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., & 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.
  • 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.
  • 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.

  • 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).

  • 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).

  • 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.

  • 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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