Computational observation

Challenges and opportunities of automated observation within algorithmically curated information environments using a browser plug-in

  • Mario Haim LMU Munich
  • Angela Nienierza LMU Munich
Keywords: data collection, computational methods, social media, observation, browser plug-in, Facebook, media use


News consumption within social media has become a prevalent phenomenon throughout recent years. Yet, such news consumption has introduced methodological challenges for data collection. Especially the fragmentation of media use, media sources, and the personalized selection of content within algorithmically curated information environments have made it difficult to adequately measure news use within social media. After discussing the chances and pitfalls of several potential approaches of data collection, we present a novel approach of computational observation: We have developed an open-source browser plug-in to unobtrusively observe Facebook users. We discuss technological, ethical, and practical considerations of such an automated solution and present potential links to panel surveys and content analyses as adequate multi-method designs. Ultimately, we present a case study as a proof of concept. While this case study suffers from severe recruitment difficulties, results indicate a reliable methodological set-up, ready to be implemented for the data collection within a variety of media-use and media-effects studies.

How to Cite
Haim, M., & Nienierza, A. (2019). Computational observation. Computational Communication Research, 1(1), 79–102. Retrieved from