Servin, A. (2022). "The Construction of Collective Identities in Social Networks." Global Digital Humanities Symposium Proceedings. Michigan State University.
Conference paper · Read the publication
Research · academic publications
Publications.
Four academic publications on digital qualitative methods and networked tribes. Citations use the legal name, Servin Arroyo, A., except where the original source abbreviated it.
Measure first. Interpret after. Publish both.
Leetoy, S. y Servin Arroyo, A. (2018). «Personalización de la política en tiempos de wikis: tribus digitales en Twitter en torno a la candidatura ciudadana de Pedro Kumamoto». En Ciudadanía digital y democracia participativa (pp. 201–228). Salamanca: Comunicación Social Ediciones.
Book chapter, in Spanish · ISBN 978-84-17600-04-4 · Read the publication
González, L. y Servin Arroyo, A. (2017). «Métodos cualitativos digitales: un acercamiento a la antropología digital y otras posturas de investigación». Virtualis, 7(15), 61–80.
Journal article, in Spanish · ISSN 2007-2678 · Read the publication
De Colsa, M., González, L. y Servin Arroyo, A. (2013). «Redes sociales: nueva era en investigación interpretativa». Versión. Estudios de Comunicación y Política, 22(31). Universidad Autónoma Metropolitana.
Journal article, in Spanish · Read the publication
Research training
Digital humanities as the spine.
MA in Digital Humanities from Tecnológico de Monterrey (2020–2022), completed with the Mención Honorífica de Excelencia — the Tec’s highest distinction. The agenda: digital ethnography, platform communities, and translating social data into human meaning.
Academic collaborations or research interviews? Write to Alex.
An open question
What makes a body of knowledge a science?
In 2001, the Sorbonne granted a doctorate in sociology to an astrologer, Élizabeth Teissier. The thesis was directed by Michel Maffesoli, from whom we borrow the language of tribes and nomadism, and the jury was chaired by Serge Moscovici, the theorist of how active minorities end up shifting majorities. It passed with the highest distinction.
The field criticized it harshly, and rightly so. But the case leaves a question that lingers: when it is humans trying to understand other humans, what legitimizes knowledge, the method or the endorsement of whoever signs it? Today that question is more urgent than ever. When a company entrusts a decision to an AI model that returns a number with total confidence, the same dilemma returns: do we believe it because the machine says so, or because someone verified that the data means what it appears to mean?
That is where we work, owning that tension instead of hiding it: empirical rigor to measure, cultural sensibility to interpret, and human judgment to decide when a finding is real and when it is merely a well-presented mirage. Between fascination and rejection lives everything we do when we read the people behind the data, and everything a business needs before betting on a figure.