Quality and reliability evaluation of chikungunya fever-related video content on Tiktok and Bilibili: A cross-sectional study

Abstract

Background

Chikungunya fever (CHIKF) is an arboviral disease caused by the Chikungunya virus, which has increasingly emerged as a global public health concern, particularly in regions like China. Short video platforms such as TikTok and Bilibili have become vital channels for disseminating health information. Given the significant reach of these platforms, it is essential to evaluate the quality and reliability of videos related to CHIKF, ensuring that the public receives accurate and credible information.

Methods

This cross-sectional study collected the top 100 videos from each platform, ranked by comprehensive sorting. Video quality was assessed using four validated tools: the Global Quality Scale (GQS), modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmarks, and the Video Information and Quality Index (VIQI), which evaluate educational, informational, and audiovisual quality.

Results

A total of 166 videos were analyzed. Overall scores were at a moderate level: 3.00 (IQR: 2.00-4.00), mDISCERN 3.00 (IQR: 2.00-4.00), JAMA 2.00 (IQR: 2.00-2.00), and VIQI 10.00 (IQR: 8.00-13.00). Compared with Bilibili, TikTok videos demonstrated significantly higher user engagement, including likes, comments, and shares (all p<0.05). In terms of quality, TikTok videos showed better information flow and significantly higher VIQI scores (p<0.05). Videos uploaded by medical professionals achieved significantly higher GQS, mDISCERN, and JAMA scores compared with those uploaded by general users (p<0.05).

Conclusions

This study found that the overall quality, reliability, and transparency of CHIKF-related videos remain suboptimal. Videos uploaded by medical professionals performed best across multiple quality indicators. These findings highlight the need to strengthen content oversight on platforms and encourage medical professionals to actively participate in the creation of CHIKF-related science videos to improve the accuracy of public health information.

Keywords

chikungunya fever social media public health communication tiktok bilibili

Introduction

Chikungunya fever (CHIKF) is an arboviral disease that poses a growing public health threat globally,¹ including significant impacts in China. The economic impact of CHIKF is estimated to surpass that of dengue, yellow fever, and even major infectious diseases such as HIV/AIDS and tuberculosis.²

Since the early 2000s, its epidemiology has transformed from a geographically restricted disease to a re-emerging global threat, accelerated by climate change and human mobility.³ Cases of infection entering China as a result of global disease transmission are continuously increasing. These cases have become a significant challenge for health systems, as diagnostic delays often facilitate local transmission.⁴ For example, a single imported case in July this year led to outbreaks across six provinces in China, resulting in sustained clusters.⁵ On July 9, 2025, Foshan City in Guangdong Province reported a cluster of CHIKF cases. As of July 26, a total of 4,824 confirmed cases had been reported across 12 prefecture-level cities in Guangdong, with the majority of cases concentrated in the Shunde District of Foshan, accounting for 4,208 cases (87.2% of all provincial cases). This gradual spread poses a significant threat to the health and socioeconomic stability of Chinese residents.^6,7

Despite the global spread of the CHIKF, there are currently no licensed antiviral treatments or vaccines available; management remains symptomatic, and prevention relies on vector control and risk communication according to WHO guidelines.⁸ Effective health communication plays a crucial role in enhancing public risk awareness, promoting adherence to preventive measures, reducing the impact of misinformation, and ultimately strengthening society’s overall capacity to respond to health crises.⁹

Health awareness plays a crucial role in public health interventions, and has shown significant improvement in the context of infectious diseases. For instance, during the COVID-19 pandemic, health communication efforts, including public service announcements, social media campaigns, and government messaging, were key in raising awareness about the virus, its transmission, and preventive measures like mask-wearing and vaccination.¹⁰ Similarly, dengue fever control programs and rapid health awareness campaigns helped reduce transmission through community engagement and informing the public about risks. These examples underscore the importance of health communication in mitigating the impact of infectious diseases by fostering awareness and promoting behavior change.¹¹

Short videos are typically defined as brief video content ranging from a few seconds to several minutes, and they have become increasingly popular as a means of disseminating information, particularly on platforms like TikTok and Bilibili. TikTok is renowned for its algorithmic promotion and high interactivity, targeting a younger audience, while Bilibili offers longer video formats that allow for deeper engagement and educational content.¹² These platforms play an increasingly important role in the dissemination of public health information.^13–15 Driven by algorithmic promotion and high interactivity, these platforms enable unprecedented scalability of information dissemination. However, they also carry the risk of rapid spread of health-related misinformation.¹⁶ Previous studies on osteoporosis,¹⁷ thyroid-associated ophthalmopathy,¹⁸ and dry eye disease¹⁹ have shown that although such short videos often achieve high levels of engagement, their overall quality and reliability are generally limited.

This study aims to evaluate the quality and reliability of CHIKF-related videos on TikTok and Bilibili, providing valuable insights into the effectiveness of public health messaging on these platforms.

Methods

Study design and search strategy

This cross-sectional study was conducted from August 25 to August 28, 2025. The objective was to evaluate the quality and reliability of Chinese-language popular science videos on CHIKF during the epidemic period. All selected videos were carefully reviewed to verify their strict relevance to our research theme, focusing on the health crisis caused by CHIKF in our country. Relevant videos were systematically searched on two major Chinese short video platforms, TikTok and Bilibili. To minimize algorithm-driven bias and avoid the influence of personalized recommendations, all searches were conducted using newly created user accounts. The primary keyword used for the search was “基孔肯雅热” (“Chikungunya fever”), and we also included related keywords such as “科普” (“Popular science”) to ensure comprehensive coverage and capture a broader range of relevant content.

Data collection was performed by two senior infectious disease specialists who assessed the videos, with a third specialist available to mediate any disputes in ratings, thereby ensuring transparency in the data collection process. Each platform provides three ranking options: comprehensive ranking, most recent, and most liked. Comprehensive ranking was selected as the sampling basis because it is the default sorting method and better reflects typical user browsing behavior. This decision was based on two observations. First, most relevant videos were concentrated within the top 100 results under comprehensive ranking. Second, users seeking health-related information typically rely on the initial pages of search results. Based on this rationale, the top 100 Chinese-language videos under comprehensive ranking from both TikTok and Bilibili were included for analysis. The video selection process is summarized in the accompanying flow diagram (Figure 1).

Figure 1.

PRISMA flow diagram for the selection of videos related to Chikungunya fever. The logos of TikTok and Bilibili are official brand logos, used in accordance with copyright policies and are free for academic use, ensuring compliance with academic integrity.

Data extraction

For each video, the following metadata were extracted: the video’s URL, its main topic or thematic focus, the date of upload, the uploader type (e.g., individual, organization, healthcare professional), and the uploader’s verification status. Additionally, the number of days the video had been available on the platform at the time of data collection was recorded. User engagement metrics, including the total number of likes, comments, shares, and favorites, were also documented to assess the level of viewer interaction.

This comprehensive dataset enabled both descriptive and inferential statistical analyses to examine video characteristics and their association with quality scores.

Inclusion criteria

Language: Only videos in Chinese were included.

Content Relevance: Videos must address aspects of CHIKF, such as prevention, awareness, practices, symptoms, and misinformation.

Time Frame: Videos uploaded within a defined period (from August 25 to August 28, 2025) were considered to ensure relevance in relation to the current disease outbreak.

KeywordsUsed: The primary keyword used for the search was “基孔肯雅热” (Chikungunya fever). We also included related keywords like “科普” (Popular science) to ensure comprehensive coverage in our search.

Exclusion criteria

Videos lacking substantial information on CHIKF.

Non-Chinese language videos.

Duplicate videos or those that had been removed prior to analysis.

Videos exceeding a specific length (e.g., longer than 10 minutes on TikTok).

Uploader categories

To simplify data analysis, the uploader categories were grouped into three main categories: 1) Medical Professionals, which includes Physicians and Medical students; 2) Organizations, covering News agencies and Science Communication; and 3) General Public, which includes Patients and General users. This grouping was based on the similarities in the uploaders’ roles and backgrounds, ensuring a more consistent and meaningful analysis. Rating statistics were then calculated according to these three categories.

Quality assessment

The quality and reliability of each video were assessed using four validated scoring instruments: GQS,²⁰ mDISCERN,²¹ JAMA²² and VIQI.²³ Each instrument captured a distinct aspect of video quality, allowing for a comprehensive evaluation of the educational, informational, and audiovisual characteristics of the content.

The GQS is a 5-point Likert scale designed to assess the overall educational value, coherence, and usefulness of video content. Scores range from 1 (poor quality, lacking logical flow and clarity) to 5 (excellent quality, highly informative, and well-structured). This tool has been widely adopted in health communication research for evaluating consumer-facing media (Table 1).

Table 1.

The Global Quality Score (GQS) quality criteria.

Item features	Points
Poor quality; poor flow of the videos; most information missing; not at all useful for patients	1
Generally poor quality; some information listed, but many important topics missing; of very limited use to patients	2
Moderate quality; suboptimal flow; some important adequately discussed, but other information poorly discussed; somewhat useful for patients	3
Good quality and generally good flow; most of the relevant information listed, but some topics not covered; useful for patients	4
Excellent quality and flow; very useful for patients	5

Note. The score is based on the video content, with a higher score indicating better overall quality of the video.

The mDISCERN evaluates the reliability of the content based on five binary items. These items assess the clarity of the video’s objectives, the citation of credible sources, the balance and neutrality of the information, the inclusion of supplementary resources, and the acknowledgment of uncertainties or conflicting evidence. Each item contributes one point, resulting in a total score ranging from 0 to 5. Higher scores indicate greater reliability and transparency in the video’s presentation (Table 2).

Table 2.

The Modified DISCERN (mDISCERN) quality criteria.

Item features	Points
Is the video clear, concise, and understandable?	1
Are valid sources cited?	2
Is the content presented balanced and unbiased?	3
Are additional sources of content listed for patient reference?	4
Are areas of uncertainty mentioned?	5

Note. Each criterion of the Modified DISCERN (mDISCERN) quality assessment was scored independently, with 1 point awarded for each criterion met, resulting in a total possible score of 5 points. A higher score indicates greater reliability and transparency of the video content.

The JAMA Benchmark Criteria assess the credibility and source accountability of online health information. This instrument comprises four elements: authorship (identification of the content creator), attribution (presence of source references), disclosure (statements of sponsorship or conflicts of interest), and currency (date of creation or last update). One point is awarded for each fulfilled criterion, yielding a maximum possible score of 4 (Table 3).

Table 3.

The Journal of the American Medical Association (JAMA) benchmark criteria.

Item features	Points
Authorship: Author and contributor credentials and their affiliations should be provided.	1
Attribution: Clearly lists all copyright information and states references and sources for content.	2
Currency: Initial date of posted content and subsequent updates to content should be provided.	3
Disclosure: Conflicts of interest, funding, sponsorship, advertising, support, and video ownership should be fully disclosed.	4

Note. The criteria of each aspect were scored separately, and 1 point for each criterion with a total score of 4 points.

The VIQI scale was utilized to assess video quality across four key dimensions: information flow, scientific accuracy, audiovisual quality, and content precision. Information flow evaluates the logical progression and clarity of the message, while scientific accuracy examines the correctness of the information presented. Audiovisual quality assesses the overall production, including elements such as still images, interviews, captions, and summaries. Finally, content precision measures the alignment between the video’s title and its actual content. Each dimension is rated on a 5-point Likert scale, with scores reflecting the degree of quality. The total VIQI score ranges from 5 to 20, with higher scores indicating greater specialized medical knowledge and quality in video production. Detailed scoring criteria are provided in Table 4.

Table 4.

Domains and scoring system of the Video Information and Quality Index (VIQI).

Item features		Points (1–5 likert scale)
VIQI-1: Flow of information	Logical sequence and organization of content	1 = disorganized, confusing; 5 = very well-structured, logical, easy to follow
VIQI-2: Information quality	Accuracy, comprehensiveness, and depth of content	1 = poor, inaccurate, minimal content; 5 = excellent, accurate, comprehensive
VIQI-3: Video quality	Visual and audio quality (clarity, resolution, background, editing)	1 = poor audio/visual quality; 5 = professional, clear, high-quality
VIQI-4: Precision	Degree to which title/description accurately reflects video content	1 = misleading or inconsistent; 5 = highly accurate, fully consistent

Note. The total VIQI score equals the sum of the four domain scores, with a maximum of 20 points.

The assessment of video quality and reliability was independently conducted by two senior infectious disease specialists with a background in epidemiology and expertise in Chikungunya fever. Prior to the evaluation, both reviewers underwent structured training based on the latest Chikungunya guidelines to ensure consistency in their assessments. In cases of disagreement, a third senior infectious disease specialist acted as an adjudicator.

Statistical analysis

Continuous variables such as video duration, days since upload, and engagement metrics were first assessed for normality using the Shapiro–Wilk test. Data that followed a normal distribution are presented as mean ± standard deviation, while non-normally distributed data are reported as median and interquartile range. Categorical variables such as uploader type, verification status, and thematic focus are expressed as frequencies and percentages. Group comparisons were performed using independent-sample t-tests or one-way analysis of variance for normally distributed variables. For non-normally distributed variables, Mann–Whitney U tests or Kruskal–Wallis tests were applied. Spearman’s rank correlation coefficients were calculated to assess the relationships between video quality scores GQS, mDISCERN, JAMA, and VIQI and engagement metrics such as likes, comments, shares, and favorites. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant. All analyses were performed using R software version 4.3.2.

Result

Video general characteristics

A comprehensive analysis of the characteristics of videos from Bilibili and TikTok resulted in a dataset comprising 166 videos (82 from Bilibili and 84 from TikTok), which accounted for 50.60% and 49.40% of the total, respectively. The careful selection process confirmed that all included videos were closely aligned with our research theme, addressing public health events linked to CHIKF and reflecting the current health crisis. The initial distribution of video uploaders across different categories was as follows: 34 general users (20.48%), 7 medical students (4.22%), 59 news organizations (35.54%), 7 patients (4.22%), 40 physicians (24.10%), and 19 science communicators (11.45%) (Figure 2). After merging the uploaders into three main categories, Organizations accounted for the largest proportion at 46.99%, while Medical Professionals and General Public accounted for 28.31% and 24.70%, respectively. The specific parameters for video duration, likes, comments, favorites, and shares are presented in Table 5.

Figure 2.

Distribution percentage of different video uploader identities on TikTok and Bilibili.

Table 5.

Characteristics of video uploaders and Quality Scores of Chikungunya Fever-Related Videos on TikTok and Bilibili.

Characteristics	Total (n = 166)	TikTok (n = 84)	Bilibili (n = 82)	P	General public (n = 41)	Medical professionals (n = 47)	Organization (n = 78)	P
General information
Video length, M (Q₁, Q₃)	85.50 (50.00, 163.50)	71.00 (42.50, 131.00)	111.00 (57.50, 347.75)	<.001	93.00 (59.00,203.00)	131.00 (71.00,293.50)	62.50 (39.25,108.25)	<.001
Likes, M (Q₁, Q₃)	71.50 (9.00, 1412.25)	180.50 (38.00,1696.50)	12.00 (4.00, 597.25)	<.001	70.00 (7.00,1673.00)	130.00 (16.50,631.50)	50.50 (10.00,892.00)	0.676
Collections, M (Q₁, Q₃)	23.50 (3.00, 198.25)	29.50 (5.00, 207.50)	11.50 (2.00, 147.75)	0.096	18.00 (2.00,196.00)	28.00 (5.00,192.50)	17.00 (4.00,252.50)	0.909
Comments, M (Q₁, Q₃)	8.00 (1.00, 95.50)	13.50 (2.75, 106.50)	3.00 (1.00, 73.00)	0.017	10.00 (2.00,348.00)	11.00 (1.00,46.00)	7.00 (1.00,82.00)	0.286
Shares, M (Q₁, Q₃)	40.00 (5.00, 320.00)	81.00 (13.00, 552.25)	15.00 (4.00, 147.00)	0.006	24.00 (4.75,433.25)	41.00 (5.00,211.00)	56.00 (6.25,343.25)	0.697
Video quality
GQS score, M(Q₁, Q₃)	3.00 (2.00, 4.00)	3.00 (2.00, 4.00)	3.00 (2.00, 4.00)	0.914	2.00 (1.00,3.00)	4.00 (3.00,4.00)	3.00 (2.00,4.00)	<.001
mDISCERN, M (Q₁, Q₃)	3.00 (2.00, 4.00)	4.00 (3.00, 4.00)	3.00 (2.00, 4.00)	0.376	2.00 (1.00,3.00)	4.00 (3.00,4.00)	4.00 (3.00,4.00)	<.001
JAMA, M (Q₁, Q₃)	2.00 (2.00, 2.00)	2.00 (2.00, 2.25)	2.00 (2.00, 2.00)	0.143	2.00 (2.00,2.00)	2.00 (2.00,2.50)	2.00 (2.00,2.75)	0.002
VIQI, M (Q₁, Q₃)	10.00 (8.00, 13.00)	12.00 (11.00, 14.00)	8.00 (6.00, 10.00)	<.001	7.00 (6.00,9.00)	12.00 (10.00,13.50)	11.00 (8.00,13.00)	<.001

Abbreviations: n, number; Q₁: 1st Quartile, Q₃: 3st Quartile.n, number; IQR, interquartile range; SD, Standard Deviation; GQS, Global Quality Scale; mDISCERN, modified DISCERN; CS,completeness scores; VIQI, Video Information and Quality Index.

Video characteristics and engagement comparison between bilibili and tiktok

The analysis revealed that videos from Bilibili had the longest median duration, with a median of 111 seconds (IQR: 57.50–347.75 seconds) (p < 0.05). In contrast, TikTok videos exhibited significantly higher engagement metrics. For instance, the median number of likes for TikTok videos was 180.50 (IQR: 38.00–1696.50), the median number of comments was 13.50 (IQR: 2.75–106.50), and the median number of shares was 81.00 (IQR: 13.00–552.25). Figure 3(a)–(d) present the comparative results of the two platforms with respect to GQS, mDISCERN, JAMA, and VIQI scores.

Figure 3.

All scoring metrics for Chikungunya informational short videos between the two platforms. (3A) GQS scores for both platforms; (3B) mDISCERN scores for both platforms; (3C) JAMA scores for both platforms; (3D) VIQI scores for both platforms. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Video Features, Quality, and Reliability by Uploader Group.

The analysis revealed that medical professionals consistently achieved the highest scores across all four metrics—GQS, mDISCERN, JAMA, and VIQI—indicating superior video quality and reliability compared to other uploader groups (p < 0.05). Figure 4(a)–(d) illustrate the comparisons of these scores across different uploader categories. Figure 5(a)–(d) present the comparisons of these four scoring systems among different uploader groups on TikTok, whereas Figure 6(a)–(d) display the corresponding comparisons on the Bilibili platform. Table 5 presents the specific parameters for comparing the general characteristics and quality scores of videos among different uploader groups. The differences in video length, Mdiscern, VIQI, GQS, and JAMA were statistically significant (p < 0.05), while the differences in Likes, Collections, Comments, and Shares were not statistically significant (p > 0.05).

Figure 4.

Scoring metrics among different uploader groups without platform restrictions. (4A) GQS scores for different video uploader groups; (4B) mDISCERN scores for different video uploader groups; (4C) JAMA scores for different video uploader groups; (4D) VIQI scores for different video uploader groups. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 5.

Comparison of scoring metrics among different uploader groups on the TikTok platform. (5A) GQS scores for different video uploader groups; (5B) mDISCERN scores for different video uploader groups; (5C) JAMA scores for different video uploader groups; (5D) VIQI scores for different video uploader groups. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 6.

Comparison of scoring metrics among different uploader groups on the Bilibili platform. (6A) GQS scores for different video uploader groups; (6B) mDISCERN scores for different video uploader groups; (6C) JAMA scores for different video uploader groups; (6D) VIQI scores for different video uploader groups. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Heatmap Analysis of Short Videos.

The analysis revealed the correlations between general parameters and quality parameters for videos across TikTok and Bilibili. Figure 7(a) illustrates these correlations on TikTok, while Figure 7(b) presents the corresponding correlations on Bilibili. The correlation patterns were largely consistent across both platforms: strong correlations were observed among engagement metrics, while no significant relationships were found with video quality scores (GQS, mDISCERN, JAMA, or VIQI).

Figure 7.

Spearman correlation coefficients between video basic characteristics and GQS, mDISCERN, VIQI, and JAMA. (7A) Spearman correlation analysis heatmap of Chikungunya-related videos on TikTok. (7B) Spearman correlation analysis heatmap of Chikungunya-related videos on Bilibili. The color intensity indicates the strength of the correlation, with darker shades representing stronger positive correlations, lighter shades indicating weaker or no correlation, and negative correlations shown in cooler colors. The values in the heatmap range from -1 to +1, where +1 indicates a perfect positive correlation, -1 represents a perfect negative correlation, and × indicates no correlation.

Discussion

Overview of popular science short videos about CHIKF

This study analyzed the general characteristics, quality, and reliability of short videos related to CHIKF on TikTok and Bilibili, with a primary focus on public health crisis. For context, public health crisis such as the previous pandemics of dengue fever, avian influenza, and COVID-19 are also categorized within this framework. We evaluated the content quality, information consistency, specialized medical knowledge, and audience reception of CHIKF videos during the epidemic period, which contributes to better preparedness in addressing public health crisis. The results revealed that the overall video quality and reliability on both platforms were suboptimal; however, videos uploaded by medical professionals demonstrated higher quality. Notably, engagement metrics, such as likes, comments, and shares, were found to be unrelated to video quality. This suggests that while these videos may attract viewer interaction, it does not necessarily reflect their educational value or accuracy. Consequently, these findings highlight both the current state and limitations of health communication through social media, offering initial insights for developing platform-specific science communication strategies aimed at improving the accuracy and effectiveness of public health information dissemination.

Video quality and information dissemination during public health crisis

In this study, we found that the majority of videos disseminated during public health crisis, particularly related to the CHIKF outbreak, exhibited generally poor quality. This observation aligns with findings from studies conducted during the COVID-19 pandemic, which reported that many health-related short videos did not meet established quality standards.^9,24 A primary reason for this is the lack of standardized content creation processes and quality control mechanisms, which leads to the dissemination of incomplete or potentially misleading health information.²⁵ Furthermore, research conducted by Yuting Lei et al. highlighted that videos produced by non-professionals often lack scientific accuracy and essential contextual information, contributing to the spread of low-quality short videos.²⁶

There is substantial evidence that videos on short video platforms, such as TikTok and Bilibili, frequently fail to meet ideal quality standards.^27,28 While these platforms excel in the rapid dissemination of content, they often lack effective quality checks. This inadequacy impedes the assurance that health-related videos adhere to the basic requirements of scientific communication.²⁹ Moreover, the fast-paced nature of short-video platforms, compounded by inadequate content moderation mechanisms, leads to the widespread circulation of health information that does not adhere to scientific communication standards.³⁰

The swift and reliable dissemination of information during public health crisis is crucial. However, low-quality information can have serious repercussions. As observed during the COVID-19 pandemic, inaccurate health advice not only resulted in widespread confusion but also contributed to the adoption of harmful behaviors. The prevalence of misinformation during this period was alarming, as it included numerous rumors and misconceptions regarding the pandemic. Erroneous information led to negative public perceptions about vaccination, which in turn delayed vaccination uptake and contributed to increased infection rates. The lack of accurate information impeded public health efforts and demonstrated the critical need for reliable health communication in times of crisis.³¹ Therefore, strengthening the training and support of public health information creators is vital for improving the quality of content.³² By providing scientific communication training, creators can enhance their ability to assess the accuracy of health information and better understand their role in public health communication.³³

We also recommend encouraging users to critically evaluate the health information they encounter on these platforms. Given the considerable variation in information accuracy, fostering media literacy among users is essential for helping them distinguish reliable content and avoid being misled by misinformation.³⁴ At the same time, platforms must take responsibility for strengthening their content moderation systems to ensure that only scientifically validated information is shared with the public. Such measures can contribute to improving the overall quality of video content, ensuring that shared information is scientifically sound and reliable.

Comparison of quality and reliability among different uploaders

The analysis showed that videos created by medical professionals consistently performed better than those produced by general users, even surpassing content from organizations like news outlets and non-profits. This advantage appears to be driven by several factors. First, medical professionals have the expertise to communicate complex health issues clearly and accurately, using proper medical terminology and evidence-based information.^35,36 This not only boosts the credibility of the content but also makes it easier for viewers to trust and understand the information being shared. In addition, medical professionals tend to adopt more engaging and creative communication styles, incorporating real-life case studies, visuals, and even humor to keep audiences interested.^37,38 In contrast, content from government agencies or organizations often feels more formal and rigid, which can make it less appealing and harder for viewers to connect with.³⁹ Furthermore, medical professionals can directly engage with their audience through interactive features such as Q&A sessions, creating a sense of involvement.⁴⁰

Our findings underscore the strengths different content creators bring to public health communication.⁴¹ However, they also suggest that differences in content quality can influence audience choices, potentially affecting the effectiveness of public health messaging. To improve the impact of health communication, platforms and policymakers should encourage more medical professionals to participate in content creation. Given their ability to produce accurate, engaging, and trustworthy content, medical professionals could play a crucial role in improving public understanding and engagement, particularly during public health crisis. Strengthening collaboration between health experts, content creators, and public health agencies would ensure that accurate health information reaches a wider audience and is better understood.

Correlation analysis

Through the analysis of relationships among interaction metrics and their correlation with content quality and reliability, we found that these dimensions generally exhibit low correlation. This aligns with previous research suggesting that interaction behavior does not always correlate directly with content quality.^42,43 Specifically, engagement metrics such as likes, comments, and shares showed varying degrees of relationship with video quality; for instance, videos with more informative content tended to receive higher interaction rates. Furthermore, no significant correlation was observed between video length and interaction metrics, implying that user engagement may not necessarily vary based on video duration. However, a weak positive correlation between video length and content quality/reliability suggests that longer videos may possess advantages in content depth or completeness.^44,45

Social media platforms like TikTok and Bilibili are vital for rapid health information dissemination, especially during public health emergencies. TikTok’s short videos quickly spread prevention messages, but user-generated content may compromise accuracy. Public health agencies should collaborate with platforms to ensure credible, authoritative information. Bilibili’s interactive features foster deeper engagement and real-time feedback, helping identify public concerns and misconceptions.

Future research should explore how different interaction models, such as user comments versus shares, impact content quality and dissemination effectiveness. Additionally, analyzing case studies of high-engagement videos could provide deeper insights into the relationship between content style and user response. Big data analysis can help refine public health strategies by identifying trends in viewer engagement and information retention.^46,47 Integrating these platforms’ strengths can improve health communication and promote informed, healthier behaviors.

Limitations

This study has several limitations. First, the relatively small sample size may restrict the representativeness of the findings. Second, the analysis was limited to only two platforms, TikTok and Bilibili, which may reduce the generalizability to other social media channels. Third, only Chinese-language videos were included, and the applicability of the conclusions to other linguistic contexts requires further investigation. Fourth, although validated instruments (GQS, mDISCERN, JAMA, VIQI) were employed for video quality assessment, the process inevitably involved a certain degree of subjectivity. Fifth, as a cross-sectional study, the dynamic nature of social media means that user-generated content changes rapidly, which may affect the timeliness of the conclusions.

Conclusion

This study analyzed videos related to CHIKF on TikTok and Bilibili. The findings indicated that the overall quality, reliability, and transparency of these videos remain suboptimal. Videos uploaded by medical professionals demonstrated relatively higher quality. No correlation was observed between video quality and interaction metrics. This study provides preliminary insights for optimizing health information dissemination. Future efforts should focus on strengthening platform content oversight, establishing rigorous review mechanisms, and encouraging active participation of medical professionals in short video creation.

Footnotes

Acknowledgements

The authors would like to express their gratitude to the participants who participated in the study.

ORCID iDs

Chen Jv

Liao Wenyu

Ethical consideration

No clinical data, human specimens, or laboratory animals were used in this study. All analyzed data were sourced from publicly available TikTok and Bilibili videos. Data collection fully complied with the terms of service of both TikTok and Bilibili platforms. No personally identifiable information was collected or processed, and no interaction with users occurred. As no private information was involved, and the study did not involve human participants, clinical data, laboratory animals, or histological research, ethics approval was not required.

Author contributions

Lu Zefeng: Methodology. Chen Jv: Writing -Original Draft; Formal analysis, Liao Wenyu: Formal analysis. Chen Xinxin: Writing -Review & Editing. Wang Shuangmiao: Supervision; Project administration. Lin Hao: Supervision; Project administration. All authors contributed to the article and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Guangdong Postgraduate Education Innovation Plan Project (No. 2024JGXM_081 to Lin Hao), the Project of Beijing Medical Award Foundation (No. YXJL-2025-0106-0117 to Lin Hao), the 2025 Guangdong Medical University Clinical + Basic Science and Technology Innovation Special Program (No. GDMULCJC202500 to Lin Hao), the 2025 PSM Guangdong Pharmaceutical Science Popularization Research Fund (Simcere Fund, No. 2025KP10 to Chen Jv), the 2025 Zhanjiang Science and Technology Project (Grant No. 2025B01245 to Chen Jv), the Guangdong Provincial Department of Education Clinical Base Undergraduate Teaching Reform Research Project (No. 2025JD133 to Chen Jv), the 2025 Undergraduate Teaching Quality and Teaching Reform Project in Guangdong Province (The Research on the Pathway of Constructing a Micro-specialty in Medical Communication under the “Dual Thousand Plan” — Practical Exploration Based on the Concept of Medical-Prevention Integration) (Wang Shuangmiao).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.*

Guarantor

Wang Shuangmiao and Chen Xinxin is the guarantor of this article. They takes full responsibility for the integrity of the research and data, has full access to all data, and had the final decision-making authority regarding publication.

References

Zaid

Burt

Liu

, et al. Arthritogenic alphaviruses: epidemiological and clinical perspective on emerging arboviruses. Lancet Infect Dis 2021; 21(5): e123–e133. https://doi.org/10.1016/S1473-3099(20)30491-6

Rosso

Flacco

Cioni

, et al. Immunogenicity and Safety of Chikungunya Vaccines: A Systematic Review and Meta-Analysis. Vaccines (Basel) 2024; 12(9): 969. https://doi.org/10.3390/vaccines12090969

Klepac

Hsieh

Ducker

, et al. Climate change, malaria and neglected tropical diseases: a scoping review. Trans R Soc Trop Med Hyg 2024; 118(9): 561–579. https://doi.org/10.1093/trstmh/trae026

Pan American Health Organization/World Health Organization . Epidemiological update: Chikungunya in the region of the Americas, 2024. 22 April 2024. Available.

China CDC . Data report. https://www.chinacdc.cn/jksj/jksj02/202509/U020250903294493257766.pdf (2025).

Sun

Shi

Liao

, et al. Chikungunya fever: pathogenesis and mechanisms underlying pain symptoms. Front Immunol 2025; 16: 1679385. https://doi.org/10.3389/fimmu.2025.1679385

Pedi

de Franca

GVA

Rodrigues

, et al. Burden of chikungunya fever and its economic and social impacts worldwide: A systematic review. Trop Med Int Health 2025; 30: 865–892. https://doi.org/10.1111/tmi.70012

Maure

Khazhidinov

Kang

, et al. Chikungunya vaccine development, challenges, and pathway toward public health impact. Vaccine 2024; 42(26): 126483. https://doi.org/10.1016/j.vaccine.2024.126483

Schillinger

Chittamuru

Ramírez

. From “Infodemics” to Health Promotion: A Novel Framework for the Role of Social Media in Public Health. Am J Public Health 2020; 110(9): 1393–1396. https://doi.org/10.2105/AJPH.2020.305746

10.

Rybacki

Szkiela

Rybacka

, et al. Attitudes regarding vaccination on the example of COVID-19 prevention. Med Pr 2024; 75(2): 123–131. https://doi.org/10.13075/mp.5893.01522

11.

Sahavechaphan

Chatrattikorn

Rattananen

, et al. Identifying villages and breeding habitats for dengue transmission in Thailand: insights from long-term larval surveys. BMC Infect Dis 2024; 24(1): 523. https://doi.org/10.1186/s12879-024-09398-7

12.

Yuan

Wang

. Characteristics, hotspots, and prospects of short video research: A review of papers published in China from 2012 to 2022. Heliyon 2024; 10(3): e24885. https://doi.org/10.1016/j.heliyon.2024.e24885

13.

Gao

Yin

Peng

, et al. Effectiveness of Social Video Platforms in Promoting COVID-19 Vaccination Among Youth: A Content-Specific Analysis of COVID-19 Vaccination Topic Videos on Bilibili. Risk Manag Healthc Policy 2022; 15: 1621–1639. https://doi.org/10.2147/RMHP.S374420

14.

Basch

Fera

Pierce

, et al. Promoting Mask Use on TikTok: Descriptive, Cross-sectional Study. JMIR Public Health Surveill 2021; 7(2): e26392. https://doi.org/10.2196/26392

15.

Rowdhwal

SSS

El Boghdady

. Exploring TikTok's influence on surgical patient education. Surgeon 2025; 23(5): 265–276. https://doi.org/10.1016/j.surge.2025.03.001

16.

Gao

Liu

Gao

. Echo chamber effects on short video platforms. Sci Rep 2023; 13(1): 6282. https://doi.org/10.1038/s41598-023-33370-1

17.

Xing

Zhang

, et al. Evaluation of the information quality related to osteoporosis on TikTok. BMC Public Health 2024; 24(1): 2880. https://doi.org/10.1186/s12889-024-20375-2

18.

Wang

Yang

Shu

, et al. TikTok as a potential patient educational tool for thyroid-associated ophthalmopathy: A cross-sectional study. Digit Health 2024; 10: 20552076241304594. https://doi.org/10.1177/20552076241304594

19.

Huang

Wang

Wei

, et al. Assessing the quality of educational short videos on dry eye care: a cross-sectional study. Front Public Health 2025; 13: 1542278. https://doi.org/10.3389/fpubh.2025.1542278

20.

Singh

. YouTube for information on rheumatoid arthritis--a wakeup call? J Rheumatol 2012; 39(5): 899–903. https://doi.org/10.3899/jrheum.111114

21.

Basim

Argun

. A Qualitative Analysis of Ostomy-Related Patient Education Videos on YouTube. Adv Skin Wound Care 2021; 34(6): 314–320. https://doi.org/10.1097/01.ASW.0000744340.11915.93

22.

Uprak

Ergenç

. Assessment of Esophagectomy Videos on YouTube: Is Peer Review Necessary for Quality? J Surg Res 2022; 279: 368–373. https://doi.org/10.1016/j.jss.2022.06.037

23.

Lian

Pan

, et al. Assessing the quality of breast cancer-related videos on TikTok: A cross-sectional study. Digit Health 2024; 10: 20552076241277688. https://doi.org/10.1177/20552076241277688

24.

Andika

Kao

Williams

, et al. YouTube as a source of information on the COVID-19 pandemic. J Community Hosp Intern Med Perspect 2021; 11(1): 39–41. https://doi.org/10.1080/20009666.2020.1837412

25.

Talie

Bogale

Anagaw

. The role of social media on COVID-19 preventive behaviors worldwide, systematic review. PLoS One 2024; 19(7): e0306284. https://doi.org/10.1371/journal.pone.0306284

26.

Lei

Liao

, et al. Quality and reliability evaluation of pancreatic cancer-related video content on social short video platforms: a cross-sectional study. BMC Public Health 2025; 25(1): 1919. https://doi.org/10.1186/s12889-025-23130-3

27.

Ding

Kong

Sun

, et al. Health information in short videos about metabolic dysfunction-associated steatotic liver disease: Analysing quality and reliability. Liver Int 2024; 44(6): 1373–1382. https://doi.org/10.1111/liv.15871

28.

Zhu

Wang

, et al. Information quality of videos related to esophageal cancer on tiktok, kwai, and bilibili: a cross-sectional study. BMC Public Health 2025; 25(1): 2245. https://doi.org/10.1186/s12889-025-23475-9

29.

Zheng

Tong

Wan

, et al. Quality and Reliability of Liver Cancer-Related Short Chinese Videos on TikTok and Bilibili: Cross-Sectional Content Analysis Study. J Med Internet Res 2023; 25: e47210. https://doi.org/10.2196/47210

30.

Sanghavi

Sachdev

, et al. Social Media as a Tool for Oral Health Promotion: An Umbrella Review of Systematic Reviews and Content-Analysis Studies Across Digital Platforms. Cureus 2025; 17(7): e87962. https://doi.org/10.7759/cureus.87962

31.

Suarez-Lledo

Alvarez-Galvez

. Prevalence of Health Misinformation on Social Media: Systematic Review. J Med Internet Res 2021; 23(1): e17187. https://doi.org/10.2196/17187

32.

Terry

Yang

Yao

, et al. The role of social media in public health crisis caused by infectious disease: a scoping review. BMJ Glob Health 2023; 8(12): e013515. https://doi.org/10.1136/bmjgh-2023-013515

33.

Dwyer

Vavaroutsos

Lutterman

, et al. Empowerment evaluation in redesigning a public health unit nutrition program. Can J Diet Pract Res 2006; 67(1): 36–40. https://doi.org/10.3148/67.1.2006.36

34.

Langille

Bernard

Rodgers

, et al. Systematic review of the quality of patient information on the internet regarding inflammatory bowel disease treatments. Clin Gastroenterol Hepatol 2010; 8(4): 322–328. https://doi.org/10.1016/j.cgh.2009.12.024

35.

Forsey

Rowland

, et al. The Basic Science of Patient-Physician Communication: A Critical Scoping Review. Acad Med 2021; 96(11S): S109–S118. https://doi.org/10.1097/ACM.0000000000004323

36.

Moezzi

Rasekh

Zare

, et al. Evaluating clinical communication skills of medical students, assistants, and professors. BMC Med Educ 2024; 24(1): 19. https://doi.org/10.1186/s12909-023-05015-4

37.

Zhao

Yang

Menta

, et al. Exploring the Use of Social Media for Medical Problem Solving by Analyzing the Subreddit r/medical_advice: Quantitative Analysis. JMIR Infodemiology 2025; 5: e56116. https://doi.org/10.2196/56116

38.

Schlopsna

Scheersoi

. Understanding student engagement in vaccination education: an interview-based multi-stakeholder study. Front Public Health 2024; 12: 1485498. https://doi.org/10.3389/fpubh.2024.1485498

39.

Pattison

Reinfelde

Chang

, et al. Finding the facts in an infodemic: framing effective COVID-19 messages to connect people to authoritative content. BMJ global health 2022; 7(2): e007582. https://doi.org/10.1136/bmjgh-2021-007582

40.

Guo

Wirza

Zawawi

, et al. Identifying determinants for promoting public engagement via Chinese E-government TikTok in public health emergencies. PLoS One 2025; 20(6): e0325967. https://doi.org/10.1371/journal.pone.0325967

41.

Lin

Zhu

, et al. The Most Popular Videos Promoting Breast Enhancement Products on TikTok: Cross-Sectional Content and User Engagement Analysis. J Med Internet Res 2025; 27: e73336. https://doi.org/10.2196/73336

42.

Zhong

Deng

, et al. Assessment of information quality and reliability on ankle sprains in short videos from Douyin and Bilibili. Sci Rep 2025; 15(1): 22654. https://doi.org/10.1038/s41598-025-07656-5

43.

Peng

, et al. Comparative analysis of NAFLD-related health videos on TikTok: a cross-language study in the USA and China. BMC Public Health 2024; 24(1): 3375. https://doi.org/10.1186/s12889-024-20851-9

44.

Alter

Charney

Karle

, et al. An Evaluation of Quality, Reliability, and Accuracy of Vocal Health Content on TikTok. J Voice. https://doi.org/10.1016/j.jvoice.2023.12.022

45.

Demirtas Karaoba

Candiri

Talu

. Examining the Contents of Pes Planus Exercise Videos and Evaluating Quality and Reliability. Foot Ankle Spec 2025; 19386400251359402. https://doi.org/10.1177/19386400251359402

46.

Tsulukidze

Grande

Naslund

. An Active Model of Research Translation for the General Public: Content Analysis of a YouTube-Based Health Podcast. JMIR Form Res 2023; 7: e46611. https://doi.org/10.2196/46611

47.

Dadgostar

Qin

Cui

, et al. Using Social Media to Disseminate Behavior Change Interventions: Scoping Review of Systematic Reviews. J Med Internet Res 2025; 27: e57370. https://doi.org/10.2196/57370