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watching many videos from their channels. For this process, I selected the tenth most recently uploaded video to the channel. This method was deployed to capture a video that hopefully represented an average upload for the channel and not a video that was either widely popular or unpopular. If the video was titled in such a way that made it clear that it was outside of the channel’s traditional format, I then selected the 11th most recently uploaded video. By the end of the video identification period, I decided to skip two videos because they were titled as a preview trailer or Vine compilation. For each video, I recorded the video metadata and the ten most popular comments. Again, time limitations meant that I had to make decisions about the number of comments to include in this study. Some of the videos in this study had thousands of comments associated with them, but the unstructured nature of the comments make the coding process time intensive. By limiting the number of comments to 500 total, I was able to ensure that I got a variety of comments for different video creators. This variety will paint a more accurate picture of the YouTube ecosystem as a whole, rather than just focusing on a limited number of channels that might solicit a very specific kind of comment feedback from viewers. Comments on YouTube can be upvoted by users, so the most popular comments are typically listed at the top. It should be noted that if a comment was not in English or was posted by the video creator, I skipped that comment and then collected the next most popular comment. In total, eight comments had to be skipped because of those factors. Coding For this study, the unit of analysis was the video content and associated metadata, which includes YouTube analytics and video comments. The master coding sheet 19PDF Image | UNDERSTANDING AND ENGAGING YOUTUBE COMMUNITIES
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