Twitter Sentiment Analysis of Long COVID Syndrome
Bibliographic record
Abstract
Background Long COVID syndrome originated as a patient phrased terminology which was initially used to describe a group of vague symptoms that persisted after recovering from COVID-19. However, it has moved from a patient lingo to a recognized pathological entity which refers to a group of symptoms that lasts weeks or months after the COVID-19 illness. The novelty of this condition, the inadequacy of research on long COVID syndrome, and its origin as a patient-coined terminology necessitated exploring the disease's sentiments and conversations by analyzing publicly available tweets. Method Tweets were extracted using the Twarc2 tool for tweets in the English language with the keywords (long COVID syndrome, long COVID, post-COVID syndrome, post-acute sequelae of SARS-CoV-2, long-term COVID, long haulers, and chronic COVID syndrome) between March 25, 2022, and April 1, 2022. The analyses included frequency of the keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language approach and the latent Dirichlet allocation algorithm were used to determine the most shared tweet topics, categorize clusters, and identify themes based on keyword analysis. Results The search yielded 62,232 tweets. The tweets were reduced to 10,670 tweets after removing the duplicates. The vast majority of the tweets originated from the United States of America (38%), United Kingdom (30%), and Canada (13%), with the most common hashtags being #longcovid (36%) and #covid (6.36%), and the most frequently used word being people (1.05%). The top three emotions detected by our analysis were trust (11.68%), fear (11.26%), and sadness (9.76%). The sentiment analysis results showed that people have comparable levels of positivity (19.90%) and negativity (18.39%) towards long COVID. Conclusions Our analysis revealed comparable sentiments about long COVID syndrome, albeit slightly positive. Most tweets connoted trust (positive), fear (negative), and sadness (negative). These emotions were linked with concerns about the infection, pandemic, chronic disability, and governmental policies. We believe this study would be important in guiding information dissemination and governmental policy implementation necessary in tackling long COVID syndrome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".