How Do Multiple Topics in Terse Tweets Affect Retweeting? Evidence from the 2013 Colorado Floods
Bibliographic record
Abstract
The rapid and wide dissemination of disaster information through tweeting and retweeting has made Twitter an important communication channel during disasters. However, due to its 140-character limit, a tweet could be considered uninformative during disasters. Based on Shannon and Weaver’s communication theory, we use the measure of entropy to quantify the extent to which a tweet is considered informative. We theorize that as a tweet’s entropy increases, its informativeness decreases, and importantly, so too does the probability of retweeting. To assess tweets’ entropy, we use topic modeling to discover topics in tweets. Using tweets collected during the 2013 Colorado floods, we empirically examine the relationship between tweets’ entropy and retweet frequency. We take this investigation one step further by examining the interaction effect of the number of URLs on that relationship. Our empirical results demonstrate the negative effect of entropy on retweet frequency. In addition, the effect of the number of URLs depends on entropy. Our findings suggest that tweets’ entropy is an important factor in explaining tweets’ retweeting mechanism during disasters and enhance the understanding of the relationship between short-length tweets and information dissemination. As a result, our study contributes to IS research on the role of Twitter in emergency communication.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".