Modern Ubasute: Public Discourse and Sentiment about Older Adults and COVID19 Using Machine Learning
Bibliographic record
Abstract
Abstract This study examined public discourse and sentiment on social media regarding older adults in COVID-19. Twitter data (N=82,893) related to both older adults and COVID-19 and dated from January 23rd to May 20th, 2020, were analyzed. Classification of tweets involved supervised machine learning. Latent Dirichlet Allocation was used to identify dominant themes in public discourse using, accompanied by a qualitative thematic analysis. Sentiment analysis was conducted based on the NRC Emotion Lexicon. The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). More than one in ten (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” The daily average of ageist content was 18%, with the highest of 52.8% on March 11th, 2020. We extracted 14 themes, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. The COVID-19 pandemic has exposed and intensified ageism in our society. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".