Speaking and Being Heard: How Nonprofit Advocacy Organizations Gain Attention on Social Media
Why this work is in the frame
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Bibliographic record
Abstract
The social media era ushers in an increasingly “noisy” information environment that renders it more difficult for nonprofit advocacy organizations to make their voices heard. How then can an organization gain attention on social media? We address this question by building and testing a model of the effectiveness of the Twitter use of advocacy organizations. Using number of retweets and number of favorites as proxies of attention, we test our hypotheses with a 12-month panel dataset that collapses by month and organization the 219,915 tweets sent by 145 organizations in 2013. We find that attention is strongly associated with the size of an organization’s network, its frequency of speech, and the number of conversations it joins. We also find a seemingly contradictory relationship between different measures of attention and an organization’s targeting and connecting strategy.
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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 | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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