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CHAPTER 3. STUDY DESIGN 10 Content Analysis Our second source of data was native formats of information sharing online. We watched videos of YouTubers talking about the algorithm on the platform. Additionally, we reviewed information available online about VidCon, the major convention for YouTubers and YouTu- ber forums (e.g. yttalk.com) and subreddits (e.g. /r/NewTubers, /r/YouTubeCreators, and /r/PartneredYoutube). We analyzed YouTube videos of YouTubers speaking about their understanding of the algorithm [18]. Two members of our team searched YouTube for the phrases: “YouTube algorithm”, “YouTube algorithm explained”, “YouTube algorithm hack”, and “YouTube algorithm rant.” We used two criteria for choosing videos 1) it must be produced by a hobbyist content creator (as we defined as someone with under 1 million subscribers) 2) describe an individual’s understanding and perception of the algorithm, not just known technical details. We watched videos, took detailed notes and quotes, and exchanged our findings frequently until we reached theoretical saturation. In total, we watched 245 minutes of content, across 11 unique creators. As of April 2019, these creators had between 11,000 to 646,758 subscribes (average = 264,432, median = 221,000). We also analyzed information available online about VidCon- including the program and recorded sessions about the algorithm. Vidcon is an annual video conference started by popular YouTubers and brothers Hank and John Green in 2010. It has since grown to an international convention where thousands of YouTubers meet yearly in the US, Australia, and the UK. VidCon is an opportunity for creators to meet each other in person, learn from successful YouTubers, and gain insider secrets from industry leaders. 3.3 Data Analysis We engaged in an iterative, collaborative process of inductive coding to extract common themes that repeatedly came up in our data. After completing the interviews we met weekly and discussed themes and concepts as we continued our fieldwork. We conducted a catego- rization exercise in which we physically laid out themes and relevant quotes into emerging categories (Figure 3.2). Some of our initial categories included attitudes and feelings towards the “algorithm”, content creators’ behaviors, their mental models, and algorithmic fairness. We used Dedoose, an online tool for open coding, to map data onto these categories. Each of two first authors independently coded half of the data. Through the open coding phase, the category of content creators personifying the algorithm was the most pervasive, occurring in all of our transcripts. To analyze responses from our wiki survey we use the analysis function on allourideas.org which we also used to collect the data. We posted the wiki survey on the subreddits: /r/NewTubers, /r/YouTubeCreators, and /r/PartneredYoutube, as well as the YouTuber forum: YTtalk and a Facebook group: Small YouTuber Zone. As of April 4th, we received 572 total votes, and 43 unique voters. We seeded the poll with 6 themes from our fieldPDF Image | How Content Creators Craft Algorithmic Personas and Perceive the Algorithm that Dictates their Work
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