Emotions in the Twitterverse and Implications for User Interface Design
Bibliographic record
Abstract
This study explores the implications of how user interface elements affect the types of messages that are produced as well as the likelihood that, and extent to which, those messages are spread within an online social system such as Twitter.com, a popular online service for sharing short messages. The current paper explores these issues by studying the dissemination patterns of emotional-type messages among Twitter users through automated techniques, coupled with observations from a survey of Twitter users about their willingness to produce or forward messages containing different types of emotional tone. The results show that Twitter users post more positive messages (tweets) than negative, and that positive tweets are 3 times more likely to be forwarded than negative tweets. The findings also suggest that the Twitter user interface may be partially responsible for this (i.e., the interface reduces the likelihood that negative messages will be posted or retweeted). To enable a wider range of discourse on Twitter and to reduce the need for Twitter users to self-censor their tweets, the paper concludes with a potential design solution that will give Twitter users more control over who will receive their tweets, and outlines a future study to evaluate such an interface.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".