Team members: Blossem Kreffer, Gabby Agustin, Jielu Liu, Yanwen Chen, Yitong Liu
Summary of Key Findings
The findings of our research suggest that Twitter is a more active and real-time platform for discussions and debates on the White Paper protest. Through analyzing users' comments on the BBC's content published on YouTube and Twitter, we found that both platforms were dominated by left-wing commentary, however, there are some significant differences between these two platforms. Twitter was found to have a higher frequency of content publishing and had a presence of right-wing topics such as xenophobia, patriotism, and national socialism. On the other hand, YouTube had a higher presence of left-wing topics such as solidarity and justice. These findings indicate that while both communities on YouTube and Twitter are dominated by left-wing commentary, YouTube forms a far more predominantly left-wing discourse space. Additionally, it is worth noting that these findings only pertain to the specific communities and topics of the White Paper protest as discussed on BBC commentary on YouTube and Twitter.
The study is based on the case of the White Paper protest that erupted in China in 2022. On 24 November, a high-rise residential building fire in Urumqi China sparked controversy as some people argued that residents were unable to escape due to the Covid-19 blockade policies that hindered rescue. A series of protests against the zero-covid policy took place in mainland China starting with one at Communication University of China, Nanjing on 26 November 2022 (Qiu, 2022). In the following days, mass protests broke out in China’s big cities such as Shanghai and Beijing and even overseas. On 7 December, China released a circular announcing 10 new prevention and control measures to ease restrictions on visits to public venues and travel and to reduce the scope and frequency of mass nucleic acid testing (Xinhua, 2023), some believe it was the result of the protests.
China's Internet censorship has blocked almost all speech unfavorable to the governing of the China Communist Party and, for reasons of social stability, the Chinese government expects mainland nationals to have no access to related information. China mainland users cannot directly access global social networks such as YouTube, Twitter, and other information-sharing social platforms due to Internet censorship in China.
"Will you arrest me for holding a blank paper?" is the implied meaning of this popular political activity and it is also a protest against blockade, restriction, and censorship. Following the White Paper Protests, Chinese censors have filtered out tens of millions of posts on domestic social media platforms, with searches for "white paper" showing only reserved results (Qiu, 2022). As of early December 2022, many Chinese, despite the scale of the protests, are still unaware of what has occurred. In that regard, the Great Chinese Firewall effectively limits further unrest (Dessein & Roctus, 2022).
However, according to StatCounter
, in 2022, Google holds 3.03% of the search engine market share in mainland China (Global Stats, 2022), which means that at least 3.03% of mainland China citizens are using various methods and tools to bypass Internet Censorship. The most prominent of these methods is using a Virtual Private Network (VPN), which changes the IP address of the local device by redirecting traffic to an external server, thus bypassing the GFW and getting to participate in political discussions on YouTube and Twitter. Thus, people who discuss the topic of White Paper protests on YouTube and Twitter form a suspicious group, whose nationality cannot be determined. In this case, our study will analyze the discursive environment of the two platforms in detail.
This study pins down Twitter and YouTube as two field sites where online political debates usually happen. As one of the most popular social platforms nowadays, Twitter has been regarded as a crucial field site of political practices by empirical studies after the 2008 American presidential election (Stolee & Caton, 2018). The other platform we explored was YouTube, the largest scaled video website worldwide. YouTube was highlighted for its specific discourse based on videos and its huge influence.
In addition, the different platforms are providing a different environment for the users to work through the design, usually known as the services and functions of a platform. Media scholars (Bucher & Helmond, 2018) defined the services a platform allows its users to steer as affordances. Therefore, specific affordances embedded in both platforms will be reviewed in this section.
Arguably, Twitter is forming its own platform discourse through specific designs provided for users. In general, Twitter is famous for its fragmented format of message transactions. Twitter constrains its users to post only one hundred-and-forty words for each tweet, limiting the volume of information a tweet can carry. Within the limited word count, the correctness and clarity of a message would be reduced. Especially for the political discussions conducted on Twitter, the limited word count and the fragmented information can lead the discussions to polarization (Stolee & Caton, 2018). Moreover, through the function of replying or retweeting, the discussion on Twitter is a process of the aggregation of opinions. In other words, Twitter enables its users to read the original tweets when they are about to build their arguments (Stolee & Caton, 2018; Gross & Johnson, 2016). In this sense, the preceding content is provided for any user who reads the arguments and encourages it to thoroughly understand what is at the stake in the argument and even post its own opinions.
Regarding YouTube, the scholar Rogers (2019), has found that YouTube has transformed itself from a content hub to a platform for individual creative content makers, known as “!YouTubers” (Rogers, 2019). As these YouTubers are busy constructing their own channels, it has been noticed for the position of YouTube in its user generation and political propaganda (Rogers, 2019). On the one hand, with the participatory culture of Web 2.0, the alternative interaction between users, creators, and platforms is valued for both potential commercial interest and the specific consumer culture of platforms (Rogers, 2019). On the other hand, since YouTube can be used by any creator, it can be used as a propaganda tool for political purposes as well, especially after the 2016 American presidential election. This also explains why we conducted YouTube as the second platform to interpret the arguments underneath the videos about White Paper Movement.
While discussing a political statement or speech, two concepts, “left” and “right”, are commonly addressed. These concepts work as the measurements of the stand of a political stand. The history of the concepts can be traced back to the seating of the French Parliament (Fuhse, 2004). The parliamentarians would sit at either left or right for their ideological position. Since then, the terms “left” and “right” were adopted to deconstruct the political place and later as tools to interpret political behaviors. Bauer et al. (2017) have found that people across different countries, cultural backgrounds, and societies suggested different interpretations of left and right wings. Therefore, Bauer and his colleagues (2017) conducted research to figure out relevant elements relevant to the understanding of left and right wings. As a result, scholars (Bauer et al., 2017) found that through the sample respondents across different countries and cultural backgrounds, the left scale commonly corresponds to the values, parties, and ideologies caring about societal equality, democracy, etc. While the right scale corresponds to the opposite, such as the maintenance of the hierarchy of the present society. Based on the findings of Bauer et al’s, we conducted our own coding scheme to analyze the discourses we collected on the platforms (see the section methodology).
Within the context, it should be borne in mind that even platforms are keen on claiming neutrality and openness, however, political biases are embedded in them. Regarding Twitter, Dean (2020) explored user ethnography which turned out that the majority of Twitter users are the American middle class who are usually leftist supporters. While there are also empirical studies on the extreme rightists’ propaganda practiced on Twitter (Barberá et al., 2015). Therefore, Twitter can be regarded as somehow a platform for tolerant voices from both sides. However, it cannot suggest the neutrality of the platform because the speeches on the platform easily went polarized to either extreme left or right on the platform according to the empirical studies (Dean, 2020; Barberá et al., 2015). For YouTube, previous scholars find a large amount of cross-partisan commenting, but much more frequently by conservatives on left-leaning videos than by liberals on right-leaning videos in the study of US partisan media and user comments (Wu & Resnick, 2021). And according to some studies, YouTube plays a prominent role in the radicalization of opinions and in the diffusion of questionable (i.e., poorly fact-checked) content (Di et al, 2021).
Overall, the empirical literature has found different discourses that platforms are constructing. In this sense, we picked Twitter and YouTube as two relevant platforms for political discussions, to shed light on the distinctions of the representation of the White Paper Movement formed on different platforms.
2. Research questions
We have two research questions: 1) How is the White Paper protest represented on the platforms YouTube and Twitter in the same timeline? 2) What are the differences between the debates and discourses in the BBC community around the White Paper Movement on these two platforms?
With the first research question, we expect to find out which day has the highest peaks of content being uploaded, resulting in a general timeline overview. Then, we will investigate whether the two platforms show this same day as the highest volume of content or not, and how these similarities or differences can be explained. We will analyze this further by diving into the content and looking at what the comments are saying, which brings us to our second research question.
For our second research question, we expect to find some differences on the platforms; the first being platform vernaculars, which is the native language to platforms. Twitter and YouTube are two different platforms, each with their own platform affordances and both allowing for their own native platform vernacular. This specific discourse will be shown when we analyze our data from the two platforms.
We also expect to analyze how politically left-winged or right-winged the reactions on YouTube and Twitter are on the topic of the White Paper Movement. This could then give us a better understanding of the political debate on YouTube and Twitter and how this differs per platform. Since we are investigating content from BBC News for both YouTube and Twitter, the main difference will be in the political preferences of the platform users because the news outlet itself will remain the same for our analysis.
To answer our research questions, we conducted both quantitative and qualitative analysis. First, we decided to look into the general overview of the White Paper protest commentary and representation on Twitter and YouTube. We used Rieder's (2015) YouTube Data Tools’s Video List Module to collect the total number of videos that discussed the protest. When setting our parameters, our search query was “china protest”, with an iteration of 1, published from November 24 to December 3, 2022, and ranked by date in chronological order. We ended up receiving a total of 1,121 videos from various channels. Additionally, we used 4CAT and created a Twitter data set with the query “china protest”, retrieving 0 tweets to get the maximum number of tweets possible, with the same date range as our YouTube parameter.
Second, we wanted to be more specific and look into two YouTube videos and two tweets of BBC News’ covering the topic of White Paper protest. We used YouTube Data Tools’s Video Info to retrieve the comments under the videos. When setting our parameters, we limited the YouTube comments to the top 30. For the first YouTube video (“Blank paper becomes a symbol of China’s protests”), we received a total of 922 comments. Meanwhile, for the second YouTube video (“Protestors urge China’s President Xi to resign over Covid restrictions”), we received a total of 912 comments. Meanwhile, we used 4CAT to retrieve the replies to two tweets from BBC. We used the query “in_reply_to_tweet_id:” and retrieved all replies by setting the ‘tweets to retrieve’ to “0” so we could receive the maximum number of replies. For both BBC’s YouTube video comments and tweets, we ended up ranking them by the highest like/favorite count. We retrieved and analyzed the top 30 comments and tweets.
Furthermore, we conducted a qualitative analysis of datasets collected from YouTube and Twitter to understand the patterns and discourse of the White Paper protest on the two platforms. The analysis was divided into two parts and adopted a comparative approach. First, we compared the data sets of 1,121 videos from YouTube and 728,166 tweets from Twitter to gain an overview of how the political issue was represented on the two platforms. The analysis focused on comparing the frequency of tweets and video creation, content representation, and co-words network analysis.
In the second phase, we conducted a more detailed qualitative analysis of BBC News' YouTube and Twitter comments to see how audiences perceived and discussed the White Paper protest in different platform contexts. We first combined the background of the research topic and the cultural and historical context of China to design an optimized coding scheme based on the findings of Bauer et al (2017). Furthermore, we performed text analysis on the top 30 liked user comments of each video and tweet, then used the coding sheet to classify them. Users were basically classified into four main categories: left-wing, right-wing, neutral, and random. As seen in Table 1, there are seven sub-categories in the ‘left-wing’ category and nine sub-categories in the ‘right-wing’ category. Meanwhile, ‘neutral’ means that it is impossible to distinguish whether the user is left-wing or right-wing, and ‘random’ refers to miscellaneous comments that are not related to either left-wing or right-wing. This coding scheme was based on the values, ideologies, parties, etc. that were conveyed in the user comments.
Table 1. Coding Scheme for YouTube Comment and Tweets Analysis
To get an overview of to what extent Twitter and YouTube have been involved in the discussions on the White Paper Movement, we have created a general timeline overview of
which days the most content for the query "china protest" was uploaded to the platforms. These overviews are visualized in figure 1. In general, there were way more discussions conducted on Twitter than on YouTube. Ranging from November 24th to December 2nd, we got a total of 728,166 tweets compared to 1,121 results caught on YouTube. On Twitter, as we can see in figure 1, the date which shows the largest number of tweets published for our query was November 28th, reaching 243,822 results in our dataset. Figure 1 also shows that most YouTube videos that were uploaded for the keyword "china protest" were on November 29th with 278 videos produced. Interestingly enough, the peak day of discussions on Twitter was one day earlier than it was on YouTube.
Figure 1: Total number of Twitter discussions and YouTube videos per day
We then created a co-word network for our Twitter dataset for the queries "china protest" and "china protests", which is presented in figure 2. This network highlights the number of times certain words have been used together in the tweets of our dataset. The bigger the node is in the network, the more often this specific word was mentioned in our dataset of tweets. Moreover, each of the different colors represents different sub-conversations on the mentioned topics.
What we see is that there are five different main sub-conversations. The first is presented in purple and the keywords for this are 'covid', 'lockdowns', 'videos', 'protests', 'lockdown', 'anti-lockdown', 'breaking', 'cities'. This purple sub-conversation is most likely focused on Twitter users expressing their concerns about the strict covid lockdowns in China, and to uncensor China.
We see another sub-conversation in green which covers the words 'censored', '!YouTube', 'coverage', 'allowed', and 'episode', which would relate to news coverage and possibly the censorship in China, meaning the news coverage on the White Paper protests in China is limited.
Another interesting sub-conversation we see is highlighted in blue and covers keywords such as 'expressing', 'rights', 'people', 'protestors', 'stand', 'justin', 'trudeau', 'canadian'. What we can conclude from this is that Twitter users would express that they stand with protestors and that Chinese people deserve human rights. Moreover, there is a conversation between Canadian president Justin Trudeau. This possibly has shown up in our co-word network analysis, because the Canadian president has recently expressed how Chinese people should be allowed to protest and Twitter users are writing about this.
The orange subsection represents trending Twitter hashtags of that time, which can make tweets more relevant when they are incorporated. Twitter users will tag their tweets with these hashtags to get more replies, retweets, favorites, etc. even if it is unrelated to the content of the tweet. It will just make sure more people will see the tweet.
Lastly, the yellow section represents a more extremist discussion, with words such as 'killing', 'age restricted', 'travesty'. The remaining words 'team YouTube', 'reason', 'viewership' and 'foxconn' are more miscellaneous and feel less related.