Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Sarker, Abeed; Lakamana, Sahithi; Hogg-Bremer, Whitney; Xie, Angel; Al-Garadi, Mohammed Ali; Yang, Yuan-Chi

doi:10.1101/2020.04.16.20067421

Cited by 46 publications

(45 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For a more detailed analysis of the tweets, the emotional quotients associated with the tweets were analyzed. Sentiment analysis using the National Research Council (NRC) sentiment lexicon enabled us to examine the expression of 10 terms related to basic emotions: anger , anticipation , disgust , fear , joy , negative , positive , sadness , surprise , and trust [ 26 , 27 ]. The terms positive and negative were removed because they are classifications and do not indicate positive or negative emotions; also, emotions (eg, fear or joy) are indicated by the NRC sentiment lexicon.…”

Section: Methodsmentioning

confidence: 99%

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

Boon‐itt¹,

Skunkan²

2020

JMIR Public Health Surveill

367

224

View full text Add to dashboard Cite

Background COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.

show abstract

Section: Methodsmentioning

confidence: 99%

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

Boon‐itt¹,

Skunkan²

2020

JMIR Public Health Surveill

367

224

View full text Add to dashboard Cite

show abstract

“…Finally, we compared the symptom prevalence of our study to the one provided by Sarker et al [ 5 ], in order to assess similarities and differences in COVID-19 symptoms experienced by different populations. As seen in Table 5 and Figure 5 , our findings complement those of Sarker et al [ 5 ] (correlation coefficient=0.72).…”

Section: Resultsmentioning

confidence: 99%

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Sarker et al [88] mined Twitter to analyze symptoms of COVID-19 from self-reported users. The authors identified 203 COVID-19 patients while searching Twitter streaming API with expressions related to self-report of COVID-19.…”

Section: Social Media Datamentioning

confidence: 99%

COVID-19 open source data sets: a comprehensive survey

et al. 2020

View full text Add to dashboard Cite

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligencebased analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

show abstract

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

Cited by 46 publications

References 17 publications

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

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

COVID-19 open source data sets: a comprehensive survey

Contact Info

Product

Resources

About