Exploring the Network Structure of Academic Silos in Framing Research

• In Event: Poster Session: Political Networks & Political Communication
  In Poster Session: Poster Session: Political Communication

Abstract

For decades, the development of the influential theory of media framing was hampered by scientific disagreements about how to conceptualize, identify, and measure frames (D’Angelo, 2018). Debates around framing became so frequent and intense, that some have even called for the altogether abandonment of the concept (Cacciatore et al., 2016). Yet, despite these arguments, a systematic analysis of the deployment of framing concepts and measures across disciplines and time is absent. Using multiple network analysis approaches, we examine the scholarly use of framing, focusing on the creation of disconnected knowledge-silos preventing collaboration and coherence.
Framing is the selective presentation of information in ways that can influence attitudes, beliefs, decisions and behaviors (Chong & Druckman, 2007). Prominent in a variety of academic fields, the theory became highly contested and controversial - a ‘fractured paradigm’ (Entman, 1993). First, researchers differ on the conceptualization of framing, torn between ‘equivalency framing’, known for the use of tight manipulations (Kahneman & Tversky, 1984) and ‘emphasis framing’, more in line with journalistic practice and real-world framing (Shah et al., 2009). Another conceptual debate revolves around the focus on either ‘generic’ news frames and ‘topic-specific’ ones (D’Angelo, 2018).
A second line of disagreement was formed around methodological choices, and particularly the question of how-to best measure frames. Those adhering to the deductive strategy identify predefined, theoretically-based, and limited sets of frames in corpora (Matthes & Kohring, 2008). Others, believing the deductive method is limited and prone to researchers’ bias (van Gorp, 2010) developed inductive methods, including ones relying on novel computational methods such as unsupervised machine learning (Walter & Ophir, 2019).
These debates and disagreements limited the ability to develop a coherent theory of framing. We argue that to solve some of the disagreements, it is first critical to understand the lines of contention, and measure their prevalence in research. To do so we analyze socio-academic connections in the form of citation networks (RQ1), examine the themes and topics used in framing research (RQ2), and identify discursive communities around framing (RQ3), over 40 years.
All abstracts containing the words “framing” or “frame” with one of the words: “media”, “journalis” or “press”, between 1980 and 2021 were collected from ProQuest and EbscoHost. To address RQ1, a sample of 1000 papers will be collected from the corpus and full citations for each paper will be retrieved. These data will be used to construct a citation network from which socio-academic communities can be segmented and analyzed, including the identification of influential papers and changes over time. To address RQ2 the analysis of topic model networks approach (Walter & Ophir, 2019) will be utilized, combining topic modeling, an analysis of topics network based on co-occurrence, community detection, and qualitative reading. Lastly, discursive communities (Walter et al., 2020) will be analyzed by constructing a network where authors are nodes, and edges represent linguistic similarity between authors. Taken together, the analyses will allow the examination of academic silos and traditions within framing theory, and its development over time.

References

Cacciatore, M. A., Scheufele, D. A., & Iyengar, S. (2016). The End of Framing as we Know it … and the Future of Media Effects. Mass Communication and Society, 19(1), 7–23.
Chong, D., & Druckman, J. N. (2007). Framing Theory. Annual Review of Political Science, 10(1), 103–126.
D’Angelo, P. (2018). Doing News Framing Analysis II: Empirical and Theoretical Perspectives. Routledge.
Entman, R. M. (1993). Framing: Toward Clarification of a Fractured Paradigm. Journal of Communication, 43(4), 51–58.
Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341–350.
Matthes, J., & Kohring, M. (2008). The Content Analysis of Media Frames: Toward Improving Reliability and Validity. Journal of Communication, 58(2), 258–279.
Shah, D. V., McLeod, D. M., Gotlieb, M. R., & Lee, N.-J. (2009). Framing and agenda setting. In R. L. Nabi & M. B. Oliver (Eds.), The SAGE Handbook of Media Processes and Effects (pp. 83–98). SAGE Publications.
van Gorp, B. (2010). Strategies to take subjectivity out of framing analysis. In P. D’Angelo & J. A. Kuypers (Eds.), Doing News Framing Analysis: Empirical and Theoretical Perspectives (pp. 84–109). Routledge.
Walter, D., & Ophir, Y. (2019). News Frame Analysis: An Inductive Mixed-method Computational Approach. Communication Methods and Measures, 13(4), 248–266.
Walter, D., Ophir, Y., & Jamieson, K. H. (2020). Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015–2017. American Journal of Public Health, 110(5), 718–724.

Authors

• Dror Walter, Georgia State University

• Yotam Ophir, University at Buffalo