The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions
Bibliographic record
Abstract
This paper is the first investigation of the connection between emotion, embodiment, and everyday language in a large sample of natural language data.First, we created corpora of body part mentions (BPMs) in online English text (blog posts and tweets).These include a subset featuring human annotations for the emotions of the person whose body part is mentioned in the text.Next, we show that BPMs are common in personal narratives and tweets (5% to 10% of posts include BPMs) and that their usage patterns vary markedly by time and location.Using word-emotion association lexicons and our annotated data, we show that text containing BPMs tends to be more emotionally charged than text without any BPMs.Finally, we show a strong and statistically significant correlation between body-related language and a variety of negative health outcomes.In sum, we argue that investigating the role of body-part related words in language can open up valuable avenues of future research at the intersection of NLP, the affective sciences, and the study of human wellbeing.
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How this classification was reachedexpand
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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".