How YouTube Mediates Current Events: In the Case of the White Paper Movement

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.

1. Introduction

Background

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.

Theoretical framework

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.

3. Methodology

Data collection

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.

Data Processing

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

4. Findings

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.

Figure 2: Co-word network on the Twitter dataset

Next, we analyzed the comment sections under two BBC YouTube videos and two BBC tweets corresponding to the same content as the YouTube videos. For the first BBC video, “Blank paper becomes a symbol of China’s protests”, each of the top 30 most-liked comments on YouTube and Twitter were classified whether they fell under the following categories: ‘left-wing’, ‘right-wing’, ‘neutral’, or ‘random’.

As presented in Table 2, a lot of comments were left-wing, particularly standing in solidarity with the protestors since a total of 18 comments were found. People in this comment section were supporting protests and wished them safety. For instance, a commenter shared their own personal experience and sent the protestors well wishes: “I spent many years in China. I can easily say that Chinese people are generally very friendly, kind,helpful and beautiful people . I always felt like these people definitely deserve more than they’ve been given. I hope everything gets better and better for them. I love you guys; stay safe and strong!”

Meanwhile, a total of 4 comments were also classified as left-wing, specifically discussing communism and justice in relation to the protests. For example, a commenter mentioned resisting the government: “When the government is not letting its citizens know of the plan forward, they are expecting absolute obedience and compliance.” On the other hand, a total of 2 comments were counted as right-wing, particularly discussions that focus on right-wing radicalism and xenophobia. Additionally, there were 5 comments that fell under the category of random, which means that they did not mention any keywords related to left-wing or right-wing topics.

Table 2: Table for top YouTube comments under "Blank paper becomes symbol of China's protests - BBC News" (BBC News, 2022)

The first BBC tweet we examined was about "Blank paper becomes a symbol of China's protests" (similar content as the YouTube video). As seen in Table 3, 10 of the 30 comments were dominated by left-wing content, with the tendency of the commenters to support the protesters and agree that protesters can promote social change through the act of protest. Their comments were relatively short, for example, "People all over the world should help them with this A4 revolution".

However, of the 30 results, 10 comments accused the BBC and the British media of reporting toxic news. The content of questioning BBC and the British media of unjustly reporting the fact is similar to the Chinese official response to the White Paper protest by Foreign Ministry Spokesperson Hua Chunying on Twitter on 9 November, which accuses BBC of 'making news' instead of 'reporting news'; “official UK comment on what happened in Shanghai shows nothing but usual hypocrisy and double standards. If the UK gov respects media freedom and freedom to protest then why did it obstruct and assault the Chinese journalist when she was just asking questions and expressing her legitimate views at a side event of the UK Conservative Party’s annual conference and later even claim her guilty? That’s hypocrisy and double standards.” Meanwhile, 7 of the 10 comments in our results have a clear xenophobic stance. For example, one said: "Can't imagine the BBC who have created so many wonderful art and scientific episodes is now making up a completely fake history with their malevolence. Shame on You, BBC!" 2 of them even questioned whether the events were real.

In addition, 11 comments fell into the random category, meaning they did not mention any keywords related to left-wing or right-wing topics.

Table 3: Table for top Twitter replies under "Blank paper becomes symbol of China's protests - BBC News" (BBC News, 2022)

The second YouTube video we analyzed was another one of BBC's videos, titled as "Protestors urge China's President Xi to resign over Covid restrictions". Again, we collected a dataset for the top 30 most-liked comments under this video. Table 4 visualizes how the comments for this YouTube video were coded.

Strikingly enough, the top 30 comments were all left-winged comments and mostly supportive of the Chinese protestors. The biggest sub-category for this dataset was those of solidarity nature; the commenters under this video felt very proud of the Chinese protesters and there was a great sense of compassion for them, saying things such as "[...] I am so proud of those who have the courage to fight against tyranny, corruption and stupid policies [...]", "[...] they are so brave and respectful", and "I am proud of these people, knowingly how risky this could be [...]".

The second largest sub-category was 'justice'. This indicates that commenters expressed their desire for justice for the Chinese people. Some comments were the following: "[...] Please pray for everyone who fight for their freedom [...]", "For all that are confused - this is not actually about covid. For most people, this is about freedom from government control. Not just freedom of movement, but freedom of speech, religious freedom, freedom to protest, etc.", "[...] They deserve the same rights and freedoms as everyone else [...]".

The smaller left-wing sub-categories were those of ‘communism’, ‘politicians’, and ‘socialism’, with a single, or just two tweets each. These tweets would mention political perspectives, and therefore we have coded them as such, for instance "Time to get rid of Xitler", "As a person lived in china,i want to say,compared with other country,china is more dark in politics", "It's tiannanmen all over again".

Table 4: Table for coded YouTube comments under "Protestors urge China's President Xi to resign over Covid restrictions" (BBC News, 2022)

The second BBC tweet we then examined was about "Protestors urge China's President Xi to resign over Covid restrictions" (similar content as the YouTube video). Unlike the previous article we explored, the headline and content highlighted the challenges to China's political organization posed by White Paper protests. We analyzed the top 30 most-liked comments, as shown in table 5.

Out of all of the comments, 20 were identified as left-winged. The largest sub-category was 'politicians', and included 8 comments related to political views. Some comments echoed the BBC's report, urging the Chinese leader to step down. For example, one commenter stated, "Xi Jinping OUT [...] We need democracy! His dictatorship oppresses every Chinese." Another group was accusing the CCP of lacking democracy, with comments such as, "Democratic elections? Why can't I see a vote?" (translated from Chinese), "So-called 'satisfaction' and 'democracy' self-claimed by CCP is a JOKE."

Additionally, 6 comments were coded as 'solidarity'. These comments expressed support with the protesters and the Chinese people, with statements such as "Fuck dictators. Power to THE PEOPLE. People who do not want to be slaves, rise!" and "Chinese people are in deep water [...] let the world see us! Let the world see us!" (translated from Chinese).

Furthermore, 6 comments emphasized the pursuit of facts and justice and were therefore categorized as justice'. For example, one commenter said, "Please put Xi Jinping on trial, urge the Communist Party to step down, and hold the origin of the virus accountable."

On the other hand, only 3 comments expressed right-winged political stands. Two of them were characterized as 'xenophobics,' blaming foreign forces for the occurrence of White Paper protests. For example, one commenter stated, "American CIA trying its best to destabilize china [...]". Moreover, one comment was considered 'patriotism' since it expressed pride in the development of the China Economy and peaceful, stating, "[...] BBC wants you to believe that the Chinese are tired of first-rate infrastructure, rising living standards, and no wars [...]".

Table 5: Table for top Twitter comments under "Protestors urge China's President Xi to resign over Covid restrictions" (BBC News, 2022)

5. Discussion

General overview of BBC’s YouTube and Twitter posts about the White Paper Protest

Through the collected data, it can be told by numbers that Twitter as a platform was more responsive to the White Paper Movement compared to YouTube. However, this can be explained by how platforms are providing different affordances to their users. Regarding Twitter, most of the tweets collected were formed in plain text. While due to the ecology of YouTube, the users must produce a video to pitch their opinions which is more complicated. Therefore, though users were communicating through long conversations in the comment section, the total production of the White Paper Movement on YouTube suggests significantly fewer discussions.

Furthermore, in the majority of the videos, we found the uploaders were news outlets or official news channels told by the channel names such as “...insight”, and “view of...”. Hence, even though YouTube is turning itself into a creator platform, it was not reflected in the case study of the White Paper Movement. In other words, the White Paper Movement was marginalized by individual news creators. In addition, while collecting our data, we noticed that BBC was releasing different reports on Twitter and YouTube. Though the BBC News outlet channel has an open comments section and released news on both platforms, we found that there were more videos on YouTube that BBC had released. For instance, we got a video titled “China protests spread to country’s biggest cities - BBC News” on YouTube, however, a responding report cannot be found on Twitter. The differences in releasing news of BBC also possibly indicate different responses on Twitter and YouTube.

Moreover, the peak day of discussions on the White Paper Movement was one day earlier than YouTube, addressed on November 28th. Hereby, we found the stronger capacity of immediacy carried by Twitter on current events. Again, the sensitivity to corresponding to current issues can be related to the platform affordances. Tweeting rarely costs much time while making a video needs time. Through the comparison, it is fair to say that the simpler the platforms provide their services to the users, the more sensitive it might be to mediating the current issues.

Analyzing BBC’s Comment Section from two YouTube Video

When analyzing the top 30 comments from BBC's YouTube videos “Blank paper becomes a symbol of China’s protests” and “Protestors urge China's President Xi to resign over Covid restrictions”, commenters were mostly supportive of the Chinese protestors. As seen in figure 3, five topics fell under the left-wing category such as ‘solidarity’, ‘justice,’ ‘communism’, ‘socialism’, and ‘politicians’, with the first one receiving the highest percentage of 55% among the five. On the other hand, three topics fell under the right-wing category such as ‘right-wing radicalism’, ‘national socialism’, and ‘xenophobics’. All three right-wing topics received a percentage of 1.7%. Lastly, the third biggest topic overall was the ‘random’ category with a percentage of 8.3%.

Figure 3. Pie chart of total coding scheme results from both BBC YouTube videos

While YouTube’s comment section provides people the opportunity to share their own thoughts and political claims about the White Paper protest, it is clear that left-wing ideologies remain superior compared to right-wing ideologies. Many felt optimistic and hopeful about the Chinese people coming together to advocate for themselves. This is a surprise since YouTube’s algorithm is notorious for pushing users to the right side of the political spectrum (Brown et al., 2022). However, a reason as to why YouTube comments are mostly left-winged is that people are deliberately searching for content on this movement so they can express their support towards Chinese people; in this case this is happening in the YouTube section. They feel that by leaving their support in the comment section, they could assist the White Paper Movement in their own way. Since a lot of people in the comment sections claim they are Chinese but live abroad, this is their way of standing with their people when they cannot be in their own country along with the protestors.

In addition, in a study conducted by the University of Michigan School of Information, conservative and liberal viewers on YouTube engage in discourse in the comment section, with conservatives commenting on left-leaning videos twice as much as liberals commenting on right-leaning videos (Wu & Resnick, 2021). Indeed, both parties were present in BBC’s YouTube videos regarding the White Paper protest. However, unlike Wu & Resnick’s study, we found that the top comments contained keywords that primarily supported left-wing politics. While YouTube generally favors right-wing agenda, the content discussed in both BBC videos favors the left a little bit more compared to the right. The videos highlight people from the communist party and working-class background and the tragedies they have experienced due to the strict lockdowns. BBC also emphasized the public’s desire to advocate for freedom and political change, applauding their bravery to risk their lives during the protest. Ultimately, the top 30 comments in our study showed that while the contents of BBC’s videos on the White Paper protest were more left leaning, the comments were not right-wing as initially expected––left-wing ideologies remained dominant instead.

Analyzing BBC’s Reply Section from two Tweets

Based on the findings of analyzing the top 30 replies from BBC's Tweet "Blank paper becomes a symbol of China’s protests" and "Protestors urge China's President Xi to resign over Covid restrictions", our study suggests that Twitter has more potential to become a public sphere for expressing left-wing political views and reinforcing collective identity. As seen in figure 4, four topics fell under the left-wing category such as ‘justice’, ‘solidarity, ‘politicians’, and ‘communism’, with the first one receiving the highest percentage of 18.6% among the four. On the other hand, three topics fell under the right-wing category such as ‘xenophobics’, ‘patriotism’, and ‘national socialism’, with the first one receiving the highest percentage of 15.3% among the three. On the other hand, 8.5% of Twitter replies fell under the neutral category while 22% fell under the random category.

Figure 4. Pie chart of total coding scheme results from both BBC Tweets

However, it is important to note that despite the diversity of views present in the replies, the majority of commentators still expressed left-winged political stands, and the largest sub-category was identified as 'justice' and 'solidarity'. Consistent with previous findings, our findings also found that Twitter can promote the construction of individual and collective identities in protests. As noted by Jost et al. (2018), users convey concerns about fairness and stand in solidarity with protesters through comments, which facilitates interpersonal feedback, and peer acceptance and further reinforces collective norms.

Furthermore, comments that were identified as 'politicians' and 'communism' mostly expressed political views echoing the news, mainly dissenting and anti-establishment views. This could be partly due to the context of the BBC report, which involved protesters against China's zero-covid restriction strategy and anti-Xi Jinping sentiment. Considering political dissent was always banned in mainland China, such findings re-emphasized what Lee et al. (2015) have previously mentioned, which is that social media platforms have undertaken the role of the insurgent public sphere (IPS), where people were able to participate in action against the establishment and political authorities. On the other hand, it is necessary to consider the impact of platform algorithms and echo chamber effects. As Bruns (2019) points out, homogeneity can be easily observed in contemporary communicative spaces, where algorithmic shaping and personalization of news feeds will produce information that users are more inclined to see and continually reinforce their pre-existing beliefs. Even though our result appears as a collision of diverse perspectives, it may actually intensify inherent ideology and political polarization.

On the other hand, as a minority, Twitter has a slightly larger share of comments from right-wing, neutral and random positions compared to the YouTube community. Xenopobic discourses have the largest share of right-wing comments, and even the second-largest proportion of all results. Since the outbreak of Covid-19, previous studies have proven that Twitter has shown a resurgence of online racism and xenophobia trend (Dubey, 2020; Lee, 2021). In the case of two BBC tweets on the White Paper protest, 15.3% of comments showed a xenophobic stance, with commenters accusing foreign forces of interfering in China's internal affairs and stigmatizing China's anti-epidemic efforts. Sternisko et al. (2023) attribute it to a kind of national narcissism, a defensive belief in the greatness of one’s nation that requires external recognition, which is positively related to the readiness to believe and disseminate Covid-19 conspiracy theories.

6. Conclusion

This paper uses the BBC's report on the White Paper protest as an example to explore the similarities and differences between the political debate and discursive spaces of YouTube and Twitter.

We quantitatively analyzed the discussion of the 'China protest' on both platforms and found that the number of results on Twitter was 700 times higher than on YouTube and that the peak day of discussion on Twitter was one day earlier than on YouTube. Twitter is a more active and real-time platform in terms of discussions and debates on the White Paper protest due to factors such as the affordance of the platform making it easier for users to post political debates.

Through a qualitative analysis of the content of comments on the case videos, we found that YouTube and Twitter users formed different communities of political debate in response to homogeneous video content about the White Paper protest. Twitter had a higher frequency of content publishing and had a presence of right-wing topics such as xenophobia, patriotism, and national socialism, while YouTube had a higher presence of left-wing topics such as solidarity and justice. While both communities on YouTube and Twitter are dominated by left-wing commentary, YouTube forms the space for a far predominantly left-wing discourse space.

Finally, there are two limitations to point out for future researchers. First, this study primarily focused on YouTube video content and tweets by BBC, filtering out other kinds of social platforms and news outlets that also discuss the White Paper Protest. Second, this research exclusively looked into the top 30 liked comments and replies from each YouTube video and tweet. Analyzing a larger pool of data and examining other metrics (e.g. reply or retweet count) would be something to consider. However, receiving a high number of likes demonstrates that a lot of users resonate and agree with the author’s point of view; in this regard, the comments and replies we analyzed are considered to be representative, thus our study is still reliable and valid.

Datasets

Overall dataset

● YouTube: https://docs.google.com/spreadsheets/d/1Zs-MDQtjeanXpnVe_O3cGud0LE-NloMLr54nTID_8Ug/edit#gid=963431163

● Twitter: https://drive.google.com/file/d/1ZoGhYPQaVCZXHPvRcFLfVC_DwfXx7081/view?usp=sharing

BBC’s “Blank paper becomes a symbol of China’s protests”

● Twitter: https://twitter.com/BBCWorld/status/1597157133040099329

○ Twitter comment dataset: https://docs.google.com/spreadsheets/d/1V5EnpsSm8QduEsLp9jZS9Rq6P-vjCTOFvrYDHPJGst4/edit?usp=sharing

● YouTube: https://www.!YouTube.com/watch?v=IbdCaaW2cDc

○ YouTube comment dataset: https://docs.google.com/spreadsheets/d/1rh3b6shDUdR0cLW8Rlt0ghy0fCSqIUsGFFlskx1f8kw/edit#gid=1597923079

BBC’s “Protestors urge China's President Xi to resign over Covid restrictions”

● Twitter: https://twitter.com/bbcworld/status/1596818352009273344

○ Twitter comment dataset: https://docs.google.com/spreadsheets/d/1vvCSSee9EKT_ED43MXsB9e6jlPixZfsKgLSJJdK3vXI/edit?usp=sharing

● YouTube: https://www.!YouTube.com/watch?v=ij6dQ5_z3lE28

○ YouTube comment dataset: https://docs.google.com/spreadsheets/d/1yb9v7L5Dnhqzx1b6XEcCaCGamtVhDp0SFtwYucNFWIU/edit?usp=sharing

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-- BernRieder - 30 Jan 2023