Justin Gross

Project Title: From Inter-Annotator Agreement to Explained Variation: Content Analytic Coding That Allows Interpretive Flexibility

Researchers typically insist upon high inter-coder reliability (ICR) for content analysis conducted by teams of media annotators, but this may be inappropriate when researching communications open to subjective interpretation. Dr. Gross will develop a measurement approach that takes advantage of annotator variability in assessing the strength of signal communicated by authors engaged in political value framing.
 
Selected Publications:
  • Gross, J.H. and K.T. Johnson. 2016. “Twitter Taunts and Tirades: Negative Campaigning in the Age of Trump.” PS: Political Science & Politics. 49(4), 748-754.
  • Card, D., J.H. Gross, A.E. Boydstun and N.A. Smith. 2016. “Analyzing Framing through the Casts of Characters in the News.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, TX.
  • Card, D., A.E. Boydstun, J.H. Gross, P. Resnik, and N.A. Smith. 2015. “The Media Frames Corpus: Annotations of Frames Across Issues. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China.
  • Gross, J.H. 2015. “Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance.” American Journal of Political Science. 59(3), 775–788.
  • Sim, Y., B.D.L. Acree, J.H. Gross and N.A. Smith. 2013. “Measuring Ideological Proportions in Political Speeches.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), Seattle, WA.