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by Katherine A. Keith, Naitian Zhou, & Lucy Li
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- Oct 30, 202357 min
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.
- Jul 9, 202256 min
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.
- May 9, 202253 min
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.
- Apr 24, 202255 min
17. Hashtag Network Analysis and Interwoven Research Ethics with Ryan Gallagher and Brooke Foucault Welles
Our guests in this episode are Ryan Gallagher, a PhD Candidate in Network Science at Northeastern University, and Brooke Foucault Welles, an Associate Professor in Communication Studies and the Network Science Institute at Northeastern University. We discuss their 2019 CSCW paper, "Reclaiming Stigmatized Narratives: The Networked Disclosure Landscape of #MeToo" with co-authors Elizabeth Stowell and Andrea G. Parker. We talk about their substantive motivation for focusing on #metoo, the networked counter public, and hashtags' influence on social change. Ryan and Brooke also walk us through the advantages of pairing qualitative and quantitative work, weaving ethics throughout every stage of the research process, dealing with missing Tweets, and taking seriously both the "computational" and "social science" sides of CSS.
- Mar 20, 202250 min
16. Measuring Uptake in Classroom Conversations and Using NLP to Support Teachers with Dora Demszky
This episode features Dora Demszky, a PhD student in Linguistics at Stanford University. Dora works at the intersection of natural language processing and education. We discuss her ACL 2021 paper titled "Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions", co-authored with Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, and Tatsunori Hashimoto. Dora's work is motivated by creating tools that are useful for educators, so her research is not only descriptive or predictive, but also applicable to classrooms. She talks about managing large interdisciplinary teams, approaching research with care, and working with actual teachers to annotate data.
- Mar 6, 202251 min
15. Race in Computational Disinformation Analysis and Deep Reading with Deen Freelon
Our guest in this episode is Deen Freelon, Associate Professor at the University of North Carolina in the School of Journalism and Media. We chat about his 2020 Social Science Computer Review Paper "Black Trolls Matter: Racial and Ideological Asymmetries in Social Media Disinformation" with co-authors Michael Bossetta, Chris Wells, Josephine Lukito, Yiping Xia, and Kirsten Adams. Deen also talks about writing a "behind the scenes" book chapter about the process of making this paper, being one of the first movers in the discipline of computational methods for communication studies, and how he learns programming best when it is connected to the goals of his project. He emphasizes that many of his great research ideas come from reading deeply and recommends devoting at least half a day a week solely to reading.
- Feb 20, 202252 min
14. The Past Decade of Computational Social Science Research with David Lazer
In this episode, we talk with David Lazer, the University Distinguished Professor of Political Science and Computer Sciences at Northeastern University and the Co-Director of the NULab for Texts, Maps, and Networks. We discuss two seminal papers in computational social science he co-authored a decade apart: "Life in the network: the coming age of computational social science" (Science 2009) and "Computational social science: Obstacles and opportunities" (Science 2020). David shares with us events in his long and distinguished CSS research career. In the early 2000s, he helped gather a small group of people working on new "data streams" and how they intentionally created the term computational social science. He also talks about his own struggles on the academic job market, advice for aspiring CSS researchers, and a wish for better data availability structures.
- Feb 10, 202249 min
13. Finding (Mis)alignments in Public Opinion and Wisdom in Collaboration Management with Kenneth Joseph and Sarah Shugars
Our guests on this episode are Kenneth Joseph, an assistant professor in Computer Science and Engineering at the University of Buffalo, and Sarah Shugars, a Faculty Fellow at New York University’s Center for Data Science. We discuss the process behind their EMNLP 2021 paper, “(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys,” co-authored with Ryan Gallagher, Jon Green, Alexi Quintana Mathé, Zijian An, and David Lazer. Kenneth and Sarah offer tips around communication, collaboration, and project management, especially for papers written during a pandemic. Kenneth talks about “privileging ethics” when making decisions around data privacy and experimental replicability, and Sarah reflects on navigating differences in terminology use in interdisciplinary environments.
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About Diaries of Social Data Research
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.
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