This article suggests two approaches for sharing education research based on a model of research dissemination for Twitter pioneered by the medical community. The first approach is the visual abstract that reflects a visual summary of a study’s textual abstract. Evidence demonstrates that compared with a text-alone approach, the visual abstract yields eight times as many shares on Twitter and three times as many people clicking to access the full article. The second strategy is the translational visual abstract that is designed for sharing education research with nonscientific audiences. I provide a general overview of using Twitter for research dissemination, describe each visual approach and share step-by-step design guidelines, offer recommendations for using these visuals for dissemination purposes, and conclude with future directions for investigating the effectiveness of visual abstracts and translational visual abstracts for communicating education research.
AllenH. G.StantonT. R.Di PietroF.MoseleyG. L. (2013). Social media release increases dissemination of original articles in the clinical pain sciences. PLOS ONE, 8(7), Article e68914. https://doi.org/10.1371/journal.pone.0068914
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165–000
BrescianiS.EpplerM. J. (2015). The pitfalls of visual representations: A review and classification of common errors made while designing and interpreting visualizations. Sage Open, 5(4), 1–14. https://doi.org/10.1177/2158244015611451
7.
BrownleeS. A.IbrahimA. M. (2020). Disseminating research and #VisualAbstracts. In DimickJ. B.LubitzC. C. (Eds.), Health services research (pp. 271–281). Springer. https://doi.org/10.1007/978-3-030-28357-5_23
8.
BruniL.FrancalanciC.GiacomazziP. (2012). The role of multimedia content in determining the virality of social media information. Information, 3(3), 278–289. https://doi.org/10.3390/info3030278
CôtéI. M.DarlingE. S. (2018). Scientists on Twitter: Preaching to the choir or singing from the rooftops?. Facets, 3(1), 682–694. https://doi.org/10.1139/facets–2018–0002
11.
DidegahF.MejlgaardN.SørensenM. P. (2018). Investigating the quality of interactions and public engagement around scientific papers on Twitter. Journal of Informetrics, 12(3), 960–971. https://doi.org/10.1016/j.joi.2018.08.002
12.
EysenbachG. (2011). Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. Journal of Medical Internet Research, 13(4), e123. https://doi.org/10.2196/jmir.2012
13.
HassanS. U.ImranM.GillaniU.AljohaniN. R.BowmanT. D.DidegahF. (2017). Measuring social media activity of scientific literature: An exhaustive comparison of scopus and novel altmetrics big data. Scientometrics, 113(2), 1037–1057. https://doi.org/10.1007/s11192–017–2512–x
14.
HouL. T.PierorazioP. M.KooK. (2020). Impact of social media visual abstracts on media attention and literature citation in urology. The Journal of Urology, 204(5), 905–906. https://doi.org/10.1097/JU.0000000000001136
15.
IbrahimA. M. (2018a). Seeing is believing: Using visual abstracts to disseminate scientific research. The American Journal of Gastroenterology, 113(4), 459–461. https://doi.org/10.1038/ajg.2017.268
IbrahimA. M.LillemoeK. D.KlingensmithM. E.DimickJ. B. (2017). Visual abstracts to disseminate research on social media: a prospective, case–control crossover study. Annals of Surgery, 266(6), e46–e48. https://pubmed.ncbi.nlm.nih.gov/28448382/
PotterM. C.WybleB.HagmannC. E.McCourtE. S. (2014). Detecting meaning in RSVP at 13 ms per picture. Attention, Perception, & Psychophysics, 76(2), 270–279. https://doi.org/10.3758/s13414–013–0605–z
22.
PriemJ.CostelloK. L. (2010). How and why scholars cite on Twitter. Proceedings of the American Society for Information Science and Technology, 47(1), 1–4. https://doi.org/10.1002/meet.14504701201
Robinson-GarcíaN.CostasR.IsettK.MelkersJ.HicksD. (2017). The unbearable emptiness of tweeting—About journal articles. PLOS ONE, 12(8), Article e0183551. https://doi.org/10.1371/journal.pone.0183551
25.
RodriguesJ.JordanN. C.HansenN. (2019). Identifying fraction measures as screeners of mathematics risk status. Journal of Learning Disabilities, 52(6), 480–497. https://doi.org/10.1177/0022219419879684
26.
SmithZ. L.ChiangA. L.BowmanD.WallaceM. B. (2019). Longitudinal relationship between social media activity and article citations in the journalGastrointestinal Endoscopy. Gastrointestinal Endoscopy, 90(1), 77–83. https://doi.org/10.1016/j.gie.2019.03.028
27.
ThelwallM.HausteinS.LarivièreV.SugimotoC. R. (2013). Do altmetrics work? Twitter and ten other social web services. PLOS ONE, 8(5), Article e64841. https://doi.org/10.1371/journal.pone.0064841
28.
ThorpeS.FizeD.MarlotC. (1996). Speed of processing in the human visual system. Nature, 381(6582), 520–522. https://doi.org/10.1038/381520a0
VeletsianosG.KimmonsR. (2016). Scholars in an increasingly open and digital world: How do education professors and students use Twitter?. The Internet and Higher Education, 30, 1–10. https://doi.org/10.1016/j.iheduc.2016.02.002
