Doing More with a DM: A Survey on Library Social Media Engagement
Bibliographic record
Abstract
Objectives – This study sought to determine the role social media plays in shaping library services and spaces, and how queries are received, responded to, and tracked differently by different types of libraries. Methods – In April and May of 2021, researchers conducted a nine-question survey (Appendix A) targeted to social media managers across various types of libraries in the United States, soliciting a mix of quantitative and qualitative results on prevalence of social media interactions, perceived changes to services and spaces as a result of those interactions, and how social media messaging fits within the library’s question reporting or tracking workflow. The researchers then extracted a set of thematic codes from the qualitative data to perform further statistical analysis. Results – The survey received 805 responses in total, with response rates varying from question to question. Of these, 362reported receiving a question or suggestion via social media at least once per month, with 247 reporting a frequency of less than once per month. Respondents expressed a wide range of changes to their library services or spaces as a result, including themes of clarification, marketing, reach, restriction, collections, access, service, policy, and collaboration. Responses were garnered from all types of libraries, with public and academic libraries representing the majority. Conclusion – While there remains a disparity in how different types of libraries utilize social media for soliciting questions and suggestions on library services and spaces, those libraries that participate in the social media conversation are using it as a resource to learn more from their patrons and communities and ultimately are better situated to serve their population.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| 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".