AI & Big Data

"Big Data" are data characterized by one or more of three Vs: large in Volume, generated continuously with high Velocity, and Varied in structure or lack thereof. (Laney, 2001)

Relevant Publications

  • Majdik, Z. P., Graham, S. S., Shiva Edward, J. C., Rodriguez, S. N., Karnes, M. S., Jensen, J. T., Barbour, J. B., & Rousseau, J. F. (2024). Sample size considerations for fine-tuning large language models for named entity recognition tasks: Methodological study. JMIR AI, 3, e52095. https://doi.org/10.2196/52095

  • Majdik, Z. P., & Graham, S. S. (2024). Rhetoric of/with AI: An introduction. Rhetoric Society Quarterly, 54(3), 222–231. https://doi.org/10.1080/02773945.2024.2343264

  • Graham, S. S., Harrison, K. R., Edward, J. C. S., Majdik, Z. P., Barbour, J. B., & Rousseau, J. F. (2024). Beyond bias: Aggregate approaches to conflicts of interest research and policy in biomedical research. World Medical & Health Policy. https://doi.org/10.1002/wmh3.608

  • Majdik, Z. P., & Wynn, J. (2023). Building better machine learning models for rhetorical analyses: The use of rhetorical feature sets for training artificial neural network models. Technical Communication Quarterly 32(1), 63–78. https://doi.org/10.1080/10572252.2022.2077452
     
  • Lu, S., & Lin, T. M. (2022). Revisiting the nexus of Internet and political participation: A longitudinal study of environmental petition in China. Journal of Information Technology & Politics, 19(3), 346-359.https://www.tandfonline.com/doi/full/10.1080/19331681.2021.1997866
     

  • Lu, S. (2022). News technology innovation as a field: A structural topic modeling analysis of patent data in mainland China. Communication & Society, 59, 147-175. http://cschinese.com/issueArticle.asp?P_No=90&CA_ID=688

  • Luqiu, L. R., & Lu, S. (2021). Bounded or boundless: A case study of foreign correspondents’ use of Twitter during the 2019 Hong Kong protests. Social Media + Society, 7(1). https://doi.org/10.1177/2056305121990637

  • Majdik, Z. P. (2021). Five considerations for engaging with Big Data from a rhetorical-humanistic perspective. Poroi, 16(1).

  • Graham, S. S., Majdik, Z. P., & Clark, D. (2020). Methods for extracting relational data from unstructured texts prior to network visualization in humanities research. Journal of Open Humanities Data, 6(8). https://doi.org/10.5334/johd.21

  • Majdik, Z. P. (2019). A computational approach to assessing rhetorical effectiveness: Agentic framing of climate change in the Congressional Record, 1994–2016. Technical Communication Quarterly, 28(3), 207–222. https://doi.org/10.1080/10572252.2019.1601774

  • Lu, S., Chen, W., Li, X., & Zheng, P. (2018). The Chinese smog crisis as media event: Examining Twitter discussion of the documentary Under the Dome. Policy & Internet, 10(4), 483-508.

 

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