The Social Aspects of Sexual Health: A Twitter-Based Analysis of Valentine’s Day Perception
Bibliographic record
Abstract
Sentiment analysis (SA) is a technique aimed at extracting opinions and sentiments through the analysis of text, often used in healthcare research to understand patients’ needs and interests. Data from social networks, such as Twitter, can provide useful insights on sexual behavior. We aimed to assess the perception of Valentine’s Day by performing SA on tweets we collected between 28 January and 13 February 2019. Analysis was done using ad hoc software. A total of 883,615 unique tweets containing the word “valentine” in their text were collected. Geo-localization was available for 48,918 tweets; most the tweets came from the US (36,889, 75.41%), the UK (2605, 5.33%) and Canada (1661, 3.4%). The number of tweets increased approaching February 14. “Love” was the most recurring word, appearing in 111,981 tweets, followed by “gift” (55,136), “special” (34,518) and “happy” (33,913). Overall, 7318 tweets mentioned “sex”: among these tweets, the most recurring words were “sexy” (2317 tweets), “love” (1394) and “gift” (679); words pertaining to intimacy and sexual activity, such as “lingerie”, “porn”, and “date” were less common. In conclusion, tweets about Valentine’s Day mostly focus on the emotions, or on the material aspect of the celebration, and the sexual aspect of Valentine’s Day is rarely mentioned.
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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 | 0.001 |
| 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) | 0.001 | 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".