Blazing Red, Paper White. How does YouTube mediate China's White Paper Protest? An analysis of the bilingual news spheres

Team members: In A Hwang, Xiaolu Ji, Anna Poggi, Bingwen (Serena) Wang, Yi Zhang

Summary of Key Findings

For the Chinese query, we generated from the YouTube data tool approximately 480 Chinese- speaking videos. The findings show the most active channels are: “美国之音中文网” (a US state sponsored Chinese-speaking news channel), “新球电台” (a Japanese branch of an anti-communist organization), “BBC News 中文” (a BBC Chinese news program based in the UK), and “TVBS” (Taiwanese channels with niche reportings). The formats or genres mostly consist of serious news reporting, commentary, live streaming shorts without editing. This affirms the censorship in China since none of the channels for the Chinese query were from Mainland China but rather Chinese- speaking communities sharing the news from abroad.

For the English query, we performed the same search and generated 2082 videos. From the dataset, we discovered that most active channels are Indian news outlets with daily detailed coverage. The top 5 most viewed videos are from reputational global English-speaking news organizations, which do not align with the most active Indian channels. And the top viewed videos contain neither personal blogs nor young channels. This can be considered a demonstration of the fact that the protest in China has become a matter of importance and resonance with the rest of the world through YouTube.

1. Introduction

From the first Covid wave in 2020, China has faced some of the most draconian Covid lockdowns. Upon the re-election of Xi Jinping as the leader of Chinese Communist Party (CCP) in October 2022, tensions escalated and social unrest began to surface. Amongst the protests, a blank sheet of A4 paper assumed the symbol and an umbrella term for the nationwide dissents. Echoing its predecessor in the 2019 Hong Kong protest, the “white paper” represents what protesters wish to say but cannot due to strict censorship. It presents a dilemma to the CCP authorities – whether to repress the protesters for saying literally nothing.

YouTube is a platform known for user generated content but in the last few years several studies have been made to better understand how mis/disinformation spreads in the platform. Another relevant aspect that has only recently been looked into is how YouTube mediates events and if the spread of information is mediated. The rise of news channels and citizen journalism around events, the system with which YouTube mediates events becomes even more unclear. Thus, this research is auditing the reports of the White Paper Protest in China on YouTube and the results the platform offers.

Online protests are a result of the spread of social media and the large reach these new forms of communication have (Meek, 2011). Social media gives people the chance of sharing information and reaching a large number of users that can resonate with it. The line between online and offline, cyberspace and real life is becoming exponentially more blurred due to the strong connection they share (Meek, 2011). Meek (Meek, 2011) discusses how the online and offline are strictly connected and it is difficult to separate them. Using the example of the flash-mobs to show how something that was born online had a real effect offline, showing how mobilizing people online can lead to people taking action offline. People can make relevant connections through social media and these ties can unite people all over the world and make regional/personal issues global (Poell, 2014). At the same time, these online ties help to spread information and issues that have the potential of helping people in need. As a user generated content platform, YouTube transcends space and therefore unites people from all over the world and helps them come together over issues of shared importance both in amateur form as citizen journalism and in more professional approaches such as news companies moving to the platform (Meek, 2011).

This paper starts with the datasets we retrieved and the results we gathered from them. Using the methods of time analysis, content analysis and network analysis, we have answered our research questions that focus on how YouTube mediates current events and in particular, the White Paper Protest. In the discussion part, we compare our results with relevant literature in order to explain them and connect them to the bigger frame. Tables and figures have been added in the results for a better understanding and readability of the results.

2. Initial Data Sets

In order to capture the videos, we designed two search queries “China Covid Protest” and “封控 抗议 (Lock-down Protest)”. These two queries can be seen as equivalent, and generate more comprehensive results referring to the event compared to queries “white paper protest” or “白纸 运动 (white paper movement)”. We used the Video List module of YouTube Data Tools (Rieder, 2015) to collect all videos generated by the two search queries. By limiting the search to videos published between 2022-10-13 and 2023-01-09 (from Sitongqiao Protest to the day on which data was collected) and setting the iteration to the maximum, two datasets (2082 English videos and 480 Chinese videos in total) were collected for further analysis. We also combined the two datasets into one and generated a channel network based on the video recommendation relations, with detailed channel information, using the Video Network and Channel Info module of YouTube Data Tools (Rieder, 2015).

3. Research Questions

This research investigates how YouTube mediates current events through the case of the White Paper Protest in China. It aims to map an overview of the videos related to the protests on YouTube with a focus on English and Chinese contents. More specifically, we ask: (1) How timely was the protest “reported”? (2) How is the protest presented by channels within the bilingual spheres and their corresponding political standing? (3) How are the protest-related videos mediated by co- watching history and recommendation algorithms?

4. Methodology

For the time analysis we extracted data as explained in section 2 of the paper. Once we had the .csv files, we transferred them on Excel and started to analyze them. Pivot tables on Excel were used to gather the number of posts in total per month and per day, along with all the necessary information in order to complete the histogram of how the events reflected on YouTube but also to rank the channels and videos in the order of activity and popularity. Firstly, we checked the number of posts in total and then divided them into months and later into days to see which periods were the most active and if the spikes in the datasets coincided with the timeline of the protests. Through the pivot table, it was also possible to extract most representative channels and videos for content analysis to further investigate our second research question.

After collecting the data and creating the pivot tables, we sorted the dataset based on the ranking of the most active channels along with the most viewed videos from both English and Chinese queries. From there, we selected the top five most active channels and the top 5 most viewed videos as our sample for content analysis.

We then conducted the qualitative approach by auditing the above-mentioned sample. We focused on information including the genre of the videos, reporting angle of the content, political standings, channel category, location, channel age, number of subscribers, number of comments and likes. The purpose of the analysis is to tease out potential alignment between the most active channels and the top videos, as well as the narratives revolving around the event.

As stated before, a channel network was generated based on the video recommendation relations combining the two datasets. We then used Gephi to conduct a network analysis of recommendation relations among channels. We set the node color according to channel locations and the node size according to the subscriber count, to identify the “central” channels in each cluster and the “bridging” channels between the bilingual spheres.

5. Findings

RESEARCH QUESTION 1 – How timely was the protest “reported”?

The timeline of the protest is reflected in the results of the datasets. November was the month with the majority of the videos published due to the Urumuqi fire that broke out on 24th of November 2022, which serves as a direct trigger of the protests. Most videos were published closely following the incident.

Figure 1 - English Query

Figure 2 - Chinese Query

Furthermore, an interesting point to observe is the interaction of the users with the channels. In figure 3 and 4, the users interact differently with the videos generated by the two queries. In fact, people responding to the results of the Chinese query tend to like the videos more than the English query, where the users prefer to comment.

Figure 3 - English Query Audience Interaction

Figure 4 - Chinese Query Audience Interaction

Interestingly, most of the videos were uploaded one or two days after the events, indicating that the peaks of posting occurred around key events. The Chinese query, although producing fewer results, shows that the spike in the number of videos is closer to the event than the English query in terms of temporality.

Figure 5 & 6 - Videos Published in October 2022

As shown in figure 5 and 6, the spike in the English query is a day later than in the Chinese one, whereas the numbers of videos published on the 13th of October 2022 are similar. On October 13th, the Sitongqiao Protest jumpstarted the chain of events, where people demanded the removal of “dictatorship thief” Xi Jinping. This is reflected in the spike on the same day of the protest in the Chinese query, which is also reflected in the English dataset, with the spike happening a day later. Regardless of the spike the next day, the numbers of videos published on October 13th between the two queries are similar.