The CCP's Digital Charm Offensive:
How TikTok's Search Algorithm and
Pro-China Influence Networks
Indoctrinate GenZ Users in the
United States

Joel Finkelstein, Ph.D.
Chief Science Officer and Director, Network Contagion Research Institute; SeniorResearch Fellow, Miller Center for Community Protection and Resilience, Rutgers
University

Lee Jussim, Ph.D.
Former Chair, Distinguished Professor,
Department of Psychology, Rutgers University, Chair of Anthropology

John Farmer
Former New Jersey State Attorney General and Senior Counsel, 9/11 Commission;
Director, Miller Center for Community Protection and Resilience, Rutgers University

Alex Goldenberg
Lead Intelligence Analyst, Network Contagion Research Institute; Research Fellow,
Miller Center for Community Protection and Resilience, Rutgers University

Jacob Zucker
Senior Intelligence Analyst, Network Contagion Research Institute

Danit Finkelstein
Ph.D. Student, Social Psychology, Rutgers University

Sonia Yanovsky
Ph.D. Student, Social Psychology, Rutgers University

Analysts
Anisha Jagdeep
Ankita Jagdeep
Collin Vasko
Mikey Sohn

PRESENTED BY

Network Contagion Research Institute Intelligence Report

1

TABLE OF CONTENTSEXECUTIVE SUMMARY ................................................................................................................ 2
BLUF (Bottom Line Up Front) ......................................................................................................... 3
BACKGROUND .............................................................................................................................. 3
METHODOLOGY............................................................................................................................ 6
DATA ANALYSIS ............................................................................................................................ 9SURVEY DATA ............................................................................................................................. 20
CONCLUSIONS AND RECOMMENDATIONS ............................................................................ 22
LIMITATIONS................................................................................................................................ 23
APPENDIX I .................................................................................................................................. 24
APPENDIX II ................................................................................................................................. 27
APPENDIX III ................................................................................................................................ 30

Network Contagion Research Institute Intelligence Report

2

EXECUTIVE SUMMARYIn December 2023, the Network Contagion Research Institute (NCRI) published an initial reportthat found a strong possibility of content on TikTok being either amplified or suppressed basedon its alignment with the interests of the Chinese Government. The report concluded with awarning: “should future research find that TikTok users exhibit attitudes and assessments ofworld events aligned with these information distortions, democracies will need to consider
appropriate countermeasures to protect information integrity and mitigate potential real-world
impacts.” These findings underscored the urgency of investigating the specific mechanisms
and broader implications of potential algorithmic manipulations.
While our initial study shed light on the potential for content manipulation on TikTok, it did not
explore whether specific moderation algorithms or practices are used to suppress topics
sensitive to the Chinese Communist Party (CCP). Moreover, it did not compare the nature andimpact of pro- and anti-CCP content across other major social media platforms like Instagramand YouTube.
This current report addresses these gaps by providing a more comprehensive analysis of
TikTok's moderation practices, examining the nature and prevalence of CCP-sensitive content,
and evaluating how different platforms handle such content. Furthermore, this study explores
the association between social media platform usage and pro-CCP attitudes among users. By
combining user journey data, survey research, and cross-platform comparisons, we uncover theextent to which TikTok and other platforms may influence user perceptions and behaviors infavor of the CCP.
The publication of this research precedes the delivery of oral arguments for TikTok Inc. and
ByteDance Ltd. v. Merrick B. Garland, a federal court case that has subjected the TikTokalgorithm to intensive new scrutiny. As detailed in the DOJ’s response to TikTok’s lawsuit,“covert content manipulation” is an area of legal interest for the DOJ, not to mention its
relevance to the broader U.S. intelligence and national security community. That said, within the
DOJ response it is noted that definitively proving covert content manipulation by the Chinese
government remains a challenge. As FBI Director Wray testified to in multiple hearings, “[t]his
sort of manipulation of the algorithm would be difficult to detect…"1 This research gap sets thestage for our findings, which, while not definitive proof of state orchestration, present
compelling and strong circumstantial evidence of TikTok’s covert content manipulation.
In response to the DOJ's acknowledgment of challenges in proving manipulation by the Chinese
government, our findings present the following circumstantial evidence:• Algorithmic Bias: TikTok's algorithms consistently amplify pro-CCP content andsuppress anti-CCP narratives.
• Content Origination: Much of the pro-CCP content originates from state-linked entities,
including media outlets and influencers.
1 U.S. Department of Justice. (2024, July 26). Response to TikTok lawsuit, pp. 38. DocumentCloud.

https://www.documentcloud.org/documents/25017864-72624-doj-response-to-tiktok-lawsuitNetwork Contagion Research Institute Intelligence Report•

3

Psychological Impact: Survey data shows significant shifts in user attitudes towards
China, especially among heavy TikTok users, indicating successful indoctrination.

These points collectively indicate a systematic manipulation of information, suggesting that
propaganda produced by state actors and orchestrated through assets owned or influenced bythem shapes user perceptions at a massive scale.

BLUF (Bottom Line Up Front)
1. Amplification of Pro-China and Irrelevant Content: TikTok amplifies frontier influencers(travel and lifestyle content accounts) and irrelevant or clickbait material, to crowd out
discussion of CCP-driven ethnic genocide and human rights abuses on its platform.
2. Suppression of Anti-China Content: TikTok's moderation algorithms significantly
augment this suppression. The views-to-likes ratio for anti-China content on TikTok
was 87% lower than pro-China content even though the content was liked nearly twice
as much.
3. Cross-Platform Influence Operations: The CCP also uses frontier influencers and stateaffiliated media to disseminate pro-China narratives to crowd out discussion of humanrights abuses on Instagram and YouTube with tourism and culture content.
4. Psychological Indoctrination: A psychological survey of Americans (n=1214) showsthat, among the platforms studied, TikTok screentime positively and uniquely predictedfavorability towards China's human rights record. Notably, heavy users of TikTok (i.e.,
those with >3 hours of daily screentime) demonstrated a roughly 50% increase in proChina attitudes compared to non-users. This suggests that TikTok's content may
contribute to psychological manipulation of users, aligning with the CCP’s strategic
objective of shaping favorable perceptions among young audiences.
5. Strategic Assessment: NCRI assesses that the CCP is deploying algorithmic
manipulation in combination with prolific information operations to impact user beliefs
and behaviors on a massive scale and that these efforts prove highly successful on
TikTok in particular. These findings underscore the urgent need for transparent
regulation of social media algorithms, or even the creation of a public trust funded by theplatforms themselves to safeguard democratic values and free will.

BACKGROUNDThis study continues the NCRI’s analysis of algorithmic bias. Therein, NCRI compared hashtag
ratios between TikTok and Instagram for terms that are sensitive issues domestically andexternally for the CCP. Though the study was preliminary, it concluded that there exists “astrong possibility that TikTok systematically promotes or demotes content on the basis ofwhether it is aligned with or opposed to the interests of the Chinese Government.” 2 In the wake2

Network Contagion Research Institute. (2023, Dec 21). A Tik-Tok-ing Timebomb: How TikTok's Global Platform
Anomalies Align with the Chinese Communist Party's Geostrategic Objectives.
https://networkcontagion.us/reports/12-21-23-a-tik-tok-in-timebomb-how-tiktoks-global-platform-anomalies-alignwith-the-chinese-communist-partys-geostrategic-objectives/

Network Contagion Research Institute Intelligence Report4of publication, TikTok disabled its hashtag measurement functionality in a move that renders
our December research findings irreplicable.
Given these platform changes undertaken in recent months, the methodological basis of thiscurrent study are user journeys. The user journey methodology recreates the on-platform
experience of a newly created, organic user. As is discussed at length the following section, theaccounts created for this study on TikTok, Instagram, and YouTube mimicked 16-year-old users
located in the United States. These accounts were used to test the three platforms’ searchalgorithm results when inputting keywords of hot-button issues for the CCP domestically. An
advantage of user journeys over web scraping or paginated mass collection techniques is thatmanual operation of dedicated sock puppet accounts more closely reproduces the search
results and content flow that an organic user is likely to encounter.
In recent years, user journeys are becoming an increasingly widespread technique for internet
and social media algorithmic researchers. AI Forensics, a European non-profit research
institute, partnered with Amnesty International in 2023 to conduct a user journey-driven analysis
to study how the TikTok algorithm negatively influences teen mental health. 3 AI forensics’
primary research design relied on automatic collection of post data via 40 sock puppets, pairedwith a manual audit that strove to heuristically confirm findings. The Amnesty International
report concluded that through TikTok’s algorithm, “children and young people are being
exposed to a system which turns their psychological vulnerabilities into a means to maximize“user engagement.”4
Following the October 7th attack by Hamas and the eruption of conflict across the Middle East,
researchers with the Wall Street Journal (WSJ) embraced user journeys as a way to gaugealgorithmic bias vis-a-vis the war in Gaza. The WSJ team created 8 sock puppet accounts
posing as 13-year-old American teens and categorized conflict-related content that was served
up as either pro-Palestinian or pro-Israel. The WSJ found that:“Similarly to other social-media platforms, much of the war content TikTok served theaccounts was pro-Palestinian—accounting for 59% of the more than 4,800 videos servedto the bots that the Journal reviewed and deemed relevant to the conflict or war. Some
15% of those shown were pro-Israel.”5This finding that four times as much pro-Palestinian conflict-centric content was served relative
to pro-Israel content likewise coincides with the CCP’s geopolitical interests in the Middle East,
which have notably chilled towards Israel while warming towards the Palestinians and other

3 AIForensics. (2023). Unmasking TikTok's influence on youth mental health: A comprehensive investigation.

https://aiforensics.org/work/tiktok-kids
4
Amnesty International. (2021). Driven into the darkness: How TikTok's 'For You' feed encourages self-harm andsuicidal ideation (p. 64). https://aiforensics.org/uploads/POL_4073502023_ENGLISH_6ace7064be.pdf
5 Schechner, S., Barry, R., Wells, G., French, J., Whitton, B., & Dapena, K. (2023, Dec 22). How TikTok brings war home
to your child. The Wall Street Journal. https://www.wsj.com/tech/tiktok-israel-gaza-hamas-war-a5dfa0ee

Network Contagion Research Institute Intelligence Report

5international backers. Though TikTok has denied claims of anti-Israel bias, mounting researchon its coverage of the ongoing Middle East conflict has sparked the interest of U.S. lawmakers.6
The analysis undertaken in this current study assumes a similar experimental design to that of
the WSJ though it focuses on domestic Chinese bellwether issues rather than global
geopolitical concerns.
While user journeys represent a powerful tool for exposing algorithmic bias on TikTok, thescope of CCP digital influence operations is significantly broader and cross-platform in nature.
One such influence vector the findings of this report illustrate is “frontier influencers,” which aredefined by the Australian Strategic Policy Institute (ASPI) as:“[An] increasingly popular set of YouTube accounts that feature mainly female Chinabased ethnic-minority influencers from the troubled frontier regions of Xinjiang, Tibet
and Inner Mongolia, hereafter referred to as ‘frontier influencers’ or ‘frontier accounts.’”7
As ASPI details, the CCP weaponizes prima facia benign travel vloggers to manage perceptionsof China abroad, particularly as relates to restive border areas like Tibet, Xinjiang, and Inner
Mongolia. As the report remarks, one tactic employed by these frontier influencers is ensuring“that state-backed content ranks well in search results because search-engine algorithms tend
to prioritize fresh content and channels that post regularly.”8 As will be discussed below, ouruser journey findings directly confirm ASPI’s 2021 findings.
Alongside the positive amplification performed by third-party frontier influencers and high-reach
official CCP media accounts, hashtag hijacking is a technique that Beijing has employed tosuppress undesired narratives. Hashtag hijacking is the deliberate attachment of trending
hashtags or keywords to unrelated content or posts to 1) boost engagement for the network ofbots performing the hijacking, and 2) suppress undesirable content bearing a target hashtag byflooding the social discourse with white noise (also bearing the target hashtag or keyword). A
dramatic example of hashtag hijacking was on display during the CCP’s 2022 crackdown on theprotests against its zero-COVID policy that erupted across the mainland. As reported by The
Guardian:“...Networks of coordinated bot accounts were targeting non-Chinese social media
platforms to crowd out genuine posts about the demonstrations with spam content andby hijacking hashtags of names of Chinese cities. It said China’s government was most
likely to be behind the tactic.” 96 Sung, M. (2023, Nov 14). TikTok disputes claims of anti-Israel bias amid calls to ban the app. TechCrunch.

https://techcrunch.com/2023/11/14/tiktok-disputes-claims-of-anti-israel-bias-amid-calls-to-ban-the-app-palestine/
7 Ryan, F., Impiombato, D., & Pai, H.-T. (2022, Oct 20). Frontier influencers: The new face of China's propaganda.
Australian Strategic Policy Institute. https://www.aspi.org.au/report/frontier-influencers
8Ibid.
9 Milmo, D. (2022, Dec 4). China accused of flooding social media with spam to crowd out protest news. The
Guardian. https://www.theguardian.com/world/2022/dec/04/china-accused-of-flooding-social-media-spam-covidprotests

Network Contagion Research Institute Intelligence Report

6

The intermingled operationalization of algorithmic manipulation on TikTok (top-down approach)alongside networks of frontier influencers, tourist vloggers, high-reach CCP-sponsored media
assets, and hashtag hijacking on US-based platforms (bottom-up approach) should be viewed
within the broader framework of the CCP’s doctrine of influence and information warfare. As
characterized by the French Armed Forces’ Institute for Strategic Research, the CCP’s
information operations strive to achieve two primary objectives: 1) “seduce and subjugate
foreign audiences by painting China in a positive light”, 10 and 2) “infiltrate and constrain – a‘harsher’ category of operations that do not involve seducing its opponents but rather bending
them.”11 This study works to advance an understanding about the specific mechanisms driving
the “seduce and subjugate” pillar of China’s information warfare doctrine as observed across
TikTok, Instagram, and YouTube around hot-button issues for Beijing.

METHODOLOGYCollection Methodology
The objective for user journey data collection was to collect ±300 videos for each of four target
keywords (i.e., “Uyghur”, “Xinjiang” “Tibet”, “Tiananmen”) across three different social media
platforms (i.e., TikTok, YouTube, Instagram), producing ±3435 results total, as some searchfeeds stopped serving content before 300 videos per term was reached. These keywords wereselected given their importance in the CCP’s information warfare and propaganda doctrine,
which enshrines projecting a positive image of China both inwards and outwards as a core
objective of its national security agenda. Results on TikTok, a subsidiary of Chinese tech giant
ByteDance, was compared to those on Instagram and YouTube, owned by U.S. companies Meta
and Google, respectively, in order to measure how content served up by the search algorithm
differs between platforms.
The user journey methodology strives to recreate the organic experience a generic new accountwould undergo. All accounts were created with IP addresses located in the USA. Equal numbersof male versus female accounts were created, and all accounts were marked as 16 years of age
to best mimic the organic user experience of an American teenager. Both TikTok and Instagram
collection was performed on mobile Android phones and recorded using a phone screen
recording app called V Recorder, while YouTube collection was performed on a desktop
computer and recorded via a screen recording tool.
In total, 24 accounts were created for the purposes of this study. A new account was createdfor each keyword for each platform to ensure experimental sterility by restricting the personal
identifiers visible to the platform algorithms to 1) IP location and 2) user age. Beyond accountcreation, searching for the target keyword, scrolling through video results, andsaving/bookmarking viewed content, no additional actions were performed that could skew the

10 The Institute for Strategic Research. (p. 413). https://www.irsem.fr/en/index.html
11 Ibid.

Network Contagion Research Institute Intelligence Report7

profile’s search preferences (e.g., no accounts were followed, no prior searches were
performed, no engagements except views and saves were performed).
A standard collection methodology was followed for all keywords across each platform. In
conducting the TikTok data collection, the user began by typing the term into the Search fieldand clicking on the first video that appeared. Subsequently, the user scrolled through eachsubsequent video, saving each one. Every video was played for at least 15 seconds or until thevideo concluded. Upon completing the recording session, the user navigated to the Saved pageon the User Profile to locate all the saved videos from the session. The upload date and the link
for each video were then copied into a spreadsheet.
For Instagram data collection, the user started by entering the term in the Search field andselecting the first post that appeared. The user then scrolled through each subsequent post,saving each one. Videos were played for at least 15 seconds or until they finished, and posts
containing multiple images were fully viewed by swiping through each image. After recording,the user accessed the Saved page on the User Profile to retrieve all saved posts from thesession. The upload date and the link for each post were copied into a spreadsheet.
During YouTube data collection, the user entered the term into the Search field and hovered
over each video in the list, allowing it to play for 15 seconds (excluding YouTube Shorts andvideos that are in playlists). After completing the recording, the user scrolled back to the top of
the list and clicked on each video to copy the upload date and URL into a spreadsheet.

Coding Methodology
Following data collection, the first phase of analysis categorized content as either pro-China,anti-China, neutral, or irrelevant. The search terms analyzed are inherently political for the CCP,
facilitating a clearer and more intuitive coding process than would be possible with broader,more neutral terms such as “China” or “democracy”.
Blind coding12 was performed by two human analysts. In cases where there was a disagreementin the coding determination, a third subject matter expert (SME) was tasked with arbitrating andassigning a final coding category. Given the subtlety of messaging alongside the intermixture of
textual, visual, and audio semiotics that were assessed in making a coding determination, we
believe that human analysis tends to be more accurate and granular than AI or machine-driven
coding.
Despite reliance on human analysts for content coding, our average margin of intercoder
disagreement across all platforms and keywords was remarkably small. The average intercoder
disagreement across all keywords and platforms was measured as 12.81%, with a range of

12 In this context, blind coding connotes that the analysts categorizing video results were isolated from one anotherto prevent conformity bias between their designations. Furthermore, this research was hypothesis agnostic in orderto minimize coder bias.

Network Contagion Research Institute Intelligence Report

818.9% (3.8% to 22.7%). Specific values for each keyword and platform are displayed in thefollowing table:
Intercoder
Disagreement

Tibet

Tiananmen

UyghurXinjiang

TikTok

13.5%

3.8%

7.7%

9%

Instagram

16.3%

14%

8.3%

20.3%

YouTube

21%

12%

4.1%

22.7%

Table 1: Intercoder Disagreement in Content Coding

Our analysis utilized a systematic approach to coding video content, ensuring consistency andaccuracy in categorizing videos. The coding parameters were customized for each keyword,and served as a blueprint for human analysts responsible for the process:Xinjiang●●●●

Pro-China: Official promotional content, frontier influencer content, showcasing ofminorities’ folk customs while whitewashing cultural erasure, idyllic portrayals of rurallife, claims of Western narrative misrepresentation
Anti-China: Content highlighting Uyghurs’ plight in China, calls for boycotts of Chinese
products grown in Xinjiang, Chinese human rights abuses and suppression of internaldissent
Neutral: Personal photos, informational graphics, unbiased news reports, historicalartifacts, consumer goods
Irrelevant: Content unrelated to Xinjiang, apolitical Xinjiang diaspora content

Uyghur●●●●

Pro-China: Highlight Uyghur/Xinjiang folk culture (food, dance, dress, women), frontierinfluencers exploring Xinjiang/Uyghur heartland
Anti-China: Content highlighting Uyghurs’ plight in China, unlawful detention, cultural
erasure, suppression of civil liberties, etc.
Neutral: Diasporic communities, apolitical Uyghur-language songs or media,
professional travel photographers and/or Western tourists unaffiliated with official
operators.
Irrelevant: Content unrelated to Uyghurs

Network Contagion Research Institute Intelligence Report

9

Tibet
●

●

●
●

Pro-China: Official promotional content, state-registered tourism companies, frontierinfluencer content, idyllic portrayals of rural life, echoing the CCP narrative points that
Tibet has been liberated
Anti-China: Mentions of Tibetan liberation, coverage of the exilic government andpolitical statements from the Dalai Lama, videos containing politically charged hashtags
like #freetibet, #SaveTibet and #proudtibetan, footage of protests, and details of Tibetan
cultural erasure by the CCP
Neutral: Informational presentations, unbiased historical content, coverage of Tibetan
Buddhism, its rituals and material culture
Irrelevant: Content unrelated to Tibet, reactions to Tibetan culture, and Tibetan
consumer & folk-art products

Tiananmen●●●●

Pro-China: Patriotic songs, official travel promotions, flag raising and other nationalist
events, denials of the massacre and revisionist historical takes, scenic pictures of thesquare that bear no mention of the massacre
Anti-China: Condemnations of the massacre, commemorations worldwide of themassacre by victims and dissidents, "Tank Man" imagery, memes highlighting the event
Neutral: News coverage of worldwide anniversaries for the massacre, tangential
mentions of Tiananmen
Irrelevant: 13 Content unrelated to Tiananmen Square or the 1989 massacre

DATA ANALYSIS
User Journey Data
User journey data shows patterns consistent with the conclusion that TikTok is suppressing
anti-China content in order to diminish the reach of narratives critical of the CCP (see Figure 1).
For all four search terms examined, TikTok contained the lowest proportion of anti-China
content compared to Instagram of YouTube. Simultaneously, we observed that pro-China andirrelevant content constituted between 61%-93% of search results on TikTok, illustrating how
flattering or distracting content is systematically amplified as a way to subsume the low
proportion anti-China coverage extant on the platform.

13 Note that 8 videos (<3%) served for “Tiananmen” on TikTok contained scenic content of the Tianmen mountains;these were categorized as irrelevant.

Network Contagion Research Institute Intelligence Report

10

Figure 1: Summary of Anti-China Bias

The results of the user journeys performed for “Tiananmen” (Figure 2) support a hypothesis that
TikTok’s search algorithm is suppressing anti-China content while amplifying pro-China andirrelevant content, relative to Instagram and YouTube. For instance, 26.6% of search results on
TikTok for “Tiananmen” were categorized as pro-China, versus 16.3% on Instagram and a mere
7.7% on YouTube.
Conversely, the proportion of anti-China content on YouTube (64.6%) and Instagram (56.3%)
was more than double that observed on TikTok (19.6%). It is also significant that 45.6% ofcontent served on TikTok was flagged as irrelevant compared to <10% for both Instagram andYouTube. This reflects the “seduce and subjugate” strategy outlined in the introduction where
content showing other Chinese cities and natural attractions was used to subsume mentions ofTiananmen.

Network Contagion Research Institute Intelligence Report

11

Figure 2: “Tiananmen” User Journey Summary

Broadly speaking, a similar breakdown of coding categories was observed for the search term
“Tibet” (Figure 3). TikTok contained the highest proportion of pro-China content (albeit only
slightly more than Instagram) and the lowest proportion of anti-China content by far across all
three platforms. Analysis also showed that the proportion of neutral content was similar acrossall three platforms, which we attribute to apolitical mentions of Tibet in relation to Tibetan
Buddhism and spirituality, a subject that enjoys widespread familiarity among the general publicin the West. Overall, however, these findings, again testifies to TikTok’s proclivity to suppress
anti-China content and amplify pro-China content, relative to Instagram and YouTube.

Figure 3: “Tibet” User Journey Summary

Network Contagion Research Institute Intelligence Report12

For the search term “Uyghur” (Figure 4), anti-China content was measured at a mere 10.7% of
TikTok results vs. 19% of YouTube and an overwhelmingly 84% of Instagram results, thereby
testifying to the hypothesis that TikTok is suppressing anti-China content. In contrast, pro-China
content comprised 17% of TikTok data compared to 2.7% of Instagram data yet an unintuitive
49.2% of YouTube results. While at first glance this increase in pro-China content on YouTube
relative to TikTok appears unintuitive, upon closer examination these differences can be
attributed to the strategic cultivation of pro-China media assets on YouTube and Instagram. We
observed that these assets have effectively dominated on-platform search results in what
constitutes an example par excellence of bottom-up influencing on YouTube and Instagram.

Figure 4: “Uyghur” User Journey Summary

This anomalously high proportion of pro-China content on YouTube was driven by the fact that
40% of the total content collected emanated from a single account, @uyghurbeauty. Manual
coding of @uyghurbeauty resulted in categorization as pro-China given its modus of amplifying
folk culture as a means of whitewashing the ongoing Uyghur cultural genocide in Xinjiang. An
in-depth analysis of @uyghurbeauty and similar accounts that are playing a core role in hashtag
keyword/hijacking of discourse around the search term “Uyghur” is presented below in
Appendix I.

Figure 5: @Uyghurbeauty YouTube Profile

Network Contagion Research Institute Intelligence Report

Also of note in the “Uyghur” user journey data set was that 60.3% of all content served by
TikTok’s search algorithm was flagged as irrelevant in contrast to <5% for Instagram andYouTube (see Figure 6). This differential demonstrates how the CCP influencing tactic ofhashtag/keyword hijacking being effectively operationalized in service of the “seduce andsubjugate” strategy.

Figure 6: Irrelevant Content Served for “Uyghur” on TikTok

The user journey data profile for “Xinjiang” (Figure 7) resembles that of “Uyghur” in some key
respects. On TikTok, we found a virtual absence of anti-China content (2.3%) compared to
21.7% on YouTube and 17.3% on Instagram. Also of note is the high proportion of irrelevant
content served to users on TikTok for “Xinjiang,” which was measured at 69.4% of all results.
This finding supports the hypothesis that the TikTok search algorithm systematicallyFigure 7: “Xinjiang” User Journey Summary

13

Network Contagion Research Institute Intelligence Report14

suppresses undesirable anti-China content while flooding search results with irrelevant content,
much of which appeared to be generic clickbait or tourist content portraying other cities orregions of China in a positive or attractive light.
In contrast to the user journey data on the other keywords examined, for “Xinjiang,” a user wasserved more than twice as much pro-China content on both YouTube (52.6%) and Instagram(49.3%) than on TikTok (24%). As was the case of “Uyghur” on YouTube, we attribute thisanomalous finding on Instagram and YouTube to a combination of pro-CCP media assets—both
frontier influencers and official media accounts-- that have successfully dominated the social
discourse around these keywords using a bottom-up approach to subsume search results.
On YouTube, four of the top five most active accounts that figured in search results were official
or semi-official CCP media sources (CGTN, South China Morning Post, ShanghaiEye, and CCTV
Video News Agency; see Figure 8) that enjoy an accumulated subscriber pool of 8M users.
These four users generated 21.7% of all content retrieved for “Xinjiang” and nearly 40% of all
pro-China content on YouTube, thereby testifying to the extent they have successfully
dominated search results for “Xinjiang.”

Figure 8: Pro-China Media Assets on YouTube

In a similar vein, on Instagram the most active account is @wilchinawildchina, which is based in
China and appears to be a frontier influencer with nearly 68K followers. An unofficial fan page(@dilireba_dilmurat) for the Uyghur-Chinese model and CCP brand ambassador Dilraba Dilmurat

Network Contagion Research Institute Intelligence Report

15

ranked as the fourth most active poster, and official state media @cgtn as the fifth most active
poster. The high visibility enjoyed on Instagram and YouTube by either official CCP media
sources or affiliated third parties around discussion of “Xinjiang” testifies to the successful
cultivation and weaponization of social media assets to again achieve the “seduce andsubjugate” pillar of Beijing’s influence doctrine.

Figure 9: Pro-China Media Assets on Instagram

Engagement Metadata
Whereas the first prong of our data analysis shed light on the pro-China bias of TikTok’s searchalgorithm, engagement metadata analysis14 illuminates which category of content (pro-China
vs. anti-China) accrued more engagement (likes and views) from platform users. This allows us
to determine not only what category of content was served most frequently, but what categoryof content was most popular with other users.
As has been widely documented by researchers and marketing experts, the initial engagement
awarded to videos posted on TikTok (views, full-length plays, likes, comments, shares)
influences the extent to which the algorithm exposes the content to successively broader
audiences.15 Despite this accepted rule of thumb, our findings point to the opposite conclusion
than is suggested by the user journey data presented above, namely that though pro-China
content is served up by the TikTok search algorithm much more often, anti-China content
appears far more engaging for organic users.
14

See Appendix III for full metadata engagement metrics. In this study, engagement metadata refers to theengagements (views, likes, comments, shares, etc.) awarded to videos analyzed.
15 Klug, D., Qin, Y., Evans, M., Zhang, B., & Zhang, K. (2022). An empirical investigation of personalization factors on
TikTok. arXiv. https://ar5iv.labs.arxiv.org/html/2201.12271

Network Contagion Research Institute Intelligence Report

Figure 10: TikTok Engagement Metadata

16

Network Contagion Research Institute Intelligence Report17

For three of the four search terms collected on TikTok (excepting Tibet), engagement awarded
to anti-China content was consistently and significantly higher than that given to pro-China
content. This finding suggests that notwithstanding the indications of systematic suppressionof anti-China content on TikTok elucidated above, said anti-China content accrued significantly
higher average likes and views than pro-China content (see Figure 10).
Engagement metadata for Tibet on TikTok showed an inverse finding relative to “Tiananmen,”“Uyghur,” and “Xinjiang” in that pro-China content accrued significantly higher average views
and likes than anti-China content. We partially attribute this skew to a single, highly viral proChina TikTok video collected for “Tibet” that received 19.4M views and 2M likes. As indicated in
the table, this video’s engagement metrics are orders of magnitude above the average observed
across all four search terms and skewed TikTok Tibet results in a markedly pro-China direction.
Interestingly, the account (@rawangm1; see Figure 11) that uploaded the viral clip fits the profile
of a classic frontier influencer—"mainly female China-based ethnic-minority influencers from thetroubled frontier regions...[who project] a false sense of legitimacy and transparency about
China’s frontier regions that party-state media struggle to achieve.”16 The account also shares alink to an ecommerce landing page for Chinese-themed consumer products. Given the lack ofconnection between the product portfolio displayed on the landing page and Tibet or Tibetan
culture, it is possible that the account is leveraging the algorithmic bias that amplifies viralfrontier influencer content to drive affiliate marketing traffic.

Figure 11: Example of Tibetan Frontier Influencer

Returning to the engagement metadata analysis, the highest average likes and views across all
four search terms analyzed on TikTok was granted to irrelevant content, signifying that genericclickbait content consistently attracts higher engagement than politicized content from users

16 Ryan, F., Impiombato, D., & Pai, H.-T. (2022, Oct 20). Frontier influencers: The new face of China's propaganda.

Australian Strategic Policy Institute. https://www.aspi.org.au/report/frontier-influencers

Network Contagion Research Institute Intelligence Report

18(see Figure 12). In sum, TikTok engagement metadata analysis shows that despite TikToksearch algorithm’s tendency to amplify pro-China content and suppress anti-China content:●●

Anti-China content engenders significantly higher user engagement than pro-China
content.
Apolitical (i.e., coded as irrelevant) clickbait receives the overall highest engagement
for all categories measured.

Analysis of engagement metadata for YouTube and Instagram yielded higher average likes andviews (YouTube only) per video for anti-China content. As was observed on TikTok, “Tibet”
remained an outlier on Instagram, and to a lesser extent, on YouTube. The results observed on
YouTube show that a higher average number of likes and views were awarded to anti-China
content across all search terms, the sole exception to this finding being 60K less views for antiChina content mentioning Tibet relative to pro-China content, as shown in the chart below. Alsoof note is that for “Tibet,” “Xinjiang,” and “Uyghur,” irrelevant content accrued a higher averageof likes and views than either pro- or anti-China content. These findings parallel those observedon TikTok, and suggests that on video-centric platforms (YouTube & TikTok), generic clickbait
content draws higher user engagement than politicized content.
On Instagram, average likes for pro-China content mentioning Tibet were 1.8x higher than
average likes awarded to anti-China content. That said, for the other three search terms
analyzed (Tiananmen, Xinjiang, Uyghur), anti-China content received 2.8x, 40.8x, and 8.3x more
average likes, respectively, than pro-China content. Though Instagram engagement metadata
examined herein was limited to likes (as views for post data is not accessible), the overall
results closely mirrored that observed on TikTok, i.e., anti-China content generally garners
significantly higher user engagement than pro-China content.
We found that though the ratio of average views per likes on TikTok was nearly twice as high for
pro-China content (15.41 vs. 8.22), for three out of the four search terms, average views andlikes were significantly higher for anti-China content. The key takeaway from this ratio is that
despite the TikTok search algorithm's pro-China bias, users favored anti-China content withlikes and views over pro-China content in three out of four keywords analyzed (see Appendix
III).
In terms of average views per like across all search terms for YouTube, pro-China content
received a value of 44.69 whereas anti-China content received 39.16. As was the case for
TikTok, pro-China content received a higher ratio of views per like than anti-China content,
notwithstanding the fact that anti-China content received significantly higher total engagement
nearly across the board.

Network Contagion Research Institute Intelligence Report

19

Figure 12: Average Views per Like for TikTok and YouTube

Whereas the views-per-likes ratio for pro-China content on TikTok (15.41) was 87% higher thananti-China content (8.22), the same ratio on YouTube was just 14%. This indicates that despiteanti-China content receiving higher engagement (views, likes) than pro-China content on
average, the algorithmic bias on TikTok drives more views per like to pro-China content thananti-China content.
This finding seemingly contradicts the previously cited finding (footnote 15) that more popular
content (i.e., more likes) drives higher exposure and more views. Therefore, it appears that
TikTok's algorithm uniquely favors pro-China content in terms of views per like, irrespective of
the overall engagement levels. Moreover, this finding is significantly more pronounced on
TikTok than YouTube, suggesting a TikTok-specific bias.

Network Contagion Research Institute Intelligence Report

SURVEY DATA

20

17Initial results of the above research suggest that TikTok possesses a drastically smaller
proportion of anti-CCP material relative to both YouTube and Instagram and also suggests thatanti-CCP material is being manipulatively suppressed from view, either algorithmically or
through moderation. But these findings do not prove that manipulated changes in platform
content might be reflected in user attitudes and psychology. This is an important point toelucidate because if surreptitious manipulation of social media platforms undermines user
autonomy, this creates significant threats to the capacity for free thought and speech.

Figure 13: Human Rights Ranking in China by TikTok Usage

17 The full survey data set, results, and methodology can be found as an accompanying addendum on the report

page. Furthermore, full details on sampling methods, exclusion criteria, and analysis can all be accessed in said
addendum.

Network Contagion Research Institute Intelligence Report

21

To better understand the relationship between mass manipulation of content and potential
impacts on underlying user psychology/attitudes, NCRI ran a survey through Amazon’s Prime
Panels Service (n=1214) wherein respondents reported on their daily social media platform use.
Survey questions were developed to probe positive/negative perceptions of China’s humanrights records, as well as subjects’ feelings about China as a travel destination (informed by
NCRI’s analysis of putative CCP information operations which strongly featured travel/culture
influencers). To disguise the purpose of the survey from subjects, these questions wererandomized with similar questions regarding numerous countries.
When controlling for age, among the video-streaming platforms we studied, TikTok use
significantly and uniquely predicted more positive perceptions of China’s human rights record (β= .21, t(4.51), p < .0001; see Figure 13 and Appendix II). This was particularly notable for heavy
users (those who use 3 or more hours on the platform daily) who showed a 49% increase inpositivity towards China’s human rights records relative to non-users. By contrast, use of
YouTube and Instagram showed no significant relationship on users’ perception of China’s
human rights record.
Similarly, TikTok use, unlike Instagram and YouTube use, significantly predicted more positive
perceptions of China as a travel destination (β = .17, p < .0001; see Figure 14 and Appendix II)
and further, predicted users would agree with the statement that “Tiananmen square is mostly
known for being a popular travel destination” (β = .18, p < .0001; see Appendix II). Thesefindings were also especially notable for heavy users, with such users showing a 41% increaseNetwork Contagion Research Institute Intelligence Report22

in favorability to China as a travel destination and 48% increase in agreement with the notionthat Tiananmen square is mostly known as a tourist site.
Figure 14: China Favorable Travel Destination by TikTok Usage

CONCLUSIONS AND RECOMMENDATIONSThis report establishes that TikTok algorithms actively suppress content critical of the Chinese
Communist Party (CCP) while simultaneously boosting pro-China propaganda and promoting
distracting, irrelevant content. Through the use of travel influencers, frontier lifestyle accounts,
and other CCP-linked content creators, the platform systematically shouts down sensitive
discussions about issues like ethnic genocide and human rights abuses. These insidious tactics
have far-reaching consequences that psychological survey data show are effective in shaping
the views of its intended target: the minds of young users. These users, through targeting or
information environments engineered to sublimate free speech, appear to absorb these biased
narratives unwittingly, leading to a distorted understanding of critical global issues.
The tension between free speech and national security has become a prominent issue in thedigital age, particularly with platforms like TikTok. First Amendment advocates argue thatbanning such platforms infringes on free speech, while national security experts highlight thenational security risks posed by potential foreign influence. Our research indicates that thesetwo concerns are not mutually exclusive. Free speech can be abridged, and democratic
institutions destabilized, as effectively through algorithmic manipulation of carefully
indoctrinated masses as by direct censorship. Our research highlights how algorithmic
manipulation is undermining free expression and destabilizing democratic nations from within.
Taken together, these findings underscore the urgent need for developing robust methods topressure test algorithms and detect when they subvert free expression without user consent.
The recent work by the Institute for Strategic Dialogue (ISD), which exposed how TikTok's
algorithms can inadvertently amplify harmful content, which is a step in this direction.
Accordingly, we recommend the creation of a Civic Trust funded by both platforms and the
public that will be empowered to develop a systematic pressure testing system to set standards
and ensure transparency. This system would help identify when platforms are manipulatinguser perceptions, forcing individuals to adopt ideas they did not choose, often without theirawareness. Such a framework is essential to protect the integrity of free speech and thesanctity of free will; in doing so, it will maintain the democratic values within which theseplatforms operate, at least in free societies.
Furthermore, this Civic Trust must also develop fair and common consequences for platforms
found to be undermining free expression. If social media algorithms are found to be subverting
the very democracies that provide them the freedom to operate, they are both unjust anddangerous. There must be accountability and corrective measures to ensure that platforms are
not exploited by state actors to erode democratic institutions and values.

Network Contagion Research Institute Intelligence Report

23

LIMITATIONS
While our study provides significant insights into the manipulation of content on TikTok andother social media platforms, there are inherent limitations that should be acknowledged. First,
our research primarily relies on the analysis of content served to newly created, organic
accounts. While this methodology is designed to mimic the experience of typical users, it does
not account for personalized content that may be delivered based on individual user histories
and interactions over time. Consequently, the data may not fully capture the breadth of content
suppression or amplification experienced by all users.
Additionally, the coding and classification of content as pro-China, anti-China, or irrelevant
involved subjective judgment, even with the use of blind coding and a third-party arbitrator fordiscrepancies. Although efforts were made to minimize bias, the potential for interpretative
differences remains. Furthermore, our study did not explore the full range of user engagement
metrics, such as comments and shares, which could provide deeper insights into user reactions
and the spread of content across networks.
Lastly, a clear limitation in this report, which compared video streaming platforms, was that itwas not a fully comprehensive cross-platform analysis. Our findings highlight a novel andconcerning shift from merely observing evidence of content influence on TikTok to detecting
psychological changes among its users because TikTok users not only encounter biased
content but as hypothesized, also exhibit altered perceptions and attitudes in favor of the CCP.
More comprehensive efforts are needed to better understand the relationship between user
psychology and the prevalence of manipulated content.

Network Contagion Research Institute Intelligence Report

24

APPENDIX IOSINT Analysis of @UyghurBeauty
In the user journey performed for “Uyghur,” we observed that 40% of all video content collectedon YouTube was uploaded by a single channel, @uyghurbeauty. On YouTube, @uyghurbeauty
has 96K subscribers, a total of nearly 50M aggregate views, and over 3K video uploads. The
channel description is a generic “Discover the beauty of Uyghur culture and traditions,” and a link isprovided to an identically named Instagram profile, @uyghurbeauty, with 161K followers.
The @uyghurbeauty YouTube channel is based in the United States, while the Instagram’s
location is not specified. Most of the recent content is highly formulaic, characterized by amusic video or folksong with the following cookie-cutter text appended in the description:“We share Uyghur folk songs and Uyghur traditional dance. You will learn about Uyghur
people and enjoy the beauty of Uyghur culture. Uyghur Beauty Channel operates
exclusively on YouTube. Please don't forget to hit the like button and subscribe to see
more!”On Instagram, a similar content pattern is observed alongside the addition of a largely static listof hashtags that include: #uyghur #uyghursong #uyghurbeauty #uyghurgarden #uighur#uyghurpeople #uyghurtraditions #uyghurculture.

Figure 15: @uyghurbeauty Instagram Profile

Though passive OSINT techniques do not allow for a definitive identification of the channel’soperator or its real geographic base of operations, it remains an emblematic example of a
Chinese media asset that uses its high-reach and high-frequency of posting to co-opt anddominate social chatter around Uyghur-related keywords and hashtags. This modus operandi
has effectively skewed YouTube content to nearly 52.6% pro-China, and provides a qualitative
contrast to the official media sources (CGTN, South China Morning Post, etc.) that skewed
YouTube content for “Xinjiang” to 52% pro-China.
Another curious YouTube channel we observed is the similarly-named @uyghurgarden (notethat @uyghurbeauty repeatedly shares the hashtag #uyghurgarden on Instagram, suggesting
though not proving a connection between the two channel operators). @uyghurgarden has 937K

Network Contagion Research Institute Intelligence Report

25

subscribers and nearly 700M net views. The YouTube profile claims it is based in the UnitedStates and was opened in 2014, yet at the time of writing the oldest video on the channel dates
from just September 2023. Given the fact that archived screenshots of @uyghurgarden from
December 2022 depict now-deleted videos, 18 one can conclude that the channel’s operators are
performing intensive content curation for unclear reasons.

Figure 16: @uyghurgarden YouTube Profile

The account growth witnessed by @uyghurgarden strongly suggests artificial inflation ofsubscribers. The channel has grown from around 50K followers to August 2022 to nearly 1M by
July 2024 as measured by Social Blade, translating into 20X growth over 24 months based on
repetitive, relatively low-caliber short videos. Subscribers are up nearly 268% for the past 30
days, while views increased by 282%. In the absence of high-profile viral content, thesesubscriber and views growth metrics appear highly inorganic and are suggestive of inauthentic
amplification.

Figure 17: SocialBlade Data for @uyghurgarden

18 Web.archive.org. (2022, December 12). Uyghur Garden. YouTube. Retrieved fromhttps://web.archive.org/web/20221212154805/https://www.youtube.com/@uyghurgarden

Network Contagion Research Institute Intelligence Report

26

Figure 18: SocialBlade Data for @uyghurgarden, cont'd

Closer analysis of the two accounts suggests that they are indeed likely managed by a single
operator. Firstly, the generic wording of @uyghurgarden’s account description closely resembles
that of @uyghurbeauty’s: “Welcome to the Uyghur Garden! Discover Uyghur people and theirdelicious food.” Furthermore, both accounts use similar custom-designed red symbols thatfurther indicate a connection. Lastly and most tellingly, an archived screenshot from December
2022 shows that @uyghurgarden had added @uyghurbeautfy as a featured account to its
channel profile.19 As this step must be undertaken by the channel operator, we can infer withhigh confidence that the operators of @uyghurgarden and @uyghurbeautfy are either shared orin some degree of contact.

Figure 19: Web Archive Screen Capture of @uyghurbeauty from 12/12/2022

Though open-source techniques are unable to definitively prove a connection between the twochannels, both exemplify the well-documented CCP tactic of using idyllic coverage of folk
culture to whitewash severe human rights abuses and cultural erasure of China’s ethnoreligious minorities. They also illustrate the significant resources being allocated to the
19 Ibid.

Network Contagion Research Institute Intelligence Report27

cultivation of media assets that are then used to target international audiences by seeding
benign content that subsumes substantive content around sensitive domestic issues for the
CCP.

APPENDIX II
Supplementary Survey Data
Coefficient plots for survey regression analysis was performed across key questions pertaining
to China and human rights. Regressions were performed in a step wise fashion to examine therobustness of main effects and check against heteroskedasticity.

Figure 20: Coefficient Plot for Social Media Platforms (Tiananmen Travel)

Coefficient Plot for Social Media Platforms (Tiananmen Square is mostly known for being a
popular travel destination)
This plot illustrates the impact of various social media platforms on the perception that
Tiananmen Square is predominantly recognized as a travel destination rather than a historicalsite of political significance. TikTok emerges as the leading platform, significantly amplifying
this perception among its users. In comparison, Facebook 20 also shows a notable effect, albeit
less pronounced than TikTok. Other platforms like Instagram, YouTube, Reddit, and X (formerly
Twitter) demonstrate comparatively minor impacts.

20 Here it is important to note that significant associations with Facebook use and favorability towards China’s

human rights record did not survive more scrupulous pressure testing of our models, but Tik Tok use did uniquely
among platforms. See extended modeling/factor testing on favorability towards China’s human rights records here:
https://docs.google.com/document/d/1wVEtKmk7XsZy1QL5llxHqcRQh9SDDxXSVtURRDnUbXE/edit?usp=sharing

Network Contagion Research Institute Intelligence Report

28

Figure 21: Coefficient Plot for Social Media Platforms (China Travel)

Coefficient Plot for Social Media Platforms (China as the Most Desirable Travel Destination)
This plot examines how different social media platforms influence the view of China as themost desirable travel destination. TikTok once again stands out, significantly enhancing
positive perceptions of China as a premier travel spot. Reddit and X show moderate influence,
whereas platforms like YouTube, Facebook, and Instagram have minimal or negligible effects inshaping this perception.

Figure 22: Coefficient Plot for Social Media Platforms (China Human Rights)

Network Contagion Research Institute Intelligence Report29

Coefficient Plot for Social Media Platforms and Age (Positive Ranking of Human Rights for
China)
This figure evaluates the role of social media platforms and user age in positively ranking
China's human rights record. TikTok prominently leads, indicating a substantial impact on
improving perceptions of China's human rights among its users. Facebook follows with asignificant but smaller effect. Instagram, X, and YouTube show modest influences, while Reddit
and user age exhibit the least impact on this perception.
Overall, TikTok consistently demonstrates the highest influence across all examined
perceptions, highlighting its powerful role in shaping user views on key issues related to China.
Facebook also shows significant, albeit lesser, influence compared to TikTok in altering user
perceptions.

Network Contagion Research Institute Intelligence Report

APPENDIX III
Engagement Metadata (YouTube and Instagram)

Figure 23: YouTube Engagement Metadata

30

Network Contagion Research Institute Intelligence Report

Figure 24: Instagram Engagement Metadata31