Explainers
'#StopXinjiang Rumors' report
The CCP’s decentralised disinformation campaign
Fergus Ryan, Ariel Bogle, Albert Zhang and Dr Jacob Wallis
This report analyses two Chinese state-linked networks seeking to influence discourse about Xinjiang across platforms including Twitter and YouTube. This activity targeted the Chinese-speaking diaspora as well as international audiences, sharing content in a variety of languages.
In the datasets we examined, inauthentic and potentially automated accounts using a variety of image and video content shared content aimed at rebutting the evidence of human rights violations against the Uyghur population. Likewise, content was shared using fake Uyghur accounts and other shell accounts promoting video ‘testimonials’ from Uyghurs talking about their happy lives in China.
Twitter has attributed both datasets to the Chinese government, the latter dataset is specifically linked to a company called Changyu Culture, which is connected to the Xinjiang provincial government. This attribution was uncovered by ASPI ICPC in the report Strange bedfellows on Xinjiang: the CCP, fringe media and US social media platforms.
The Chinese party-state and influence campaigns
The Chinese party-state continues to experiment with approaches to shape online political discourse, particularly on those topics that have the potential to disrupt its strategic objectives. International criticism of systematic abuses of human rights in the Xinjiang region is a topic about which the CCP is acutely sensitive.
There has been an effort to reframe international debate about human rights to assert the perception of moral equivalence between the CCP’s domestic policies in Xinjiang and human rights issues in democratic states, particularly the US.
We see that effort to reframe international debate about human rights continuing in these most recent datasets. This shift also highlighted that CCP information operations deployed on US social media platforms could be increasingly entrepreneurial and agile in shifting focus to take advantage of strategic opportunities in the information domain.
Methodology
This analysis uses a quantitative analysis of Twitter data as well as qualitative analysis of tweet content. In addition, it examines independently identified accounts and content on Twitter, YouTube and Douyin, among other platforms, that appear likely to be related to the network.
The Twitter takedown data
In both datasets, most of the tweeting activity seeking to deny human rights abuses in Xinjiang appears to have started around 2020. In the CNHU dataset, accounts appear to have been created for the purpose of disseminating Xinjiang-related material and began tweeting in April 2019 before ramping up activity in January 2021. That spike in activity aligns with the coordinated targeting of efforts to discredit the BBC that ASPI has previously identified. While some accounts in the CNCC dataset may have originally had a commercial utility, they were probably repurposed some time before 19 June 2020 (the date of the first tweet mentioning Xinjiang and Uyghurs in the dataset) and shifted to posting Xinjiang-related content.
Dataset 1: CNHU
The CNHU network shared a range of content aimed broadly at pushing back against allegations and evidence of human rights abuses in Xinjiang, often in three overarching categories:
- content that aimed to reframe the conversation by presenting video footage of ‘happy’ Uyghurs participating in vocational training in Xinjiang, as well as state media and government events promoting that content
- content that aimed to counter specific allegations made by foreign media, researchers and governments about Xinjiang, focusing on the foreign individuals or entities, not the abuses in Xinjiang
- content created by third parties—including foreign diplomats, journalists and visitors—that was presented as organic but that may have been created as part of curated state-sponsored events or tours.
Content shared by the network promoted stories about Uyghurs living ‘peaceful and happy lives’. In total, more than 1,000 screenshots of China Daily articles on this theme were shared. For example, one screenshot was of a China Daily article about a video series depicting ‘graduates’ from what Chinese officials describe as ‘vocational education and training centres’ in the region.
Dataset 2: CNCC
The CNCC dataset contained a considerable amount of spam and porn, as well as content linked to Korean music and television. Thirteen of the top 20 hashtags referred to Korean television shows, and in particular a drama series titled ‘It’s Okay to Not Be Okay’ (사이코지만 괜찮아). While there was a small amount of content regarding Hong Kong and other issues, most of the non-spam content related to Xinjiang. Much of it sought to present ‘testimonials’ from Uyghurs talking about their happy lives in China and about Covid-19 responses in Xinjiang.
Conclusion:
The two datasets that we analyse in this report demonstrate that international criticism of CCP policy in Xinjiang continues to be acutely sensitive for the party-state. Within the data we see overlaps that reflect different strands of pro-CCP online and offline influence activity.
There are multiple intersections in the data that suggest coordination across the party-state’s propaganda assets. Some of this is clearly directly coordinated, for example where we see this covert information operation’s interactions with, and reciprocal amplification of, the party’s state and local media.
Other interactions with the party’s propaganda assets, however, may be more opportunistic (for example, the engagement with prominent pro-CCP social media influencers and diplomats). Yet, cumulatively, they point to ecosystem building that creates a propaganda system for projecting the party’s discourse power into international audiences.
This creates challenges for the information operations research community, as well as the security teams at the social media platforms, because of the importance of being able to disambiguate distinct sets of inauthentic activity from one another in order to provide nuanced analysis and effective countermeasures.