Detection and Visualization of Emotions in an Affect-Aware City
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
Smart cities use various deployed sensors and aggregate their data to create a big picture of the live state of the city. This live state can be enhanced by incorporating the affective states of the citizens. In this work, we automatically detect the emotions of the city's inhabitants from geo-tagged posts on the social network Twitter. Emotions are represented as four-dimensional vectors of pleasantness, arousal, dominance and unpredictability. In a training phase, emotion-word hashtags in the messages are used as the ground truth emotion contained in a message. A neural network is trained by using the presence of words, hashtags and emoticons in the messages as features. During the live phase, these features are extracted from new geo-tagged Twitter messages and given as input to the neural network. This allows the estimation of a four-dimensional emotion vector for a new message. The detected emotions are aggregated over space and time and visualized on a map of the city.
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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 | 0.001 | 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