We wanted to go deeper into the debate, to really find out what people think and say regarding the topic. For this reason, YouTube was the best platform to explore at first what the world says about the argument, so we analyzed the first 30 videos with the query Social Credit System. To maintain the duality within the process, we did the same on Iqiyi, the most used Chinese portal for videos. But at a first sight of the Chinese videos, as in protocol three, we didn’t have a proper controversy: all of them were just informative or gave neutral opinions about it, as the visualization clearly shows with the interaction possibility on the two buttons. For this reason, we went to YouTube in Chinese and analyzed even there the first 30 videos. For each of the 90 videos we: watched them; grasped some general information such as the title, likes, dislikes and so on; categorized them based on the type of video and channel; divided each video in sections, based on what the speakers were saying at that moment. The visualization shows 90 time bars, which represent the 90 videos we analyzed; the colors of each section represent what was being said in that moment of the video: just information, an “in favor” approach, against the topic, concerned, uncertain or ironic about it. We also defined when specifically the video made a reference on Black Mirror or Orwell. This visualization clearly shows the huge amount of information that we wanted to present to have a full look around the theme: at the hover of the mouse on each bar, the section on the left changes, showing all the detailed information about each video. The order of the bars is dictated by the ranking on the platform, and by clicking to the relative number and bar the correspondent link can be opened.
On Firefox browser, on incognito mode, we searched for Social Credit System on Youtube in English, in Chinese and on Iqiyi. First of all, we watched all the selected videos to have a general idea about the information that they contained and how these were communicated to the public. In this analysis we grasped the specific data that we wanted to show. During that research we noticed the presence of some recurring images, so we decided to take some screenshots for each video (these will be shown in the protocol 6).
We built the dataset according to these general parameters: video title, description, video type, channel name, channel type, country, speaker, date, length, views, likes, dislikes, comments. To go in deeper in the analysis, we also highlighted the content of each video according to the following items: relevant quotes, approach (neutral, in favor, against, concerned, ironic, uncertain) and relevant references. In the dataset all these information are based on time sections.