We change our tools, and then our tools change us. – Harold Innis

Combinations of natural language process and visualization techniques enable the creation of alternative representations of text data. Such representations can highlight the implicit, connotative aspects of language. They can also provide different mediation for both expert and lay interpretations of text. Application domains for this work span from qualitative research methods, to supporting critical analysis of political discourse.

Publications

Amin Hosseiny Marani, Joshua Levine, Eric P. S. Baumer. 2022. One Rating to Rule Them All? Evidence of Multidimensionality in Human Assessment of Topic Labeling Quality. In Proceedings of the ACM International Conference on Information & Knowledge Management (CIKM).

Eric P. S. Baumer, Drew Siedel, Lena McDonnell, Jiayun Zhong, Patricia Sittikul, and Micki McGee. (2020). Topicalizer: Reframing Core Concepts in Machine Learning Visualization by Co-designing for Interpretivist Scholarship. Human-Computer Interaction, Special Issue on Unifying Human Computer Interaction and Artificial Intelligence 35(5-6): 452-480.

Eric P. S. Baumer and Micki McGee. (2019). Speaking on Behalf of: Representation, Authority, and Delegation in Computational Text Analysis. in Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES). (Honolulu, HI).

Eric P. S. Baumer, Jaime Snyder, and Gery Gay. (2018). Interpretive Impacts of Text Visualization: Mitigating Political Framing Effects. ACM Transactions on Human-Computer Interaction (ToCHI), 25(4), 20:1–20:26.

Eric P. S. Baumer, David Mimno, Shion Guha, Emiy Quan, and Geri Gay. (2017). Comparing Grounded Theory and Topic Modeling: Extreme Divergence or Unlikely Convergence? Journal of the Association for Information Science and Technology (JASIST), 68(6): 1397–1410.

Michael Muller, Shion Guha, Eric P. S. Baumer, David Mimno, and N. Sadat Shami (2016). Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination. in Proceedings of the ACM Conference on Supporting Group Work (GROUP). (Sanibel Island, FL).

Eric P. S. Baumer, Elisha Elovic, Ying Qin, Francesca Polletta, & Geri K. Gay. Testing and Comparing Computational Approaches for Identifying the Language of Framing in Political News. in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL – HLT) (pp. 1472–1482). Denver, CO.

Eric P. S. Baumer, Claire Cipriani, Mitchell Davis, Gary He, Jaclyn Jeffrey-Wilensky, James Kang, Jinjoo Lee, Justin Zupnick, and Geri K. Gay. (2014). Broadening Exposure, Questioning Opinions, and Reading Patterns with Reflext: a Computational Support for Frame Reflection. Journal of Information Technology and Politics, 11(1), 45-63.