Measuring Distributed Mentoring in an Online Fanfiction Community

Abstract

This dissertation approaches questions about how creators informally learn from their online networks using a human-centered data science perspective. Over the past few decades, participation in online communities has become a staple piece of how people engage with mainstream media, produce narratives, and develop creative skills. Informal exchanges of knowledge, feedback and support across networked spaces are key in the creative process and growth of today’s creators. Distributed Mentoring provides a theoretical framework for how individually brief exchanges among a network of media producers and consumers may sum to a greater whole of mentorship. This dissertation expands on the rich lineage of ethnographic research in this area by contributing new quantitative analyses that model distributed mentoring in a large fanfiction community where millions of writers have participated for decades. In addition to contributing new findings about the structure and effects of distributed mentoring in the fanfiction community, this work demonstrates an interdisciplinary, human-centered approach to conducting data science for the purpose of studying online informal learning. I conclude with implications for effective feedback exchange and network growth in creative communities, such as addressing socio-emotional needs, signaling interests and identities, supporting authentic relationships and designing inclusive and safe feedback environments.