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Social impact in social media: A new method to evaluate the social impact of research
Open accessAbstract
The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.
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“Altmetrics, on the other hand, can serve as a valuable index of societal impact, indicating public engagement and interest in research (Bornmann, 2014; Pulido et al., 2018).”
From Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among 2222 scientific abstracts (2024) · cites this paper as Pulido et al., 2018
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Metadata
- Authors
- Pulido, C. M., Redondo-Sama, G., Sordé-Martí, T., & Flecha, R.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- Universidad de Zaragoza (ES)
- Corresponding author
- Pulido, Cristina M.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-08-29
- Volume
- 13
- Issue
- 8
- Pages
- e0203117
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6114920
- OpenAlex
- W2889275779
- Cited by
- 158
- References
- 19
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 16 · 2023: 19 · 2022: 20 · 2021: 28 · 2020: 34 · 2019: 12 · 2018: 2
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Zaguan (University of Zaragoza Repository) (submittedVersion, cc-by)
- Funders
- FP7 Socio-Economic Sciences and Humanities (Grant Agreement n° 613202)
- Topics
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- social impact of research, social media, altmetrics, research impact assessment, Twitter, Facebook, citizen participation
- MeSH terms
- Biology, Communication, Humans, Public Health, Research, Social Change, Sociometric Techniques, Genomics, Qualitative Research, Social Media
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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