scholarly journals Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis (Preprint)

2020 ◽  
Author(s):  
Eisa Alanazi ◽  
Abdulaziz Alashaikh ◽  
Sarah Alqurashi ◽  
Aued Alanazi
Keyword(s):  
Saudi Arabia ◽  
Assessment System ◽  
Clinical Settings ◽  
Chronological Order ◽  
Specific Group ◽  
Related Data ◽  
Self Reports ◽  
Risk Estimator ◽  
Twitter Users ◽  
Asymptomatic Individuals

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.

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

10.2196/21329 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e21329
Author(s):  
Eisa Alanazi ◽  
Abdulaziz Alashaikh ◽  
Sarah Alqurashi ◽  
Aued Alanazi
Keyword(s):  
Saudi Arabia ◽  
Assessment System ◽  
Clinical Settings ◽  
Chronological Order ◽  
Specific Group ◽  
Related Data ◽  
Self Reports ◽  
Risk Estimator ◽  
Twitter Users ◽  
Asymptomatic Individuals

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.

scholarly journals Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter

2020 ◽  
Author(s):  
Eisa Alanazi ◽  
Abdulaziz Alashaikh ◽  
Sarah Alqurashi ◽  
Aued Alanazi
Keyword(s):  
Arabic Language ◽  
Assessment System ◽  
Valuable Resource ◽  
Chronological Order ◽  
Specific Group ◽  
Related Data ◽  
Self Reports ◽  
The Common ◽  
Risk Estimator ◽  
Twitter Users

AbstractBackgroundMassive amount of covid-19 related data is generated everyday by Twitter users. 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 the common symptoms and whether their order of appearance differs among different groups in the community. With sufficient available data, this has the potential of developing a covid-19 risk-assessment system that is tailored toward specific group of people.ObjectiveThe 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.MethodsWe 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.ResultsThe 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).ConclusionsThis study demonstrates that Twitter is a valuable resource to analyze and identify COVID-19 early symptoms within the Arabic content of Twitter. It also suggests the possibility of developing a real-time covid-19 risk estimator based on the users’ tweets.

Lungsounds@UA Interface and Multimedia Database

2014 ◽  
Vol 5 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Cátia Pinho ◽  
Ana Oliveira ◽  
Daniela Oliveira ◽  
João Dinis ◽  
Alda Marques
Keyword(s):  
Pilot Study ◽  
User Interfaces ◽  
Graphical User Interfaces ◽  
Respiratory Sounds ◽  
Multimedia Database ◽  
Clinical Settings ◽  
Related Data ◽  
Future Developments ◽  
Healthcare Technologies ◽  
Available Information

The development of graphical user interfaces (GUIs) has been an emergent demand in the area of healthcare technologies. Specifically for respiratory healthcare there is a lack of tools to produce a complete multimedia database, where respiratory sounds and other clinical data are available in a single repository. This is essential for a complete patients' assessment and management in research/clinical settings. Therefore, this study aimed to develop a usable interface to collect and organise respiratory-related data in a single multimedia database. A GUI, named LungSounds@UA, composed by a multilayer of windows, was developed. The usability of the user-centred interface was assessed in a pilot study and in an evaluation session. The users testified the utility of the application and its great potential for research/clinical settings. However, some drawbacks were identified, such as a certain difficulty to intuitively navigate in the great amount of the available information, which will inform future developments.

scholarly journals Questioning the Effort Hypothesis That Depressed Patients Perform Disproportionately Worse on Effortful Cognitive Tasks

2020 ◽  
Vol 127 (2) ◽  
pp. 401-414
Author(s):  
Dustin B. Hammers ◽  
Sara Weisenbach
Keyword(s):  
Digit Span ◽  
Severe Depression ◽  
Clinical Settings ◽  
Trail Making Test ◽  
Moderate Depression ◽  
Self Reports ◽  
Trail Making ◽  
Neuropsychological Evaluations ◽  
Accuracy Indices ◽  
Effort Hypothesis

The debate over Hasher and Zacks’ effort hypothesis—that performance on effortful tasks by patients with depression will be disproportionately worse than their performance on automatic tasks—shows a need for additional research to settle whether or not this notion is “clinical lore.” In this study, we categorized 285 outpatient recipients of neuropsychological evaluations into three groups—No Depression, Mild-to-Moderate Depression, and Severe Depression—based on their Beck Depression Inventory-2 self-reports. We then compared these groups’ performances on both “automatic” and “effortful” versions of the Ruff 2 & 7 Selective Attention Test Total Speed and Total Accuracy Indices, the Digit Span subtest from the Wechsler Adult Intellectual Scale—Fourth Edition, and Trail Making Test Parts A and B, using a two-way (3 × 2) mixed multivariate analysis of variance. Patients with Mild-to-Moderate Depression or Severe Depression performed disproportionately worse than patients with No Depression in our sample on more effortful versions of only one of the four attention or executive functioning measures (Trail Making Test). Thus, these data failed to fully support a hypothesis of disproportionately worse performance on more effortful tasks. While this study failed to negate the effort hypothesis in some specific instances, particularly for use in the Trail Making Test, there is cause for caution in routinely applying the effort hypothesis when interpreting test findings in most clinical settings and for most measures.

scholarly journals Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource

2020 ◽  
Vol 27 (8) ◽  
pp. 1310-1315 ◽  
Author(s):  
Abeed Sarker ◽  
Sahithi Lakamana ◽  
Whitney Hogg-Bremer ◽  
Angel Xie ◽  
Mohammed Ali Al-Garadi ◽  
...  
Keyword(s):  
Clinical Studies ◽  
Future Research ◽  
Clinical Settings ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Self Reports ◽  
Body Ache ◽  
Standard Concept

Abstract Objective To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. Materials and Methods We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. Results We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. Conclusion The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.

scholarly journals Anime affection on human IQ and behavior in Saudi Arabia

2021 ◽  
Vol 14 (2) ◽  
pp. 143-154
Author(s):  
Saud Abdullah S Alsahlly ◽  
Sultan Khalid M Algmrawi ◽  
Ahmad Saeed A Alshehri ◽  
Nasser Talal N Alotiby ◽  
Mohammad Arshad ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Research Design ◽  
Research Method ◽  
Research Question ◽  
Social Aspects ◽  
General Knowledge ◽  
Specific Group ◽  
Descriptive Research ◽  
Direct Observations ◽  
And Behavior

The present study attempted to determine the effects of watching anime and understanding if watching anime could affect the mental and social aspects of kids or other group of ages, and also to decide that the teenagers and children should watch anime or not. The research design used in this study is the descriptive research method and observational where in data and facts from direct observations and online questionnaires were used to answer the research question. The finding of this study suggested that anime viewers has higher level of general knowledge comparing with the non- anime viewers and as well as higher IQ level significantly in a specific group, besides anime can be used to spread a background about any culture and plays a role in increase the economy.

scholarly journals First Report of Multi-drug Resistant Staphylococcus haemolyticus in Nosocomial Infections in North Western Saudi Arabia

2021 ◽  
Author(s):  
Tahani F. H. Alahmadi ◽  
Ziab Z. Alahmadey ◽  
Sameer R. Organji ◽  
Khaled Elbanna ◽  
Iqbal Ahmad ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Antimicrobial Agents ◽  
Clinical Settings ◽  
Drug Resistant ◽  
Mannitol Salt Agar ◽  
Staphylococcus Haemolyticus ◽  
Emerging Trends ◽  
Primary Identification ◽  
First Time ◽  
North Western

We report in this study for the first time the prevalence of multiple resistant Staphylococcus haemolyticus in clinical settings in Saudi Arabia. A total of 1060 clinical specimens of hospitalized patients were screened for the presence of S. haemolyticus in the period between September and December 2020. Primary identification of the isolates was carried out by colonial characteristics on mannitol salt agar and clumping factor test, confirmation of presumptive isolates and antimicrobial susceptibility testing was performed by Vitek® 2, while PCR was employed to detect mecA and vanA genes. A total of 20 S. haemolyticus isolates were recovered from 20 samples (blood cultures, urine, nasal swab, wound swab, groin swab, and axilla swab), 90% (P <0.001, x2) of the isolates were multiple resistant to three antimicrobial agents and more. Resistance to oxacillin was exhibited in 95% of the isolates, while none of the isolates were resistant to vancomycin and linezolid, yet resistance to rifampicin was observed in 30 % of the isolates. The findings of this study highlights the emerging trends of Staphylococcus haemolyticus as potential drug resistant pathogen in hospital settings in Saudi Arabia, which requires in depth investigation on molecular understanding on antimicrobial resistance and virulence traits of the strains.

scholarly journals Subtyping of Blastocystis sp. isolated from symptomatic and asymptomatic individuals in Makkah, Saudi Arabia

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Raafat T. Mohamed ◽  
Mohammed A. El-Bali ◽  
Anhar A. Mohamed ◽  
Mona A. Abdel-Fatah ◽  
Mohamed A. EL-Malky ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Blastocystis Sp ◽  
Asymptomatic Individuals

scholarly journals Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

2021 ◽  
Vol 18 (24) ◽  
pp. 13388
Author(s):  
Samar Binkheder ◽  
Raniah N. Aldekhyyel ◽  
Alanoud AlMogbel ◽  
Nora Al-Twairesh ◽  
Nuha Alhumaid ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Sentiment Analysis ◽  
Health Sector ◽  
Public Perceptions ◽  
Support Vector ◽  
The Public ◽  
Mobile Health Applications ◽  
Twitter Users ◽  
Mhealth Apps ◽  
Health Applications

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

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