Daniel Gayo-Avello @ the University of Oviedo

Associate professor in the Department of Computer Science at the University of Oviedo

Daniel Gayo-Avello's main area of interest is Web IR but he has been focused on social media research from the early 2010s, publishing in CACM, IEEE Internet Computing, or IEEE Multimedia. He acted as guest co-editor for a special issue of Internet Research on the predictive power of social media, contributed a chapter on Political Opinion to "Twitter: A Digital Socioscope", coauthored the book "Retooling Politics: How Digital Media Are Shaping Democracy" (Cambridge University Press), and contributed to the Handbook of Computational Social Science (Edward Elgar Publishing). From March 2023 to February 2026 he was part of ActEU, a EU-funded research project to strengthen trust in democracy.

I'm sharing this photo because people generally like seeing the person they do business with, not because I'm proud of my own picture.

Latest updates

March 27, 2026

"Activating European Citizens’ Trust in Times of Crisis and Polarization"

Thrilled to see this open-access book published! The volume is the result of the ActEU project, exploring ways to rebuild political trust and strengthen representative democracy in Europe. I co-authored a chapter on scraping political discourse across 10 EU countries, analyzing news articles, tweets, and Telegram posts to uncover patterns of online debate around migration, climate, and gender—and how these narratives relate to citizens’ trust and polarization.

December 19, 2025

"Handbook of Computational Social Science"

Edward Elgar Publishing has released this volume edited by Taha Yasseri. I contribute to the book with the chapter “Social media and electoral prediction: ten years after”, co-authored with Andreas Jungherr and Gonzalo Rivero, in which we revisit a decade of research on electoral prediction using social media. The book offers a comprehensive and forward-looking overview of computational approaches in the social sciences.

November 27, 2025

"Los algoritmos de X pueden alterar la polarización afectiva"

Un nuevo estudio publicado en Science demuestra que la “curación” algorítmica del feed de X/Twitter puede aumentar o reducir la polarización afectiva de los usuarios según la exposición a mensajes hostiles. La Vanguardia se ha hecho eco del trabajo y recoge mis declaraciones sobre la importancia de estudiar el impacto de los algoritmos sin necesidad de cooperación de las plataformas y cómo los resultados de este estudio desafían investigaciones previas sobre Facebook e Instagram que sugerían un efecto mínimo de los algoritmos sobre la polarización.