cv
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Basics
| Name | Jose Marichal |
| Label | Political Scientist |
| marichal@callutheran.edu | |
| Phone | (805) 493-3328 |
| Url | https://josemarichal.github.io/ |
| Summary | I am a professor of political science at California Lutheran University. I specialize in studying the role that algorithms and AI play in restructuring social and political institutions. I am currently writing a book entitled -- You Must Remain an Algorithmic Problem -- that will come out in 2025 with Bristol University Press (UK). The book explores the unwritten social contract we have with the algorithms that shape what we see, hear and think. I have a number of projects with collaborators looking at how social media shapes political discourse. |
Work
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2004.08 - current Professor of Political Science
California Lutheran University
Teaching at Teaching at CLU. Here are the courses I teach or have taught: Courses I Teach (or Have Taught): Technology and Politics, Social Media and Politics, Social Media as Data - Intro to Natural Language Processing and Network Analysis, Scope and Methods of Political Science, Race, Multiculturalism and Politics, Contemporary Issues in Public Policy, Modern Political Thought, American Political Thought, Introduction to Political Science, The Politics of Community Development, Seminar in Citizenship and Civic Engagement
- All of them
Education
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1996.01 - 2003.01 Boulder, CO
Publications
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2024 Why Do Some Shout and Others Stay Silent? Communication Context Consistency in Political Discourse Offline and on Facebook with Don Waisenan and Carrie Anne Platt.
International Journal of Communication
Through an open-ended survey of 206 U.S. adults, we investigate how communication context (offline and on Facebook) informs one’s willingness to share political opinions. We develop an analytical approach for examining how the stability of or shifts between offline and online messages can provide insights into the discourse environments of social media platforms. Our study uses qualitative analysis to identify and explore (1) self-censorship types, (2) conditions, and (3) tactics specific to online and offline contexts. Our approach helps explain both polarization and self-censorship in political conversations on Facebook. More so, it can provide a way to operationalize and evaluate the environments for political discourse on social media platforms in general.
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2023 Fracking Twitter: Utilizing Machine Learning and Natural Language Processing Tools for Identifying Coalition and Causal Narratives. with Andy Pattison, Christoper Cherniakov and William Cipoli
Politics and Policy
This study builds upon this emerging body of literature and our previous work, which uses sentiment analysis, a natural language processing technique, to evaluate the use of the angel/devil shift across coalitions before and after a major policy change. We examined Tweets that included the terms “fracking” and “New York” before and after the introduction of a moratorium.
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2021 The devil we know and the angel that did not fly: An examination of devil/angel shift in twitter fracking “debates” in NY 2008–2018
Review of Policy Research
We adapt and test NPF propositions related to the use of the devil/angel shift strategies before and after a major state-wide policy change, that is, a state-wide moratorium on high volume hydraulic fracturing or fracking.