Analysis and prediction of influenza in the UAE based on Arabic tweets

Alkouz, Balsam; Aghbari, Zaher Al

doi:10.1109/icbda.2018.8367652

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…Several studies have utilized the abundance of information offered by social platforms to conduct nonclinical medical research. For example, Twitter has been a source of data for many health and medical studies, such as surveillance and monitoring of flu and cancer timelines and distribution across the United States [ 1 ], analyzing the spread of influenza in the United Arab Emirates based on geotagged tweets in Arabic [ 2 ], and the surveillance and monitoring of influenza in the United Arab Emirates based on tweets in Arabic and English [ 3 ]. In addition, Twitter data have been utilized in symptom and disease identification in Saudi Arabia [ 4 ], and most recently, to examine COVID-19 symptoms as reported on Twitter [ 5 ] and to analyze the chronological and geographical distribution of infected tweeters in the United States [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Alanazi¹,

Alashaikh²,

Alqurashi³

et al. 2020

J Med Internet Res

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Background A substantial amount of COVID-19–related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. Objective The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. Methods We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. Results The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%). Conclusions This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator.

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Section: Introductionmentioning

confidence: 99%

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Alanazi¹,

Alashaikh²,

Alqurashi³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…They reported to have obtained a best classifier results using SVM with Linear Support Vector Classification and Stochastic Gradient Descent. Alkouz and Aghbari [16] detect influenza in the UAE from Arabic tweets. They classified tweets and used them to predict the number of future hospital visits using a linear regression model.…”

Section: Twitter Data Analytics In Healthcare (Arabic)mentioning

confidence: 99%

“…Moreover, some works are available in Modern Standard Arabic (MSA), but in general (not specific to healthcare), the works on Arabic dialects are very limited in number and scope [10,14]. The three works in Arabic specific to healthcare [15][16][17] that we have discussed in the previous section are limited in scope, depth, and/or functionalities. There is no known work in data analytics specifically on Saudi Arabic dialect in healthcare.…”

Section: Research Gapmentioning

confidence: 99%

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

et al. 2020

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Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

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“…Several researches have utilized the abundance of information offered by social platforms to conduct non-clinical medical research. For example, Twitter has been the source for data for many health and medical studies; such as surveillance and monitoring of Flu and Cancer timeline and distribution across the USA using Twitter [1], analyzing the spread of influenza in the UAE based on geo-tagged Arabic Tweets [2], surveillance and monitoring of Influenza in the UAE based on Arabic and English tweets [3], identifying symptoms and disease in Saudi Arabia using Twitter [4], and most recently on analyzing COVID-19 symptoms on Twitter [5]. and analyzing the chronological and geographical distribution of COVID-19 infected tweeters in the USA [6].…”

Section: Introductionmentioning

confidence: 99%

Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter

Alanazi

Alashaikh

Alqurashi

et al. 2020

Preprint

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Objective: The aim of this study is to identify the most common symptoms reported by covid-19 patients in the Arabic language and order the symptoms appearance based on the collected data. Methods: We search the Arabic content of Twitter for personal reports of covid-19 symptoms from March 1st to May 27th, 2020. We identify 463 Arabic users who tweeted testing positive for covid-19 and extract the symptoms they publicly associate with covid-19. Furthermore, we ask them directly through personal messages to opt in and rank the appearance of the first three symptoms they experienced right before (or after) diagnosed with covid-19. Finally, we track their Twitter timeline to identify additional symptoms that were mentioned within +-5 days from the day of tweeting having covid-19. In summary, a list of 270 covid-19 reports were collected and symptoms were (at least partially) ranked from early to late. Results: The collected reports contained roughly 900 symptoms originated from 74% (n=201) male and 26% (n=69) female Twitter users. The majority (82%) of the tracked users were living in Saudi Arabia (46%) and Kuwait (36%). Furthermore, 13% (n=36) of the collected reports were asymptomatic. Out of the users with symptoms (n=234), 66% (n=180) provided a chronological order of appearance for at least three symptoms. Fever 59% (n=139), Headache 43% (n=101), and Anosmia 39% (n=91) were found to be the top three symptoms mentioned by the reports. They count also for the top-3 common first symptoms in a way that 28% (n=65) said their covid journey started with a Fever, 15% (n=34) with a Headache and 12% (n=28) with Anosmia. Out of the Saudi symptomatic reported cases (n=110), the most common three symptoms were Fever 59% (n=65), Anosmia 42% (n=46), and Headache 38% (n=42).

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Analysis and prediction of influenza in the UAE based on Arabic tweets

Cited by 11 publications

References 13 publications

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter

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