Emotions in the Twitterverse and Implications for User Interface Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it