Panic buying behavior analysis of COVID-19 related toilet paper hoarding content on Twitter
Bibliographic record
Abstract
Background: In March 2020, the COVID-19 outbreak has led to unprecedented panic buying phenomena across the globe, where social media has played a significant role in the dissemination of observations. Purpose: We used Twitter data to qualitatively analyse Tweets related to panic buying of toilet paper during the crisis. Methods: A dataset of n=255,171 Tweets were collected between 29th February to 29thMarch 2020. The n=4,081 with 10 or more Retweets were selected and separated into batches of 100 Tweets for analysis by adopting a grounded theory approach until a saturation was met. Results: Five key themes emerged from our analysis of the dataset: 1) humour or sarcasm, 2) marketing or profiteering, 3) opinion and emotions, 4) personal experience, and 5) support or information. Discussion: About half of the Tweets carried negative sentiments, expressing anger or frustration towards the deficiency of toilet paper and the frantic situation of toilet paper hoarding, which were among the most influential Tweets. The spontaneous contagion of fear and panic through social media could fuel psychological reactions in midst of crises. Conclusions: Our study demonstrated the application of social media data, a contemporary research method to provide rapid infodemiology of public psychology. In a pandemic or crisis situation, real-time data could be monitored and content-analysed for authorities to promptly address public concerns and sentiments. Findings have implications on how the government and related stakeholders could monitor and react to what social media information can reveal about public psychology during a crisis.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".