Social Media Interaction as Informal Science Learning: a Comparison of Message Design in Two Niches

Abstract

Social media provides science learners opportunities to interact with content-specific messages. However, most science-specific social media content is designed to disseminate information instead of encouraging dialog. In this novel, ex post facto exploratory study of a science social media community, we sought to understand the relationships among community member interaction, design elements of messages, and post type on two digital niches (i.e., Facebook and Twitter). Framed by the theory of symbolic interactionism, we conducted a content analysis of 1370 messages that were systematically created by an informal science learning project and found that usage frequency of messaging elements varied by niche; interaction within each niche differed, varying by messaging element; and differential interaction was found to be associated with post types within Facebook only. This study suggests a pathway for developing and examining social media as an educational component of informal science learning.

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References

  1. Barriault, C. (2010). Assessing exhibits for learning in science centers: a practical tool. Visitor Studies, 13(1), 90–106.

    Article  Google Scholar 

  2. Bell, P., Lewenstein, B., Shouse, A. W., & Feder, M. A. (Eds.). (2009). Learning science in informal environments. Washington, D.C.: National Academies Press. doi:https://doi.org/10.17226/12190

  3. Bex, R. T., Lundgren, L., & Crippen, K. J. (2019). Scientific Twitter: The flow of paleontological communication across a topic network. PLOS ONE, 14(7), e0219688. https://doi.org/10.1371/journal.pone.0219688.

    Article  Google Scholar 

  4. Bruns, A., & Stieglitz, S. (2012). Quantitative approaches to comparing communication patterns on Twitter. Journal of Technology in Human Services, 30(3–4), 160–185. https://doi.org/10.1080/15228835.2012.744249.

    Article  Google Scholar 

  5. Bucher, T., & Helmond, A. (2017). The affordances of social media platforms. In J. Burgess, T. Poell, & A. Marwick (Eds.), The SAGE handbook of social media. London: SAGE Publications, Ltd..

    Google Scholar 

  6. Budge, K. (2017). Objects in focus: museum visitors and Instagram. Curator: The Museum Journal, 60(1), 67–85. https://doi.org/10.1111/cura.12183.

    Article  Google Scholar 

  7. Bugeaud, T., Benton, J., Hopper, K., Backer, J., Valverde, W., Vanderbilt, S., Giorgione, E., McClune, J., Rathi, V., Midelfort, L., DiJulio, S., and Stanionis, M., (2016). M+R Benchmarks 2016. https://www.mrbenchmarks.com

  8. Cardoso, M., Warrick, E., Golbeck, J., & Preece, J. (2016). Motivational impact of Facebook posts on environmental communities. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (pp. 237–240). New York, NY: ACM.

  9. Carpenter, J. P., & Krutka, D. G. (2014). How and why educators use twitter: a survey of the field. Journal of Research on Technology in Education, 46(4), 414–434. https://doi.org/10.1080/15391523.2014.925701.

    Article  Google Scholar 

  10. Carpenter, J. P., & Krutka, D. G. (2015). Engagement through microblogging: educator professional development via Twitter. Professional Development in Education, 41(4), 707–728. https://doi.org/10.1080/19415257.2014.939294.

    Article  Google Scholar 

  11. Charon, J. M. (2009). Symbolic interactionism: an introduction, an interpretation, an integration. Upper Saddle River, N.J: Pearson Education.

    Google Scholar 

  12. Catalani, J. (2014). Contributions by amateur paleontologists in 21st century paleontology. Palaeontologia Electronica, 17(2) https://doi.org/10.26879/143.

  13. Collins, K., Shiffman, D., & Rock, J. (2016). How are scientists using social media in the workplace? PLoS One, 11(10), e0162680. https://doi.org/10.1371/journal.pone.0162680.

    Article  Google Scholar 

  14. Côté, I. M., & Darling, E. 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.

    Article  Google Scholar 

  15. Crable, B. (2009). Symbolic interactionism. In S. W. Littlejohn & K. A. Foss (Eds.), Encyclopedia of communication theory (pp. 946–948). Thousand Oaks, CA: SAGE Publications, Inc..

    Google Scholar 

  16. Crippen, K. J., Ellis, S., Dunckel, B. A., Hendy, A. J. W., & MacFadden, B. J. (2016). Seeking shared practice: A juxtaposition of the attributes and activities of organized fossil groups with those of professional paleontology. Journal of Science Education and Technology, 25(5), 731–746. https://doi.org/10.1007/s10956-016-9627-3

    Article  Google Scholar 

  17. Crossley, N. (2011). Networks and complexity: directions for interactionist research? Symbolic Interaction, 33(3), 341–363. https://doi.org/10.1525/si.2010.33.3.341.

    Article  Google Scholar 

  18. Daume, S., & Galaz, V. (2016). “Anyone know what species this is?”—Twitter conversations as embryonic citizen science communities. PLoS One, 11(3), e0151387. https://doi.org/10.1371/journal.pone.0151387.

    Article  Google Scholar 

  19. Drotner, K., & Schrøder, K. (2013). Museum communication and social media. New York: Routledge.

  20. Essex, J., & Haxton, K. (2018). Characterising patterns of engagement of different participants in a public STEM-based analysis project. International Journal of Science Education, Part B, 8(2), 178–191. https://doi.org/10.1080/21548455.2017.1423128.

    Article  Google Scholar 

  21. Falk, J. H., & Dierking, L. D. (2013). The museum experience revisited. Walnut Creek, Calif: Left Coast Press, Inc..

    Google Scholar 

  22. Falk, J. H., & Storksdieck, M. (2010). Science learning in a leisure setting. Journal of Research in Science Teaching, 47(2), 194–212. https://doi.org/10.1002/tea.20319.

    Article  Google Scholar 

  23. Fauville, G., Dupont, S., von Thun, S., & Lundin, J. (2015). Can Facebook be used to increase scientific literacy? A case study of the Monterey Bay Aquarium Research Institute Facebook page and ocean literacy. Computers & Education, 82, 60–73. https://doi.org/10.1016/j.compedu.2014.11.003.

    Article  Google Scholar 

  24. Fauville, G. (2017). Questions as indicators of ocean literacy: students’ online asynchronous discussion with a marine scientist. International Journal of Science Education, 39(16), 1–20. https://doi.org/10.1080/09500693.2017.1365184.

    Article  Google Scholar 

  25. Gabriel, K. R. (1969). Simultaneous test procedures—some theory of multiple comparisons. Ann. Math. Statist., 40(1), 224–250. https://doi.org/10.1214/aoms/1177697819.

    Article  Google Scholar 

  26. Gerrard, D., Sykora, M., & Jackson, T. (2017). Social media analytics in museums: extracting expressions of inspiration. Museum Management and Curatorship, 32(3), 232–250. https://doi.org/10.1080/09647775.2017.1302815.

    Article  Google Scholar 

  27. Gibson, J. J. (1986). The ecological approach to visual perception. Hillsdale, N.J: Lawrence Erlbaum Associates.

    Google Scholar 

  28. Hargittai, E., Füchslin, T., & Schäfer, M. S. (2018). How do young adults engage with science and research on social media? Some preliminary findings and an agenda for future research. Social Media + Society, 4(3), 1–10. https://doi.org/10.1177/2056305118797720.

    Article  Google Scholar 

  29. Hayes, R. A., Carr, C. T., & Wohn, D. Y. (2016). It’s the audience: differences in social support across social media. Social Media + Society, 2(4), 1–12. https://doi.org/10.1177/2056305116678894.

    Article  Google Scholar 

  30. Hines, H., & Warring, S. (2019). How we use Instagram to communicate microbiology to the public. Nature. https://doi.org/10.1038/d41586-019-00493-3.

  31. Hoban, G., Nielsen, W., & Shepherd, A. (2015). Student-generated digital media in science education: learning, explaining and communicating content. London: Routledge.

    Google Scholar 

  32. Hwong, Y.-L., Oliver, C., Van Kranendonk, M., Sammut, C., & Seroussi, Y. (2017). What makes you tick? The psychology of social media engagement in space science communication. Computers in Human Behavior, 68, 480–492. https://doi.org/10.1016/j.chb.2016.11.068.

    Article  Google Scholar 

  33. IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.

  34. Krippendorff, K. (2012). Content analysis: an introduction to its methodology. Los Angeles: SAGE Publications, Inc..

    Google Scholar 

  35. Lam, A.R., Bauer, J.E., Borden, R.M., Fraass, S., Fraass, A., Hartshorn, K., Hils, J. M., Limbeck, M. R., Thompson-Munson, M., Muskelly, C. O., & Sheffield, S. L. (2019). Making sense of climate change and evolution in a digital age: communicating science to the public through blogs, web pages, and social media platforms. Journal of STEM Outreach, 2(1), https://doi.org/10.15695/jstem/v2i1.05.

  36. Lewis, S., Pea, R., & Rosen, J. (2010). Beyond participation to co-creation of meaning: mobile social media in generative learning communities. Social Science Information, 49(3), 351–369. https://doi.org/10.1177/0539018410370726.

    Article  Google Scholar 

  37. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Los Angeles: SAGE Publications, Inc..

    Google Scholar 

  38. Littlejohn, S. W., & Foss, K. A. (2011). Theories of human communication (10th ed.). Long Grove, Ill: Waveland Press.

    Google Scholar 

  39. Lundgren, L., & Crippen, K. J. (2017). Developing social paleontology: A case study implementing innovative social media applications. In D. Remenyl (Ed.), The Social Media in Practice Excellence Awards 2017 at ECSM 2017: An Anthology of Case Histories (pp. 11–26). Reading, UK: Academic Conferences and Publishing International Limited (ACPIL).

  40. Lundgren, L. M., & Crippen, K. J. (2019). Learning and the practice of social media in informal science education centers. International Journal of E-Learning, 18(1), 31–52. http://www.learntechlib.org/p/181959/

  41. Lundgren, L., Crippen, K. J., Gardner, E. E., Perez, V., & Leder, R. M. (2018). Mental models and social media personas: A case of amateur palaeontologists. International Journal of Social Media and Interactive Learning Environments, 6(1), 44–69. https://doi.org/10.1504/IJSMILE.2018.092374.

    Article  Google Scholar 

  42. Marsh, O. M. (2018). “Nah, musing is fine. You don’t have to be ‘doing science’”: emotional and descriptive meaning-making in online non-professional discussions about science (Doctoral thesis, University College London, London, England). Retrieved from http://discovery.ucl.ac.uk/10044289/

  43. Martin, A. J., Durksen, T. L., Williamson, D., Kiss, J., & Ginns, P. (2016). The role of a museum-based science education program in promoting content knowledge and science motivation. Journal of Research in Science Teaching, 53(9), 1364–1384. https://doi.org/10.1002/tea.21332.

    Article  Google Scholar 

  44. McClain, C. R. (2017). Practices and promises of Facebook for science outreach: becoming a “nerd of trust”. PLoS Biology, 15(6), e2002020. https://doi.org/10.1371/journal.pbio.2002020.

    Article  Google Scholar 

  45. Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: accountable talk in the classroom and in civic life. Studies in Philosophy and Education, 27(4), 283–297. https://doi.org/10.1007/s11217-007-9071-1.

    Article  Google Scholar 

  46. Mortimer, E., & Scott, P. (2003). Meaning making in secondary science classrooms. Maidenhead, Berkshire: Open University Press.

    Google Scholar 

  47. Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. C. (2011). Bad news travel fast: a content-based analysis of interestingness on Twitter. Proceedings of the 3rd International Web Science Conference on - WebSci ’11 (pp. 1–7). New York, New York, USA: ACM Press. https://doi.org/10.1145/2527031.2527052.

  48. Perrin, A. (2015). Social media usage: 2005-2015. (A. Smith & D. Page, Eds.). Washington, D.C.: Pew Research Center.

  49. Petrovic, S., Osborne, M., & Lavrenko, V. (2011). RT to win! Predicting message propagation in Twitter. In N. Nicolov & J. G. Shanahan (Eds.), Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (pp. 586–589). Barcelona, Catalonia: AAAI Press.

    Google Scholar 

  50. Russo, A., Watkins, J., & Groundwater-Smith, S. (2009). The impact of social media on informal learning in museums. Educational Media International, 46(2), 153–166. https://doi.org/10.1080/09523980902933532.

    Article  Google Scholar 

  51. Shea, N. A. (2015). Examining the nexus of science communication and science education: a content analysis of genetics news articles. Journal of Research in Science Teaching, 52(3), 397–409. https://doi.org/10.1002/tea.21193.

    Article  Google Scholar 

  52. Shuai, X., Pepe, A., & Bollen, J. (2012). How the scientific community reacts to newly submitted preprints: article downloads, Twitter mentions, and citations. PLoS One, 7(11), e47523. https://doi.org/10.1371/journal.pone.0047523.

    Article  Google Scholar 

  53. Stryker, S., & Vryan, K. D. (2003). The symbolic interactionist frame. In J. D. Delamater (Ed.), Handbook of social psychology. New York, N.Y: Springer.

    Google Scholar 

  54. Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. Proceedings of the 2010 IEEE Second International Conference on Social Computing (pp. 177–184). IEEE. https://doi.org/10.1109/SocialCom.2010.33.

  55. Sun, N., Rau, P. P.-L., & Ma, L. (2014). Understanding lurkers in online communities: a literature review. Computers in Human Behavior, 38, 110–117. https://doi.org/10.1016/j.chb.2014.05.022.

    Article  Google Scholar 

  56. Twitchett, R. J., Scriven, S., Kerr, G., & Hughes, Z. (2017). Citizen science, public engagement and geoconservation in the jurassic coast world hertiage site of Southern England. In Geological Society of America Abstracts with Programs (Vol. 49, p. 6). Seattle, WA: Geological Society of America.

  57. van Mierlo, T. (2014). The 1% rule in four digital health social networks: an observational study. Journal of Medical Internet Research, 16(2), e33. https://doi.org/10.2196/jmir.2966.

    Article  Google Scholar 

  58. Vaynerchuk, G. (2013). Jab, jab, jab, right hook. New York: HarperBusiness.

    Google Scholar 

  59. Visser, R. D., Evering, L. C., & Barrett, D. E. (2014). #TwitterforTeachers: the implications of Twitter as a self-directed professional development tool for K–12 teachers. Journal of Research on Technology in Education, 46(4), 396–413. https://doi.org/10.1080/15391523.2014.925694.

    Article  Google Scholar 

  60. Warren, S. J. (2016). The twitter academic: supporting learning communications in 140 characters or less. International Journal of Social Media and Interactive Learning Environments, 4(1), 1. https://doi.org/10.1504/IJSMILE.2016.075052.

    Article  Google Scholar 

  61. Welbourne, D. J., & Grant, W. J. (2016). Science communication on YouTube: factors that affect channel and video popularity. Public Understanding of Science, 25(6), 706–718. https://doi.org/10.1177/0963662515572068.

    Article  Google Scholar 

  62. Wenger, E., White, N., & Smith, J. D. (2009). Digital habitats: stewarding technology for communities. Portland, OR: CPsquare.

    Google Scholar 

  63. Zhao, X., Lampe, C., & Ellison, N. B. (2016). The social media ecology: user perceptions, strategies and challenges. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI ’16 (pp. 89–100). New York, New York, USA: ACM Press. https://doi.org/10.1145/2858036.2858333.

  64. Zittoun, T. & Brinkmann, S. (2012). Learning as meaning making. In N.M. Seel (Eds.), Encyclopedia of the sciences of learning.Boston, MA: Springer.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. (1322725). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. We thank the undergraduate intern who initially helped with coding of posts in 2017 as well as the three undergraduate interns who assisted with coding posts as part of their undergraduate research project in spring 2018.

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Correspondence to Kent J. Crippen.

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Lundgren, L., Crippen, K.J. & Bex, R.T. Social Media Interaction as Informal Science Learning: a Comparison of Message Design in Two Niches. Res Sci Educ (2020). https://doi.org/10.1007/s11165-019-09911-y

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Keywords

  • Social media
  • Informal science learning
  • Twitter
  • Facebook
  • Content analysis