Chinese Douyin Sentiment about the War in Ukraine

Engagement surrounding 'culture war' narratives

Team Members

Kefeng Cao, Jinru Dong, Xinzi Xu, Xinwen Xu, Richard Rogers

Contents

1. Introduction

This research focuses on the distribution of information about the Russia- Ukraine conflict through Douyin, the online short-video platform also known as the Chinese TikTok. It is a project that analyzes the public perception of the conflict as viewed through the social media platform.

In order to study Douyin, we formulated a list of pro-Russian and pro- Ukrainian keywords and queried them via the search function. We subsequently scraped the returns, some 1,000 in total. We then compared the videos containing the pro-Russian and pro-Ukrainian hashtags, and found that the engagement with pro-Russian videos is much greater than pro- Ukrainian, meanwhile the term Russian-Ukraine conflict is mentioned far more often than Russian-Ukraine war. Finally, the key phrase 'counter hegemony' has emerged in the videos with pro-Russia hashtags.

3. Research Questions

Q1: How to characterize Douyin content creators' sentiment about the war in Ukraine?

SubQ2: How do people engage and share specific narratives on Douyin?

SubQ3: How does the hashtag of 'justice and world peace' in videos of both Pro-Ukraine and Pro-Russian hashtags present various characterizations?

4. Methodology and initial datasets

We collect our data by looking at queries for two general war-related keywords in the 'search' function. We then create our corpus by extracting hashtags from the top 1000 videos and ranking the hashtags by frequency of appearance. We are able to identify the main narratives (pro-Russian and pro-Ukrainian) distributed on Douyin. There are very few pro-Ukrainian hashtags overall, therefore, we retained all hashtags we could find that support Ukraine. Ultimately, we make a graph that contains 13 pro-Russian and 5 pro-Ukrainian hashtags in Chinese Mandarin and corresponding English translations. The next step is to observe user engagement with the narratives of the war in Ukraine by looking at the sum of the likes, reposts, comments and favorites from the created dataset.

To visualise our findings, we use a word cloud generator to indicate the engagement of pro-Russian and pro-Ukrainian, in which the larger the font size the greater the engagement. Then we undertook a co-word network analysis to display the centality, size and linkages of the keywords. Every node represents an individual keyword and every line represents an association. We are analysing how each keyword is connected. The amount of connections each keyword has influences each node's diameter. The more connections, the larger the node. "Russia" is in the centre of the map, which demonstrates its centrality. It is another confirmation that the pro-Russian keywords and hashtags have gained greater resonance. So in a way, most of the keywords are connected to the node, "Russia". This can be because it is directly connected, but it can also be indirectly connected.

5. Findings

douyin-sml.png
  1. At the data collection stage, it was challenging to find obvious pro-Ukrainian hashtags in Douyin.
  2. The world clouds that we plotted to analyse the data showed that the engagement with pro-Russian videos is indeed much greater than with pro-Ukrainian ones. Even when, eventually, we found videos with pro-Ukrainian hashtags most videos concerning the topic were still pro-Russian.
  3. Out of all the pro-Ukrainian hashtags, the ones with clear, positive sentiments (such as “Come on Ukraine”) paled in comparison with the significance of the pro-Russian hashtags.
  4. We saw a key phrase “counter hegemony” emerge in a video with pro-Russian content (and hashtags). We observed different understandings of “world peace” as presented in videos with pro-Russian and pro-Ukrainian hashtags. The pro-Ukraine videos presented “world peace” as “stopping the war”, whereas pro-Russian videos presented “countering the hegemony” as a way of achieving and maintaining world peace.

6. Discussion

7. Conclusions

8. References

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Topic revision: r2 - 13 Feb 2023, KamilaKoronska
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