Policy insight through social and online media

A systematic mapping of qualitative passive citizensourcing studies

Authors

  • Kane Callaghan Charles Sturt University
  • Michael Mehmet University of Wollongong
  • Peter Simmons Charles Sturt University
  • Belinda Curley New South Wales Department of Primary Industries

Keywords:

Systematic mapping, Qualitative research, Online comments, Citizensourcing, Public policy, Social media

Abstract

This systematic mapping study examines how and why researchers conduct qualitative studies of online citizen commentary on public policy matters, and identifies procedural commonalities and differences. Findings indicate that researchers typically: choose online comments to help understand public discussion and salient attitudes to important policy matters; believe online citizen commentary can give insights into community attitudes because it is unsolicited and relatively anonymous and unconstrained; believe anonymity is the main disadvantage because it can be difficult to ascertain who is commenting or which groups are represented; justify their choice of source data sites by preferring those with ready access to a large readership or large numbers of comments; manually ‘copy and paste’ online comments from one or more sites, mostly news media websites; use thematic analysis to identify emergent attitudes and influences; and either omit discussion of ethical issues or assert that because the data is freely accessible to the public, it is open to use without ethics permission. Recommendations include: more specificity in research areas and questions; greater use of multiple and diverse source types; less use of frequency and description in analysis and more use of theory and complex analysis; and integration of ethical considerations into research design.

References

Androutsopoulou, A., Charalabidis, Y., & Loukis, E. (2018). Policy Informatics in the Social Media Era: Analyzing Opinions for Policy Making, Cham.

Bossetta, M. (2018). The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election. Journalism & Mass Communication Quarterly, 95(2), 471-496. doi:10.1177/1077699018763307

Burnap, P., Rana, O. F., Avis, N., Williams, M., Housley, W., Edwards, A., . . . Sloan, L. (2015). Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change, 95, 96-108. doi:https://doi.org/10.1016/j.techfore.2013.04.013

Charalabidis, Y., Loukis, E., Androutsopoulou, A., Karkaletsis, V., & Triantafillou, A. (2014). Passive crowdsourcing in government using social media. Transforming Government: People, Process and Policy, 8(2), 283-308. doi:doi:10.1108/TG-09-2013-0035

Cooper, I. D. (2016). What is a "mapping study?". Journal of the Medical Library Association : JMLA, 104(1), 76-78. doi:10.3163/1536-5050.104.1.013

Franco, M., Tursunbayeva, A., & Pagliari, C. (2016). Social media for e-Government in the public health sector: protocol for a systematic review. JMIR research protocols, 5(1), e42.

Ghermandi, A., & Sinclair, M. (2019). Passive crowdsourcing of social media in environmental research: A systematic map. Global Environmental Change, 55, 36-47. doi:https://doi.org/10.1016/j.gloenvcha.2019.02.003

Grover, P., Kar, A. K., Dwivedi, Y. K., & Janssen, M. (2019). Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences. Technological Forecasting and Social Change, 145, 438-460. doi:https://doi.org/10.1016/j.techfore.2018.09.009

Guajardo Leal, B. E., Navarro-Corona, C., & Valenzuela González, J. R. (2019). Systematic Mapping Study of Academic Engagement in MOOC. International Review of Research in Open and Distributed Learning, 20(2). doi:https://doi.org/10.19173/irrodl.v20i2.4018

Head, B. W. (2013). Evidence-Based Policymaking – Speaking Truth to Power? Australian Journal of Public Administration, 72(4), 397-403. doi:10.1111/1467-8500.12037

Jalali, S., & Wohlin, C. (2012). Systematic literature studies: database searches vs. backward snowballing. Paper presented at the Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement, Lund, Sweden.

Jaspal, R., Nerlich, B., & Koteyko, N. (2012). Contesting Science by Appealing to Its Norms: Readers Discuss Climate Science in the Daily Mail. Science Communication, 35(3), 383-410. doi:10.1177/1075547012459274

Kaplan, S., & Prato, C. G. (2016). “Them or Us”: Perceptions, cognitions, emotions, and overt behavior associated with cyclists and motorists sharing the road. International Journal of Sustainable Transportation, 10(3), 193-200. doi:10.1080/15568318.2014.885621

Keelan, J., Pavri, V., Balakrishnan, R., & Wilson, K. (2010). An analysis of the Human Papilloma Virus vaccine debate on MySpace blogs. Vaccine, 28(6), 1535-1540. doi:https://doi.org/10.1016/j.vaccine.2009.11.060

Kozinets, R. V. (2019). Netnography: The Essential Guide to Qualitative Social Media Research: SAGE Publications.

Mandelzis, L., Bernstein, A., & Ringel, S. (2017). Critical reading of talkbacks discourse as separatist integration: on the Israeli 2004 Football Cup victory. Israel Affairs, 23(5), 934-950. doi:10.1080/13537121.2017.1345418

Marino, V., & Presti, L. L. (2018). From citizens to partners: the role of social media content in fostering citizen engagement. Transforming Government: People, Process and Policy.

McKenna, B., Myers, M. D., & Newman, M. (2017). Social media in qualitative research: Challenges and recommendations. Information and Organization, 27(2), 87-99. doi:https://doi.org/10.1016/j.infoandorg.2017.03.001

Mehmet, M. I., D'Alessandro, S., Pawsey, N., & Nayeem, T. (2018). The national, regional and city divide: Social media analysis of stakeholders views regarding biological controls. The public reaction to the carp control herpes virus in Australia. Journal of Environmental Management, 227, 181-188. doi:https://doi.org/10.1016/j.jenvman.2018.08.093

Natow, R. S. (2019). Online Qualitative Data Sources for Federal Regulatory Policy Studies. American Behavioral Scientist, 63(3), 315-332. doi:10.1177/0002764218820567

O'Cathain, A., Thomas, K. J., Drabble, S. J., Rudolph, A., & Hewison, J. (2013). What can qualitative research do for randomised controlled trials? A systematic mapping review. BMJ Open, 3(6), e002889. doi:10.1136/bmjopen-2013-002889

Panagiotopoulos, P., Bowen, F., & Brooker, P. (2017). The value of social media data: Integrating crowd capabilities in evidence-based policy. Government Information Quarterly, 34(4), 601-612. doi:https://doi.org/10.1016/j.giq.2017.10.009

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.

Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1-18. doi:https://doi.org/10.1016/j.infsof.2015.03.007

Saldana, J. (2016). The Coding Manual for Qualitative Researchers: SAGE Publications.

Simmons, P., & Mehmet, M. I. (2018). Shark management strategy policy considerations: Community preferences, reasoning and speculations. Marine Policy, 96, 111-119. doi:https://doi.org/10.1016/j.marpol.2018.08.010

Taeihagh, A. (2017). Crowdsourcing: a new tool for policy-making? Policy Sciences, 50(4), 629-647. doi:10.1007/s11077-017-9303-3

Templier, M., & Paré, G. (2018). Transparency in literature reviews: an assessment of reporting practices across review types and genres in top IS journals. European Journal of Information Systems, 27(5), 503-550. doi:10.1080/0960085X.2017.1398880

Tengku Izhar, T. A. (2018). A Framework-based Mapping and Filtering for Social Media. 2018, 7(1), 18. Retrieved from https://www.thejsms.org/index.php/TSMRI/article/view/175/178

Tursunbayeva, A., Franco, M., & Pagliari, C. (2017). Use of social media for e-Government in the public health sector: A systematic review of published studies. Government Information Quarterly, 34(2), 270-282.

Walker, M., King, G. A., & Hartman, L. R. (2018). Exploring the Potential of Social Media Platforms as Data Collection Methods for Accessing and Understanding Experiences of Youth with Disabilities: A Narrative Review. 2018, 7(2), 26. Retrieved from https://www.thejsms.org/index.php/TSMRI/article/view/393

Waterloo, S. F., Baumgartner, S. E., Peter, J., & Valkenburg, P. M. (2017). Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. New Media & Society, 20(5), 1813-1831. doi:10.1177/1461444817707349

White, P. (2017). Developing Research Questions: Palgrave.

Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2017). Researching mental health disorders in the era of social media: systematic review. Journal of medical Internet research, 19(6), e228.

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Published

2021-12-30

Issue

Vol. 10 No. 2 (2021): The Journal of Social Media in Society

Section

Articles

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