Creating conversation: how Bell Let's Talk produces engaging mental health content on Twitter
Bibliographic record
Abstract
This pilot study explores how Bell Let’s Talk, a mental health initiative to foster positive conversation about mental health in Canada, uses Twitter to disseminate mental health messages with the intention of increasing awareness and reducing stigma. A content analysis was conducted of 89 tweets posted by the official Bell Let’s Talk Twitter account, @Bell_LetsTalk between December 1, 2016 and January 31, 2017 to establish the overall engagement of content, examine which content receives the highest engagement and establish which message function creates most conversation. The results suggest Bell Let’s Talk produces medium engagement content. The majority of tweets feature a non-celebrity influencer (n=37) or non-influencer (n=37). However, celebrity content had the highest level of engagement (mdn=1102). Of the communication features used, links were the most frequently utilized (n=52). Public-centric topics (n=45) were the most common type of tweet, yet organizational-centric action tweets received the highest level of engagement (mdn=1382). The results of this pilot study suggest Bell Let’s Talk produces content of medium. They also indicate there is potential for further research to build upon and improve health professionals’ knowledge regarding successful content about mental health on Twitter.
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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 | 0.005 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".