Movember: Twitter Conversations of a Hairy Social Movement
Bibliographic record
Abstract
Movember is an annual “month-long celebration of the moustache” where men grow a mustache and raise money in the largest philanthropic endeavor for men’s health. Movember is predominantly an online campaign, and consequently, participants have actively embraced social media; this is evidenced in the 1,879,994 tweets collected during Movember 2012 in this research project. This article presents an analysis of Movember that examines how individuals use the numerous syntactical features of Twitter to engage in conversation and share information in order to develop a nuanced understanding of how people are utilizing social media as part of the social movement. While Movember has been successful in gaining traction on social media, the Twitter data point to surprising conclusions that have implications for understanding non-profits and social movements online. The following study provides two main contributions to existing sociotechnical social movement literature using a mixed-methods approach. First, the findings suggest that there is limited true conversation taking place although the stated purpose of the campaign is to facilitate conversation. Second, the findings identify that participants are more engaged with Movember as a branded movement than engaged in health promotion. While the tweets are conversational in form, they are largely not conversational in function, which points to Twitter being used as a broadcast tool in this context. These findings have broad implications for understanding how social media is used to engage individuals in social campaigns and engage with each other and share information.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".