The internet, social media, and online communication have great potential as a platform for political engagement, from seeking political information to engaging in political discussion and deliberation. The wealth of content available, and the wide variety of means for participation, facilitate civic engagement for many diverse groups. Furthermore, such online political activities can operate on a larger scale than their face-to-face counterparts, by including a greater number and variety of participants.
However, these online media are not wholly unproblematic. Online political participation has not been radically democratic, but has tended to reproduce and reinforce preexisting inequalities and balkanization in political talk. Furthermore, the constantly and rapidly increasing quantity of political content produced on a daily basis can be difficult to understand and sort through, particularly with respect to how issues are framed.
Such challenges are not entirely unique to political discussion. Indeed, techniques developed for the analysis of large bodies of textual data, such as topic modeling, sentiment analysis, and other computational linguistic and data analysis methods, may be useful here. However, relatively little work has involved applying those techniques to support awareness of and reflection about framing in political discourse, nor has much research sought to understand how the design of tools that leverage such techniques can mediate political discussion and deliberation. These computational methods may be able to transform the problems of scale, i.e., overwhelming amounts of content, into a resource, i.e., a data source from which to draw insightful analyses.
This project involves the design, implementation, and evaluation of tools that incorporate computational analysis techniques to support frame reflection into the processes of online political engagement. This work involves both the application of existing analytic methods and the development of novel computational techniques, as well as evaluation in two real-world settings: in public deliberative forums and with readers of political blogs.
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.
Vera Khovanskaya, Eric P. S. Baumer, and Phoebe Sengers. (2015). Double Binds and Double Blinds: Evaluation Tactics in Critically Oriented HCI. in Proceedings of the Fifth Decennial Aarhus Conference on Critical Computing. Aarhus, Denmark.
Eric P. S. Baumer. (2015). Reflective Informatics: Conceptual Dimensions for Designing Technologies of Reflection. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI) (pp. 585–594). Seoul, South Korea.
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.
Eric P. S. Baumer, Francesca Polletta, Nicole Pierski, Christopher Celaya, Karen Rosenblatt, and Geri K. Gay. (2013). Developing Computational Supports for Frame Reflection. in Proceedings of the iConference. (Fort Worth, TX). [36% acceptance rate]