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CHAPTER 3. STUDY DESIGN 8 for content related to our university and reached out to the content creators (3 participants). Third, we posted a notice on our university’s various Facebook pages. We recruited one per- son from our Facebook post and reimbursed them with $10 for their time. We interviewed a total of 9 people (6 male, 3 female; 3 White/Caucasian, 3 Asian, 2 South Asian, 1 Hispanic; aged 18 to 30, M = 21). Our interview participants had an average of 5 years creating YouTube videos and ranged from posting content weekly to yearly on their channels. As of December 2018, our participants had between 56 to 257,000 subscribers (average=38,100, median=4,950). Most of our participants primarily make “lifestyle videos” with one partic- ipant making music videos. Interviews were either held on the UC Berkeley campus or via Google Hangouts when in-person was not possible. Each interview took around one hour to complete. We audio recorded and transcribed all interviews. Our interviews were semi-structured and centered around the following questions: • How do content creators make sense of the YouTube algorithm? • How do their perceptions of the algorithm affect how and what they post? • If they could, what would YouTube content creators change about the algorithm? One of the challenges that we faced in our interviews was prompting participants to dig deeper into how they make sense of the algorithm beyond the surface level features that they imagined the algorithm cares about (e.g. thumbnails). Part of the reason was that algorithms have very recently entered mainstream conversation and we have not yet developed the conceptual tools for people to describe their effects. The problem became even more challenging when we asked people to imagine alternatives. Traditions of participatory design teach us to engage users in the design process [63]. Speculative design allows us to overcome opacity stemming from corporate secrecy and instead imagine what we would want an algorithm to do. But it is not clear how to engage people when opacity is a fundamental characteristic of algorithms [13]. How might HCI researchers engage with stake-holders about the effects of seemingly invisible algorithms? In absence of a physical representation of an algorithm to point to and discuss we chose to create physical representations of algorithmic effects. This is close to the ways that people actually experience algorithms in the real world. Eslami et al. took a similar approach [26]. They made study participants aware of the existence of algorithms in their Facebook News Feed by creating an alternative feed that did not have algorithmic selection. We extend this approach and invite participants to analyze and change what the algorithm does: After our first three interviews, we created prototypes of alternative YouTube front pages and recommendation tabs that addressed matters our interviewees had discussed (see Figure 3.1). We continuously adapted these prototypes and made new ones based on our interviews. The goal was not for us to create the best possible YouTube algorithm, but to use design as provocation to elicit reactions from our interviewees. We used our designs toPDF Image | How Content Creators Craft Algorithmic Personas and Perceive the Algorithm that Dictates their Work
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