Person or PC? A Comparison of Human and Computer Coding as Content Analyses Tools Evaluating Severe Weather

Cory L. Armstrong 1 * , Nathan A. Towery 2
More Detail
1 University of Alabama, USA
2 Jackson State University, USA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 12, Issue 2, Article No: e202211.
OPEN ACCESS   493 Views   256 Downloads   Published online: 18 Jan 2022
Download Full Text (PDF)


Computer-aided content analyses programs have been deployed for social science research in recent years; however, few studies have evaluated their effectiveness, compared to human coding. This study uses open-ended responses from respondents seeking information in preparation for Hurricane Michael to compare human- and computer-coding. In particular, the comparison involves the use of Excel as a common and relatively simple coding instrument. Results indicated significant differences between frequencies coded by humans and a computer, with additional findings suggesting that residents employ television as a tool for information gathering when severe weather is imminent. Final discussion focuses on support for a blended model of both human and computer coding, while examining the findings related to severe weather.


Armstrong, C. L., & Towery, N. A. (2022). Person or PC? A Comparison of Human and Computer Coding as Content Analyses Tools Evaluating Severe Weather. Online Journal of Communication and Media Technologies, 12(2), e202211.


  • Alam, F., Ofli, F., & Imran, M. (2018). Processing social media images by combining human and machine computing during crises. International Journal of Human-Computer Interaction, 34(4), 311-327.
  • Armstrong, C. L., & Towery, N. (2021). Before and after the storm: How individuals hypothetically and realistically respond to media messages about severe weather. International Journal of Disaster Response and Emergency Management, 4(1), 46-62.
  • Armstrong, C. L., Hou. J., & Towery, N. (2020). The ‘Michael’ effect: Risk perception and behavioral intentions through varying lenses. Journal of Extreme Events, 1&2, 2050007-1-22.
  • Ash, K. D., Schumann, R. L., & Bowser, G. C. (2014). Tornado warning trade-offs: Evaluating choices for visually communicating risk. Weather, Climate, and Society, 6(1), 104-118.
  • Baker, E. J. (1991). Hurricane evacuation behavior. International Journal of Mass Emergencies, 9(2), 287-310.
  • Boumans, J. W., & Trilling, D. (2016). Taking stock of the toolkit. Digital Journalism, 4(1), 8-23,
  • Daniels, G. L., & Loggins, G. M. (2007). Conceptualizing continuous coverage: A strategic model for wall-to-wall local television weather broadcasts. Journal of Applied Communication Research, 35(1), 48-66.
  • Griffin, R. J., Dunwoody, S., & Neuwirth, K. (1999). Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environmental Research, 80, S230-S245.
  • Griffin, R. J., Neuwirth, K., Dunwoody, S., & Giese, J. (2004). Information sufficiency and risk communication. Media Psychology, 6, 23-61.
  • Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling. Journalism & Mass Communication Quarterly, 93(2), 332-359.
  • Kang, J. E., Lindell, M. K., & Prater, C. S. (2007). Hurricane evacuation expectations and actual behavior in Hurricane Lili. Journal of Applied Social Psychology, 37(4), 887-903.
  • Krippendorff, K. (2004). Content analysis: An introduction to its methodology. SAGE.
  • Landsea, C. W., & Franklin, J. L. (2013). Atlantic Hurricane database uncertainty and presentation of a new database format. Monthly Weather Review, 141(10), 3576-3592.
  • Landwehr, P. M., Wei, W., Kowalchuck, M., & Carley, K. M. (2016). Using tweets to support disaster planning, warning and response. Safety Science, 90, 33-47.
  • Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods, Journal of Broadcasting & Electronic Media, 57(1), 34-52.
  • Linardi, S. (2016). Peer coordination and communication following disaster warnings: An experimental framework. Safety Science, 90, 24-32.
  • Mileti, D. S., & O’Brien, P. W. (1992). Warnings during disaster: Normalizing communicated risk. Social Problems, 39(1), 40-57.
  • Mileti, D. S., & Sorensen, J. H. (1987). Determinants of organizational-effectiveness in responding to low probability catastrophic events. Columbia Journal of World Business, 22(1), 13-21.
  • Moore, T. W., & Dixon, R. W. (2011). Tropical cyclone-tornado casualties. Natural Hazards, 61(2), 621-634.
  • Morss, R. E., Demuth, J. L., Lazo, J. K., Dickinson, K., Lazrus, H., & Morrow, B. H. (2016). Understanding public hurricane evacuation decisions and responses to forecast and warning messages. Weather Forecasting, 31, 395-417.
  • Riffe, D., Lacy, S., & Fico, F. (2014). Analyzing media messages. Routledge.
  • Sattler, D. N., Kaiser, C. F., & Hittner, J. B. (2000). Disaster preparedness: Relationships among prior experience, personal characteristics, and distress. Journal of Applied Social Psychology, 30(7), 1396-1420.
  • Sheldon, P., & Antony, M. G. (2018). Sharing emergency alerts on a college campus: How gender and technology matter. Southern Communication Journal, 83(3), 167-178.
  • Su, L. Y., Cacciatore, M. A., Liang, X., Broussard, D., Scheufele, D. A., & Xenos, M. A. (2016). Analyzing public sentiments online: Combining human- and computer-based content analysis. Information, Communication & Society, 20(3), 406-427.
  • Wachinger, G., Renn, O., Begg, C., & Kuhlicke, C. (2013). The risk perception paradox-implications for governance and communication of natural hazards. Risk Analysis, 33(6), 1049-1065.
  • Wu, H., Lindell, M. K., Prater, C. S., & Samuelson, C. D. (2014). Effects of track and threat information on judgments of hurricane strike probability. Risk Analysis, 34(6), 1025-1039.
  • Zahran, S., Tavani, D., & Weiler, S. (2013). Daily variation in natural disaster casualties: Information flows, safety, and opportunity costs in tornado versus hurricane strikes. Risk Analysis, 33(7), 1265-1280.