Using Reddit data to investigate perspectives on the COVID-19 pandemic using natural language processing: a comparative study of the US, the UK, Canada and Australia (Preprint)
Bibliographic record
Abstract
BACKGROUND Since COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11, 2020, the disease has had an unprecedented impact worldwide, with, as of December 21, 2021, more than 276 million confirmed cases and 5.3 million deaths[1]. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health promotion messaging. OBJECTIVE With this work, we compare public attitudes towards the 2020/2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. METHODS We utilized a natural language processing method called topic modeling (more specifically Latent Dirichlet Allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically in- fer topics (i.e. semantically-related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19-related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. RESULTS From the Reddit data we found that (1) the volume of posting declined consistently across all four countries during the study period (Feb. 2020 to Nov. 2020); (2) during lockdown events, the volume of posts peaked; and (3) the UK and Australian subreddits contained much more policy discussion – and less conspiratorial content – than the US or Canadian subreddits. CONCLUSIONS This work demonstrated that (a) there were key differences between salient topics discussed across the four countries, and (b) Reddit data has the potential to provide insights not readily apparent in survey-based approaches.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".