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the potential to be infinitely large. As such, the first portion of data collection involved establishing the ecosystem. For this process, I used data from Social Blade, a website that tracks analytics for popular internet social networks. Social Blade provides real-time tracking for the Top 5000 YouTube channels. I sorted through the Top 250 most subscribed channels and filtered out those that did not meet the definition of original programming, as well as those that were not in English. While YouTube is a global platform with lots of popular non-English speaking channels, the translations of videos is not accurate enough for English speaking coders to reliably interpret the video content. I used information from VidCon, Tubefilter and VideoInk, to supplement the Social Blade data and ensure that a wide variety of channel sizes were represented in this study. VidCon is an annual online video conference that focuses on YouTube creators. I used their list of panelists for the 2015 conference to find creators that fit the original programming requirements. Tubefilter and VideoInk are industry publications that release news about YouTube creators. By subscribing to their newsletters and searching their archives, I was able to find articles that highlighted original programming creators. In total, I captured metadata from over 247 channels that qualified as YouTube original programming. From that data, I randomly selected 50 channels to study in further detail. For the full list of the 50 channels that were selected for this study, see Appendix B. After the 50 channels were identified, I selected one video from the channel to include in the study. Due to time limitations, I decided my research would be more impactful if I was able to watch videos from a variety of creators to get an understanding of the YouTube ecosystem as a whole, instead of selecting only a handful of creators and 18PDF Image | UNDERSTANDING AND ENGAGING YOUTUBE COMMUNITIES
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