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TechTrends

, Volume 62, Issue 5, pp 501–508 | Cite as

Tweet, and We Shall Find: Using Digital Methods to Locate Participants in Educational Hashtags

  • Spencer P. Greenhalgh
  • K. Bret Staudt Willet
  • Joshua M. Rosenberg
  • Matthew J. Koehler
Original Paper

Abstract

Although researchers have discovered a great deal about who uses Twitter for educational purposes, what they post about, when they post and why they participate, there has so far been little work to explore where participants in educational Twitter contexts are located. In this paper, we establish a methodological foundation that can support the exploration of geographical issues in educational Twitter research. We surveyed 46 participants in one educational Twitter hashtag, #michED, to determine where they lived; we then compared these responses to results from three digital methods for geolocating Twitter users (human coding, machine coding and GPS coding) to explore these methods’ affordances and constraints. Human coding of Twitter profiles allowed us to analyze more participants with higher levels of accuracy but also has disadvantages compared to other digital—and traditional—methods. We discuss the additional insights obtained through geolocating #michED participants as well as considerations for using geolocation and other digital methods in educational research.

Keywords

Digital methods Geolocation Educational hashtags Hashtag Social media Twitter 

Notes

Acknowledgements

We would like to thank Ben Rimes, Mary Wever and everyone else who helped us reach out to the #michED community.

Compliance with Ethical Standards

Conflict of Interest

Spencer P. Greenhalgh declares that he has no conflict of interest. K. Bret Staudt Willet declares that he has no conflict of interest. Joshua M. Rosenberg declares that he has no conflict of interest. Matthew J. Koehler declares that he has no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Baker, L. M. (2008). Unobtrusive research. In L. M. Given (Ed.), The SAGE encyclopedia of qualitative research methods (pp. 905–906). Thousand Oaks: SAGE.Google Scholar
  2. 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, 414–434.  https://doi.org/10.1080/15391523.2014.925701.
  3. Carpenter, J. P., & Krutka, D. G. (2015). Engagement through microblogging: Educator professional development via Twitter. Professional Development in Education, 41, 707–728.  https://doi.org/10.1080/19415257.2014.939294.
  4. Carpenter, J. P., Tani, T., Morrison, S., & Keane, J. (2018). Exploring the education Twitter hashtag landscape. In E. Langran & J. Borup (Eds.), Proceedings of Society for Information Technology and Teacher Education Conference 2018 (pp. 2230–3325). Waynesville: Association for the Advancement of computing in Education (AACE).Google Scholar
  5. Cheng, Z., Caverlee, J., & Lee, K. (2010). You are where you tweet: A content-based approach to geo-locating Twitter users. In X. J. Huang, G. Jones, N. Koudas, X. Wu, & K. Collins-Thompson (Eds.), Proceedings of the 19 th ACM international conference on information and knowledge management (pp. 759–768). New York: Association for Computing Machinery.Google Scholar
  6. Fiesler, C., & Proferes, N. (2018). “Participant” perceptions of Twitter research ethics. Social Media + Society, 4(2).  https://doi.org/10.1177/2056305118763366.
  7. Gao, F., & Li, L. (2017). Examining a one-hour synchronous chat in a microblogging-based professional development community. British Journal of Educational Technology, 48, 332–347.  https://doi.org/10.1111/bjet.12384.CrossRefGoogle Scholar
  8. Gentry, J. (2015). twitteR: R based Twitter client (Version 1.1.9) [Computer Software]. Retrieved from http://CRAN.R-project.org/package=twitteR
  9. Gleason, B. (2013). #Occupy Wall Street: Exploring informal learning about a social movement on Twitter. American Behavioral Scientist, 57, 966–982.  https://doi.org/10.1177/0002764213479372.
  10. Graham, M., Hale, S. A., & Gaffney, D. (2014). Where in the world are you? Geolocation and language identification in Twitter. The Professional Geographer, 66, 568–578.  https://doi.org/10.1080/00330124.2014.907699.
  11. Greenhalgh, S. P., & Koehler, M. J. (2017). 28 days later: Twitter hashtags as “just in time” teacher professional development. TechTrends, 61, 273–281.  https://doi.org/10.1007/s11528-016-0142-4.CrossRefGoogle Scholar
  12. Hawksey, M. (2014). Need a better Twitter Archiving Google Sheet? TAGS v6.0 is here! [Blog post]. Retrieved from https://mashe.hawksey.info/2014/10/need-a-better-twitter-archiving-google-sheet-tags-v6-0-is-here/
  13. Jurgens, D., Finnethy, T., McCorriston, J., Xu, Y. T., & Ruths, D. (2015). Geolocation prediction in Twitter using social networks: A critical analysis and review of current practice. In Proceedings of the twenty-ninth AAAI conference on artificial intelligence and the twenty-seventh innovative applications of artificial intelligence conference. Palo Alto: Association for the Advancement of Artificial Intelligence.Google Scholar
  14. Krutka, D. G., Asino, T. I., & Haselwood, S. (2018). Editorial: Eight lessons on networked teacher activism from #OklaEd and the #OklaEdWalkout. Contemporary Issues in Technology and Teacher Education, 18(2) Retrieved from http://www.citejournal.org.
  15. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.CrossRefGoogle Scholar
  16. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., et al. (2009). Computational social science. Science, 323, 721–723.  https://doi.org/10.1126/science.1167742.CrossRefGoogle Scholar
  17. Lee, R. M. (2015). Unobtrusive measures.  https://doi.org/10.1093/OBO/9780199846740-0048.Google Scholar
  18. Lee, R. M., Fielding, N., & Blank, G. (2008). The internet as a research medium: An editorial introduction to The Sage Handbook of Online Research Methods. In N. Fielding, R. M. Lee, & G. Blank (Eds.), The SAGE handbook of online research methods (pp. 2–22). Thousand Oaks: SAGE.CrossRefGoogle Scholar
  19. Luo, T., & Clifton, L. (2017). Examining collaborative knowledge construction in microblogging-based learning environments. Journal of Information Technology Education: Research, 16, 365–390.CrossRefGoogle Scholar
  20. Markham, A., & Buchanan, E. (2012). Ethical decision-making and internet research: Recommendations from the AoIR ethics working committee (Version 2.0). Chicago: Association of Internet Researchers.Google Scholar
  21. Marres, N. (2016). Foreword. In H. Snee, C. Hine, Y. Morey, S. Roberts, & H. Watson (Eds.), Digital methods for social science: An interdisciplinary guide to research innovation (pp. viii–viix). New York: Palgrave Macmillan.Google Scholar
  22. Mishra, P., Koehler, M. J., & Greenhow, C. (2016). The work of educational psychologists in a digitally networked world. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 29–40). New York: Routledge.Google Scholar
  23. Munzert, S., Rubba, C., Meißner, P., & Nyhuis, D. (2015). Automated data collection with R: A practical guide to web scraping and text mining. West Sussex: Wiley.Google Scholar
  24. Rosenberg, J. M., Greenhalgh, S. P., Koehler, M. J., Akcaoglu, M., & Hamilton, E. (2016). An investigation of State Educational Twitter Hashtags (SETHs) as affinity spaces. E-Learning and Digital Media, 13, 24–44.  https://doi.org/10.1177/2042753016672351.
  25. Rosenberg, J. M., Akcaoglu, M., Staudt Willet, K. B., Greenhalgh, S. P., & Koehler, M. J. (2017). A tale of two Twitters: Synchronous and asynchronous use of the same hashtag. In P. Resta & S. Smith (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2017 (pp. 283–286). Waynesville: Association for the Advancement of computing in education (AACE).Google Scholar
  26. Rudis, B. (2016). nominatim: Tools for working with the “Nominatim” API. (Version 0.2.2.9000) [Computer Software]. Retrieved from https://github.com/hrbrmstr/nominatim
  27. Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton: Princeton University Press.Google Scholar
  28. Shaffer, D. W. (2017). Quantitative ethnography. Madison: Cathcart Press.Google Scholar
  29. Sloan, L. (2017). Social science ‘lite'? Deriving demographic proxies from Twitter. In L. Sloan & A. Quan-Haase (Eds.), The SAGE handbook of social media research methods (pp. 90–104). London: SAGE.Google Scholar
  30. Snee, H., Hine, C., Morey, Y., Roberts, S., & Watson, H. (2016). Digital methods as mainstream methodology: An introduction. In H. Snee, C. Hine, Y. Morey, S. Roberts, & H. Watson (Eds.), Digital methods for social science: An interdisciplinary guide to research innovation (pp. 1–11). New York: Palgrave Macmillan.Google Scholar
  31. Takhteyev, Y., Gruzd, A., & Wellman, B. (2012). Geography of Twitter networks. Social Networks, 34, 73–81.  https://doi.org/10.1016/j.socnet.2011.05.006.
  32. Veletsianos, G. (2017a). Three cases of hashtags used as learning and professional development environments. TechTrends, 61, 284–292.  https://doi.org/10.1007/s11528-016-0143-3.CrossRefGoogle Scholar
  33. Veletsianos, G. (2017b). Toward a generalizable understanding of Twitter and social media use across MOOCs: Who participates on MOOC hashtags and in what ways? Journal of Computing in Higher Education, 29, 65–80.  https://doi.org/10.1007/s12528-017-9131-7.
  34. Welser, H. T., Smith, M., Fisher, D., & Gleave, E. (2008). Distilling digital traces: Computational social science approaches to studying the internet. In N. Fielding, R. M. Lee, & G. Blank, The SAGE handbook of online research methods (pp. 116–141). Thousand Oaks: SAGE.Google Scholar
  35. Wesely, P. M. (2013). Investigating the community of practice of world language educators on Twitter. Journal of Teacher Education, 64, 305–318.  https://doi.org/10.1177/0022487113489032.

Copyright information

© Association for Educational Communications & Technology 2018

Authors and Affiliations

  1. 1.School of Information ScienceUniversity of KentuckyLexingtonUSA
  2. 2.Department of Counseling, Educational Psychology and Special EducationMichigan State UniversityEast LansingUSA
  3. 3.Department of Theory and Practice in Teacher EducationUniversity of Tennessee, KnoxvilleKnoxvilleUSA
  4. 4.Department of Counseling, Educational Psychology and Special EducationMichigan State UniversityEast LansingUSA

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