This chapter assesses the possibilities of integrating computational network analysis into ethnographic fieldwork. Grounded in examples from an ethnographic case of travel vlogging in Singapore, I discuss how participant observation in urban areas can be combined with network cluster analysis. The huge variety of data collection tools available to present-day media researchers has prompted a plethora of disparate data points and a multitude of ethical dilemmas. Computational network analysis provides manifold avenues for combining interpretations of localised meaning-making with visual evidence about networks of recommended videos on the popular video-sharing platform YouTube. The dissemination of video content is largely shaped by the platform’s recommender system. Exploring how the YouTube recommender algorithm drives the circulation of vlogs about Singapore’s touristic Clark Quay area, this chapter brings to light that the act of recommending a video is based on semantic similarities among video titles and video keywords. Furthermore, the methodological reflections presented in this chapter demonstrate that the combination of computational network analysis and ethnographic fieldwork provides holistic understandings of how highly mediated tourist places are unbound from their physical settings and drawn into platform ecologies, consisting of local areas of production, algorithmic technologies, disseminated place images and platform audiences. As algorithmic mediation plays an important part in accessing platform content about politics, health, culture and entertainment, a myriad of everyday practices is affected by recommender systems. Such algorithmic technologies raise multiple ethical concerns about the accountability of platform owners for a fair and balanced distribution of content on the internet.