Large-scale data has become a major component of research about human behavior and society. But how are interdisciplinary collaborations that use large-scale social data formed and maintained? What obstacles are encountered on the journey from idea conception to publication? In this podcast, we investigate these questions by probing the “research diaries” of scholars in computational social science and adjacent fields. We unmask the research process with the hope of normalizing the challenges of and increasing accessibility in academia.
Music: Jon Gillick.
20. Navigating the Shores of Computational Text Analysis Validity with Christian Baden, Christian Pipa, and Mariken van der Velden
In this episode, we speak to Christian Baden, Christian Pipal, and Mariken van der Velden about their 2022 journal paper in Communications Methods and Measures, titled, “Three Gaps in Computational Text Analysis Methods for Social Sciences: A Research Agenda”. They co-authored this paper with Martijn Schoonvelde, and the authors span several disciplines, from communication to political science.
We discuss the challenges and joys of writing for a cross-disciplinary audience, how their frustrations with the validity of computational methods are shared across fields with different methodological conventions, and how this paper laid the groundwork for a larger project on European political text analysis.
9 Jul 2022
19. Constructing a Taxonomy of Implicit Hate Speech Grounded in Social Theory with Diyi Yang and David Muchlinski
Our guests on this episode are Diyi Yang, assistant professor at the School of Interactive Computing, and David Muchlinski, assistant professor in the Sam Nunn School of International Affairs, both at Georgia Tech. We discuss their EMNLP 2021 paper, "Latent Hatred: A Benchmark for Understanding Implicit Hate Speech." This paper is co-authored with Mai ElSherief, Caleb Ziems, Vaishnavi Anupindi, Jordyn Seybolt, and Munmun De Choudhury.
Diyi and David reveal that the annotation process behind this paper took two years and incorporated domain expertise on the broader context around hateful language. That is, an understanding of the social groups who produce this language allowed for better categorization and interpretation of implicit hate. We also discuss the cross-discipline connections they’ve forged in the past and present, and the ongoing challenges this type of work poses for computational methods.
9 May 2022
18. Gender Patterns in English-Language Fiction and Interrogating Data with Ted Underwood and David Bamman
This episode features Ted Underwood, a professor in the School of Information Sciences and Department of English at the University of Illinois Urbana-Champaign, and David Bamman, an associate professor at UC Berkeley’s School of Information. We discuss their 2018 Cultural Analytics paper co-authored with literary studies PhD student Sabrina Lee, titled “The Transformation of Gender in English-Language Fiction.”
We trace how Twitter brought Ted and David together as collaborators, and the email that sparked the beginnings of this project. They describe how this paper uses predictive modeling for an unconventional purpose, and various “means of interrogating data.” They also provide tips for establishing collaborative relationships, and advocate using substantive research questions to motivate learning technical skills.
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