Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.06399 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911305466642432 |
|---|---|
| author | Maleki, Ashraf Holmberg, Kim |
| author_facet | Maleki, Ashraf Holmberg, Kim |
| contents | Understanding who shares academic publications on Twitter is critical to interpreting altmetrics as signals of scholarly or societal impact. Prior studies have used diverse and often incompatible user classification schemes, making synthesis difficult. This study presents a systematic review and meta-analysis of 23 empirical studies (covering 79,014 Twitter users, over 20 million tweets, and more than 5 million tweeted publications) to estimate category-specific engagement across three metrics: user counts, tweets, and tweeted publications. We developed a harmonized categorization scheme encompassing 11 user types and applied both Random Effects Models (REM) and Beta-Binomial Hierarchical Models (BBHM) to estimate proportions, account for study-level variation, and model uncertainty. Across all indicators, individual users were the most active, comprising 66% of users, 55% of tweets, and 50% of tweeted publications. BBHM further enabled in-category vs. out-of-category comparisons and revealed engagement differences not detected by REM. T-tests on study-level means confirmed significant differences between academic individuals and other user types. Despite methodological heterogeneity, results consistently show that academic and non-academic individuals statistically equally dominate Twitter engagement with scholarly content. Our findings support the need for standardized user classification schemes and demonstrate the value of Bayesian modeling for synthesizing altmetric data in study variation and sparsity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06399 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Who Are Tweeting About Academic Publications? A Systematic Review and Meta-Analysis of Altmetric Studies Maleki, Ashraf Holmberg, Kim Digital Libraries Understanding who shares academic publications on Twitter is critical to interpreting altmetrics as signals of scholarly or societal impact. Prior studies have used diverse and often incompatible user classification schemes, making synthesis difficult. This study presents a systematic review and meta-analysis of 23 empirical studies (covering 79,014 Twitter users, over 20 million tweets, and more than 5 million tweeted publications) to estimate category-specific engagement across three metrics: user counts, tweets, and tweeted publications. We developed a harmonized categorization scheme encompassing 11 user types and applied both Random Effects Models (REM) and Beta-Binomial Hierarchical Models (BBHM) to estimate proportions, account for study-level variation, and model uncertainty. Across all indicators, individual users were the most active, comprising 66% of users, 55% of tweets, and 50% of tweeted publications. BBHM further enabled in-category vs. out-of-category comparisons and revealed engagement differences not detected by REM. T-tests on study-level means confirmed significant differences between academic individuals and other user types. Despite methodological heterogeneity, results consistently show that academic and non-academic individuals statistically equally dominate Twitter engagement with scholarly content. Our findings support the need for standardized user classification schemes and demonstrate the value of Bayesian modeling for synthesizing altmetric data in study variation and sparsity. |
| title | Who Are Tweeting About Academic Publications? A Systematic Review and Meta-Analysis of Altmetric Studies |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2312.06399 |