in this blessed hour, we ask you to have mercy on us and keep away from us all disease and calamity, and protect us from the evil of diseases and sicknesses. preserve our country and other muslim countries. 0 (unrelated) ## conclusion and future work in this paper, we identified and analysed one million tweets related to the covid-19 pandemic in the arabic language. we performed three experiments which we expect can help to develop methods of analysis suitable for helping arab world governments and public health organisations. our analysis first identifies the topics discussed on social media during the epidemic, detects the tweets that contain false information, figure 4: results using support vector classification figure 5: results using naïve bayes and predicts the source of the rumour related tweets based on our previous model for other diseases. the clustered topics are covid-19 statistics, prayers for god, covid-19 locations, advise for preventing education, and advertising. our second contribution is a labeled sample of tweets (2,000 out of 1 million) annotated for false information, correct information, and unrelated. to investigate the replicability and scalability of this annotation, we applied multiple machine learning algorithms with different sets of features. the highest accuracy result was 84% achieved by the lr classifier with count vector set of features and svc with tf-idf. finally, we also used our previous model to predict the source types of the sampled tweets. around 60% of the rumour related tweets are classified as written by health professional and academics which shows the urgent need to respond to such fake news. the dataset, including tweet ids, manually assigned labels for the sampled tweets, and other resources used in this paper are made freely available for academic research purposes 11 . there are clearly many potential future directions related to analysing social media data on the topics of pandemics. since false information has the potential to play a dangerous role in topics related to health, there is a need to enhance and automate the automatic detection process supporting different languages beyond just english. future potential directions include monitoring the spread of the disease by finding the infected individuals, defining the infected locations, or observing people that do not apply self isolation rules. moreover, the analysis could proceed in an exploratory and thematic way such as discovering further topics discussed during the epidemic, as well as assisting governments and public health organisations in measuring people's concerns resulting from the disease.