Analysis of Social Media Perceptions During the COVID-19 Pandemic in the United Kingdom: Social Listening Study (2019-2022)
Bibliographic record
Abstract
Background: Social media listening can be leveraged to obtain authentic perceptions about events, their impact, guidelines, and policies. There has been to date no research that has examined the experiences of patients with COVID-19 from diagnosis to treatment using social media listening in the United Kingdom. Objective: This study aimed to assess public perceptions, insights, and sentiments throughout the patient journey from diagnosis to treatment during the COVID-19 pandemic. Methods: A comprehensive search query was designed to retrieve social media data that referred to COVID-19 and treatment. The search was conducted using the social media monitoring tool, Synthesio (Ipsos). Data were retrospectively collected for the period covering September 2019 to September 2022 from Twitter (subsequently rebranded X), Facebook, Instagram, and YouTube as well as 126 public forums (including Health Unlocked, Mums Net, The Student Room, and Patient Forums UK). Available data in the United Kingdom expressed in the English language were collected and filtered, generating a final dataset consisting of 31,319 posts from an overall initial dataset of 706,634 posts. Complimentary Google trend analyses of search terms mentioning COVID-19 treatments were also performed. Results: Social media posts related to COVID-19 symptoms accounted for 6% of overall posts, compared to 35% of posts related to testing, 25% of posts related to diagnosis, and 32% of posts related to treatment. Overall, the trend observed from social media posts relating to COVID-19 treatment extracted in Synthesio was largely congruent with the trend of COVID-19 searches on Google, indicating a potential relationship between public discourse and social media and internet search behavior. Conclusions: The findings from this study have the potential to inform decision-making regarding public health interventions, communication strategies, and health care policies in the United Kingdom during future public health emergencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".