Health information quality of gallbladder cancer videos on TikTok and Bilibili in China: A cross-sectional content analysis

Abstract

Backgrounds

Gallbladder cancer is a highly invasive malignant tumor characterized by challenging early diagnosis and poor prognosis. With the widespread adoption of short-video platforms in China, the public increasingly accesses health information through channels such as TikTok (Chinese version) and Bilibili. However, the quality and reliability of gallbladder cancer-related videos on these platforms have not been systematically evaluated.

Objective

This study aims to evaluate content characteristics, information quality, and reliability of gallbladder cancer-related videos on TikTok and Bilibili in China, thereby providing evidence-based guidance for optimizing health information dissemination through short video content.

Methods

A total of 158 videos (99 from TikTok, 59 from Bilibili) were included in the final analysis. We extracted basic information and user interaction data from these videos. Video quality, reliability, and information coverage were assessed using the Global Quality Scale (GQS), modified DISCERN tool (mDISCERN), and Content Completeness Score (CS). Nonparametric statistical methods and chi-square tests were used for data analysis.

Results

Regarding general information, Bilibili videos are notably longer (170 seconds vs. 82 seconds, p < 0.001), while TikTok videos achieve higher scores across all engagement metrics (likes: 391 vs. 10, p < 0.001; collections: 110 vs. 9, p < 0.001; comments: 67 vs. 1, p < 0.001; shares: 74 vs. 7, p < 0.001). Regarding uploader types, TikTok predominantly featured specialist physicians (66.67%), while Bilibili primarily showcased knowledge disseminators (33.90%). Regarding quality scores, TikTok videos demonstrated significantly higher mDISCERN scores than Bilibili videos (p = 0.049), while Bilibili videos achieved significantly higher CS scores (p = 0.030). Additionally, the identity of content creators is a key determinant of video quality. Video engagement metrics bear no relation to video quality scores (GQS, mDISCERN and CS).

Conclusion

In summary, TikTok videos are more interactive, whereas Bilibili videos tend to be longer and offer more comprehensive content. However, videos on both platforms suffer from insufficient information completeness and inconsistent quality, with popularity failing to reflect scientific accuracy. It is recommended that platforms, healthcare professionals and content creators collaborate to collectively enhance the overall quality and dissemination effectiveness of health information.

Keywords

gallbladder cancer short videos information quality reliability mDISCERN TikTok Bilibili

1. Introduction

Gallbladder cancer is a highly invasive malignant tumor originating in the biliary tract system. Its incidence exhibits significant geographical variation worldwide, with a particularly high burden in East Asia and parts of South America.^1,2 Due to the insidious nature of early symptoms and the lack of effective screening methods, most patients are diagnosed at an advanced stage, with a poor overall prognosis and a five-year survival rate of merely 5% to 10%.^3–5 This grim clinical reality underscores the urgent need to enhance public awareness of gallbladder cancer and promote its early diagnosis and standardized treatment.⁶

In recent years, the digital health sector has undergone profound transformation. Short-video platforms like YouTube, TikTok and Bilibili have surpassed traditional media, becoming key channels for the public—particularly younger demographics—to access health information and medical knowledge.^7,8 These platforms demonstrate significant advantages in the breadth and efficiency of information dissemination through their visual content presentation, high interactivity, and algorithm-driven information distribution models.⁹ However, their openness and low content entry barriers also present significant challenges. Existing research indicates that the quality of health information on these platforms varies considerably, with widespread issues including inaccuracies, partiality, and even misleading content. Such information, particularly misinformation, may lead patients to make inappropriate health decisions and self-management choices, thereby causing delays in diagnosis and treatment, reduced adherence to evidence-based medicine, and even adverse outcomes.^10–12 As short videos increasingly influence health information dissemination, researchers have begun conducting systematic assessments of information quality across multiple disease domains. For instance, recent studies have examined the quality and reliability of videos related to esophageal cancer, pancreatic cancer, breast cancer, and prostate cancer on platforms like YouTube, TikTok, and Bilibili.^9,13–15 These investigations not only highlight the potential contributions of short videos in disseminating medical knowledge but also point out their widespread limitations, thereby providing reference points for health education initiatives targeting these diseases. However, systematic empirical research on the information quality of short videos concerning gallbladder cancer remains scarce.

Given gallbladder cancer’s high malignancy and poor prognosis, coupled with the public’s growing demand for reliable health information, systematically evaluating the content quality of short videos in this field holds significant practical importance and urgency.¹⁶ Moreover, structured analytical approaches have increasingly been applied in large-scale real-world health data studies—including national outpatient prescription surveillance and population-level health analytics—highlighting the importance of systematic data evaluation frameworks in digital health research.¹⁷ Therefore, this study aims to conduct a cross-sectional investigation to systematically analyze the video content, information quality, and reliability of gallbladder cancer-related short videos on TikTok and Bilibili platforms. The findings will provide directional guidance for patient education to clinicians, offer evidence-based support for optimizing health content governance on platforms, and ultimately contribute to enhancing public health literacy.

The subsequent sections of this paper are as follows: Section 2 outlines the search strategy, video classification criteria, quality assessment tools, and statistical methods. Section 3 presents characteristics of included videos, comparative results across platforms, and displays quality and reliability assessment findings stratified by uploaders and content category. Section 4 discusses key findings in light of existing literature, articulates implications for public health communication and clinical practice, offers recommendations for patients, healthcare professionals, and platform policymakers, and acknowledges study limitations. Finally, Section 5 summarizes the core findings and conclusions of this paper.

2. Methods

2.1. Ethical considerations

This study analyzed publicly accessible videos (Tiktok and Bilibili) and did not involve human participants, clinical data, biological samples, or animal experiments. All videos included in the analysis were publicly available at the time of data collection. Data extraction was restricted to non-identifiable video information and was subject to standardized reliability assessments based on displayed content. No personally identifiable information was collected or reported, nor were any verbatim comments analyzed or cited. Under these conditions, no formal institutional ethical review was required.

2.2. Search strategy and data processing

This cross-sectional study involved 220 videos from two platforms: TikTok (https://www.tiktok.com) and Bilibili (https://www.bilibili.com). On 2 October 2025, we searched using the keyword “胆囊癌” (Gallbladder Cancer) and collected the top 110 videos under the platform’s ‘Comprehensive Ranking’ order (the most commonly used sorting method by users) from each platform. This strategy was developed based on previous research methodologies, where findings indicate that videos exceeding the threshold (100) do not significantly impact results. Selecting the top 110 entries ensures both the representativeness of the sample and ample scope for subsequent exclusion of non-compliant videos.⁹ To avoid bias from personalized recommendations, all searches were conducted in visitor mode while logged out, with browser cache and search history cleared beforehand. Video content must be in Chinese or English with Chinese subtitles.

After the initial retrieval, we excluded videos irrelevant to the gallbladder cancer topic (merely mentioning the disease name or focusing on other conditions without including any information about gallbladder cancer) and duplicates. Specifically, 11 videos were removed from TikTok (8 irrelevant, 3 duplicates) and 51 from Bilibili (37 irrelevant, 14 duplicates), yielding 158 videos (99 videos are from TikTok and 59 are from Bilibili) as the final analysis sample (Figure 1). On 2 October 2025, we systematically extracted raw data from all 158 videos, including video titles, URLs, uploader information (name and type), video content type, video duration, and multiple user engagement metrics (number of likes, saves, comments, and shares). All extracted data were recorded in a Microsoft Excel spreadsheet. This methodology adheres to standard practices for data collection and analysis in cross-sectional studies, ensuring the reliability and validity of our research findings.¹⁸

Figure 1.

Search strategy and video filtering program.

2.3. Classification of videos

These videos are systematically categorized based on uploader types and video content types.

Based on account verification details and personal profiles, uploaders are divided into the following 5 types:

(1) Specialist physicians: Physicians certified by formal medical institutions in oncology, biliary tract surgery, hepatobiliary medicine, or gastroenterology; (2) Non-specialist physicians: Physicians certified by formal medical institutions practicing in other specialties; (3) Knowledge disseminators: Creators specializing in medical knowledge popularization or content curation, possessing relevant professional backgrounds though not licensed physicians; (4) General users: Individuals without institutional affiliation, including gallbladder cancer patients and their families; (5) Institutions: Hospitals, news organizations, general media outlets, for-profit entities, and non-profit organizations.

Video content types are categorized into 4 types:

(1) Medical science communication: Videos explaining general medical knowledge related to gallbladder cancer (e.g., causes, symptoms, diagnosis, prevention); (2) Professional knowledge: Videos covering specialized medical knowledge, professional courses, or recordings of academic conferences/lectures related to gallbladder cancer; (3) Authentic outpatient scenarios: Videos authentically documenting gallbladder cancer patients communicating with doctors during outpatient visits (real scenarios, not simulated); (4) Other content: Videos not falling into the above categories, such as personal experiences of gallbladder cancer patients, advertisements, or news reports.

2.4. Video quality and reliability assessments

The overall quality of video content was assessed using GQS. This scale is a recognized tool for evaluating the quality of health information on digital video platforms, primarily assessing the overall quality and practical value of videos. It focuses on content clarity, coherence, and its guidance for patients. GQS comprises 5 criteria, scored on a scale of 1 to 5, with higher scores indicating better quality. The reliability of video content was assessed using mDISCERN, which focuses on the dependability of health information. It evaluates the clarity of video’s purpose, the objectivity of evidence sources, and the manner in which diagnostic or therapeutic uncertainties are presented. mDISCERN comprises 5 items, employing a binary scoring method (Yes = 1 point, No = 0 points) to assess these 5 aspects of video. The total mDISCERN score ranges from 0 to 5 points, with a higher score indicating greater overall reliability.^11,19 The specific evaluation criteria for GQS and mDISCERN are presented in the Supplementary Material S1.

Additionally, to further evaluate the content coverage of the video, we applied a tool—the content integrity score (CS). This tool was originally developed by Zhang et al.²⁰ for colorectal cancer videos and later applied to gastric¹¹ and esophageal cancer⁹ video analysis. In this study, we adapted it to the gallbladder cancer context. In the absence of standardized tools for evaluating gallbladder cancer-related videos, we retained the core domains—“Etiology,” “Symptoms,” “Diagnosis,” and “Treatment”—while adding ‘Epidemiology’ and “Prognosis” to form six mutually exclusive content categories, ensuring conceptual clarity. Scoring criteria for each category were: 0 points for no mention, 1 point for brief mention, and 2 points for detailed explanation. The interpretation criteria for the total CS score (range 0–12 points) are as follows: 0–3 points (low) indicates limited valuable information; 4–8 points (moderate) indicates moderate amount of valuable information; 9–12 points (high) indicates coverage of most key information.

All videos were independently assessed by 2 reviewers (T.S. and R.Z.), both of whom possess specialist qualifications in oncology and gastroenterology respectively (with over 3 years’ professional experience). Prior to formal scoring, both reviewers received standardized training on GQS, the mDISCERN tool, and CS scoring. Subsequently, the two evaluators independently scored 158 videos. In cases of disagreement, a third reviewer—a senior oncologist (Ya.Z.) with over 10 years of experience in gallbladder cancer diagnosis and treatment—arbitrated. This expert reviewed the videos and initial scores, facilitated consensus discussions, and determined the final scores based on clinical guidelines and evidence-based practice.^21,22

To assess inter-rater reliability, this study employed weighted Kappa coefficients for GQS consistency testing and intraclass correlation coefficients (ICC) for mDISCERN and CS. Results demonstrated excellent inter-rater reliability: the weighted Kappa value for GQS was 0.811 (95% Confidence Interval (CI): 0.728–0.895); ICC for mDISCERN and CS were 0.930 (95% CI: 0.906–0.949), and 0.922 (95% CI: 0.894–0.942), respectively.

2.5. Statistical analysis

Data processing and analysis were performed using R version 4.3.3, along with Zstats 1.0 (https://www.zstats.net). The Shapiro-Wilk test assessed data normality. As none of the data met normality assumptions, subsequent analyses employed non-parametric testing methods. Given nonparametric data characteristics, continuous variables were expressed as median (M) and interquartile range (IQR: Q₁, Q₃), while categorical variables were presented as frequency and percentage. Intergroup comparisons of continuous variables were performed using the Mann-Whitney U test (two groups) or the Kruskal-Wallis H test (three or more groups). Comparisons of categorical variables were conducted using the chi-square test. Spearman’s rank correlation analysis was used to examine correlations between video variables (video duration and engagement metrics) and quality scores (GQS, mDISCERN, CS). P values < 0.05 indicated statistically significant differences.

3. Results

3.1. Video characteristics

As shown in Table 1, the median duration of Bilibili videos was significantly longer than that of TikTok videos (170 seconds vs. 82 seconds, p < 0.001); In contrast, TikTok videos demonstrated significantly higher user engagement metrics across all indicators compared to Bilibili, including likes (391 vs. 10, p < 0.001), collections (110 vs. 9, p < 0.001), comments (67 vs. 1, p < 0.001), and shares (74 vs. 7, p < 0.001). These findings reflect the significant differences in content formats between the two platforms: Bilibili primarily features long-form video content, while TikTok focuses more on highly interactive short-form videos.

Table 1.

Detailed information and comparative analysis of general information, uploader type, video type and video content of gallbladder cancer-related videos on TikTok and Bilibili.

Variables	Total (n = 158)	TikTok (n = 99)	Bilibili (n = 59)	Statistic	p
General information
Video duration (s), M (Q₁, Q₃)	90.50 (60.25, 158.75)	82.00 (53.50, 110.50)	170.00 (80.50, 437.00)	Z=-5.06	<.001
Likes, M (Q₁, Q₃)	150.50 (23.25, 876.50)	391.00 (118.50, 1610.50)	10.00 (5.00, 44.00)	Z=-7.95	<.001
Collections, M (Q₁, Q₃)	41.00 (8.25, 308.50)	110.00 (25.00, 532.50)	9.00 (4.50, 44.00)	Z=-5.76	<.001
Comments, M (Q₁, Q₃)	16.50 (2.00, 116.50)	67.00 (12.00, 277.50)	1.00 (0.00, 9.50)	Z=-7.24	<.001
Shares, M (Q₁, Q₃)	25.50 (4.25, 148.75)	74.00 (14.50, 367.50)	7.00 (1.00, 19.00)	Z=-6.28	<.001
Uploader				-	< 0.001
Specialist physicians, n (%)	79 (50.00)	66 (66.67)	13 (22.03)
Non-specialist physicians, n (%)	42 (26.58)	26 (26.26)	16 (27.12)
Knowledge disseminators, n (%)	20 (12.66)	0 (0.00)	20 (33.90)
General users, n (%)	10 (6.33)	6 (6.06)	4 (6.78)
Institutions, n (%)	7 (4.43)	1 (1.01)	6 (10.17)
Video Type				χ²=31.11	<.001
Medical science communication, n (%)	85 (53.80)	55 (55.56)	30 (50.85)
Professional knowledge, n (%)	15 (9.49)	0 (0.00)	15 (25.42)
Authentic outpatient scenarios, n (%)	45 (28.48)	36 (36.36)	9 (15.25)
Other content, n (%)	13 (8.23)	8 (8.08)	5 (8.47)
Video content (1/2)
Epidemiology, n (%)	24 (15.19)	10 (10.10)	14 (23.73)	χ²=5.33	0.021
Etiology, n (%)	76 (48.10)	45 (45.45)	31 (52.54)	χ²=0.74	0.388
Symptoms, n (%)	42 (26.58)	23 (23.23)	19 (32.20)	χ²=1.52	0.217
Diagnosis, n (%)	59 (37.34)	36 (36.36)	23 (38.98)	χ²=0.11	0.742
Treatment, n (%)	93 (58.86)	62 (62.63)	31 (52.54)	χ²=1.55	0.213
Prognosis, n (%)	49 (31.01)	26 (26.26)	23 (38.98)	χ²=2.80	0.095

Z: Mann-Whitney test, χ²: Chi-square test, -: Fisher exact.

M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile.

3.2. Video sources and type distribution

Table 1 and Figure 2 present the distribution of uploader types and content types for gallbladder cancer-related videos on TikTok and Bilibili.

Figure 2.

Video sources and types on gallbladder cancer-related videos from TikTok and Bilibili. (a, c) Circular Pie chart showing the percentage of uploader types and video types on all platforms; (b, d) percentage stacked bar chart showing the percentage of uploader types and video types on different platforms (TikTok and Bilibili).

The distribution of uploader types differed significantly between 2 platforms (p < 0.001). On TikTok, specialist physicians were the primary content source (n = 66, 66.67%), while this category accounted for a relatively low proportion on Bilibili (n = 13, 22.03%). Conversely, knowledge disseminators were the most common uploader type on Bilibili (n = 20, 33.90%), while this category was absent on TikTok (n = 0, 0.00%).

Regarding video content types, medical science communication videos dominated on both platforms (TikTok: n = 55, 55.56%; Bilibili: n = 30, 50.85%). However, the second most common content type differed by platform: TikTok featured primarily authentic outpatient scenarios (n = 36, 36.36%), while Bilibili featured primarily professional knowledge videos (n = 15, 25.42%). Notably, professional knowledge videos were not included on the TikTok platform.

3.3. Video contents

As shown in Table 1, “Treatment” (TikTok: n = 62, 62.63%; Bilibili: n = 31, 52.54%) and “Etiology” (TikTok: n = 45, 45.45%; Bilibili: n = 31, 52.54%) were the two most frequently covered content categories across both platforms. On TikTok, “Diagnosis” (n = 36, 36.36%) and “Prognosis” (n = 26, 26.26%) ranked third and fourth, respectively; on Bilibili, ‘Diagnosis’ (n = 23, 38.98%) and “Prognosis” (n = 23, 38.98%) tied for third place. “Symptoms” (TikTok: n = 23, 23.23%; Bilibili: n = 19, 32.20%) and “Epidemiology” (TikTok: n = 10, 10.10%; Bilibili: n = 14, 23.73%) were the least covered content categories across both platforms. Chi-square test results revealed a statistically significant difference between platforms only in the coverage distribution of the “epidemiology” category (p = 0.021). No statistically significant differences were found in the coverage distributions of the remaining five content categories (p > 0.05), indicating overall similarity in the coverage focus of gallbladder cancer content across platforms. Figure 3 further illustrates the completeness of information in gallbladder cancer-related videos across different platforms through coverage depth.

Figure 3.

Content coverage depth by category of gallbladder cancer-related videos on TikTok and Bilibili platforms.

3.4. Video quality and reliability

Table 2 details the video quality scores across both platforms. Statistical analysis revealed no significant difference in GQS scores between the two platforms (p > 0.05), while significant statistical differences were observed in mDISCERN (p = 0.049) and CS (p = 0.030) scores. Specifically, TikTok videos (M: 2, Q₁: 2, Q₃: 2) outperformed Bilibili videos (M: 2, Q₁: 1, Q₃: 2) in mDISCERN scores, indicating higher reliability. Conversely, Bilibili videos (M: 3, Q₁: 1.5, Q₃: 5) outperformed TikTok (M: 2, Q₁: 1, Q₃: 3) in CS scores, reflecting more comprehensive content coverage. Further details are provided in Supplementary Material S2 (Table S1).

Table 2.

Comparative analysis of quality scores (GQS, mDISCERN and CS) of gallbladder cancer-related videos on TikTok and Bilibili.

Variables	Total (n = 158)	TikTok (n = 99)	Bilibili (n = 59)	Statistic	p
Video quality
GQS, M (Q₁, Q₃)	3.00 (2.00, 3.00)	3.00 (2.00, 3.00)	3.00 (2.50, 3.00)	Z=-1.51	0.130
mDISCERN, M (Q₁, Q₃)	2.00 (2.00, 2.00)	2.00 (2.00, 2.00)	2.00 (1.00, 2.00)	Z=-1.97	0.049
CS, M (Q₁, Q₃)	2.00 (1.00, 4.00)	2.00 (1.00, 3.00)	3.00 (1.50, 5.00)	Z=-2.17	0.030

Z: Mann-Whitney test.

M: Median, Q₁: 1st Quartile, Q₂: 3st Quartile.

3.5. Comparisons between videos from different uploaders and between different types of videos

As shown in Table 3, the median GQS score (3.5 points) was highest in the knowledge disseminator group, followed by specialist physicians, non-specialist physicians, and institutions (all with a median of 3 points). The median GQS score was lowest among general users (2 points). Regarding mDISCERN scores, the median mDISCERN score (2 points) was higher for specialist physicians, non-specialist physicians, and knowledge disseminators than for general users and institutions (1 point). CS scores were highest among knowledge disseminators (median: 5.5 points), followed by institutions (median: 4 points), specialist physicians (median: 3 points), non-specialist physicians (median: 2 points), and general users (median: 1 point). Further details are provided in Supplementary Material S2 (Table S2 and Figure S1).

Table 3.

Comparative analysis of quality scores (GQS, mDISCERN and CS) of gallbladder cancer-related videos across different uploaders.

Variables	Total (n = 158)	Specialist physicians (n = 79)	Non-specialistphysicians (n = 42)	Knowledge disseminators (n = 20)	General users (n = 10)	Institutions (n = 7)	Statistic	p
Video quality
GQS, M (Q₁, Q₃)	3.00 (2.00, 3.00)	3.00 (3.00,3.00)	3.00 (2.00,3.00)	3.50 (3.00,4.00)	2.00 (1.00,2.00)	3.00 (2.00,4.00)	χ²=38.19#	<.001
mDISCERN, M (Q₁, Q₃)	2.00 (2.00, 2.00)	2.00 (2.00,2.00)	2.00 (2.00,2.00)	2.00 (1.00,3.00)	1.00 (1.00,1.00)	1.00 (1.00,2.00)	χ²=35.98#	<.001
CS, M (Q₁, Q₃)	2.00 (1.00, 4.00)	3.00 (2.00,4.00)	2.00 (2.00,3.00)	5.50 (2.75,7.00)	1.00 (0.00,1.00)	4.00 (1.50,5.00)	χ²=32.19#	<.001

#: Kruskal-waills test, χ²: Chi-square test, -: Fisher exact.

M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile.

As shown in Table 4, the median GQS score (3 points) for medical science communication, professional knowledge, and authentic outpatient scenarios videos was higher than that of other content groups (median: 1 point). Regarding mDISCERN scores, medical science communication and authentic outpatient scenarios videos had a higher median mDISCERN score (2 points) than the professional knowledge group and other content groups (median: 1 point). CS scores were highest for professional knowledge videos (median: 5 points), followed by medical science communication videos (median: 3 points), authentic outpatient scenarios videos (median: 2 points), and other content videos (median: 1 point). Further details are provided in Supplementary Material S2 (Table S3 and Figure S2).

Table 4.

Comparative analysis of quality scores (GQS, mDISCERN and CS) of gallbladder cancer-related videos across different video types.

Variables	Total (n = 158)	Medical science communication (n = 85)	Professional knowledge (n = 15)	Authentic outpatient scenarios (n = 45)	Other content (n = 13)	Statistic	p
Video quality
GQS, M (Q₁, Q₃)	3.00 (2.00, 3.00)	3.00 (3.00,3.00)	3.00 (3.00,4.00)	3.00 (2.00,3.00)	1.00 (1.00,2.00)	χ²=64.25#	<.001
mDISCERN, M (Q₁, Q₃)	2.00 (2.00, 2.00)	2.00 (2.00,2.00)	1.00 (1.00,2.00)	2.00 (2.00,2.00)	1.00 (1.00,1.00)	χ²=45.75#	<.001
CS, M (Q₁, Q₃)	2.00 (1.00, 4.00)	3.00 (2.00,4.00)	5.00 (3.00,7.00)	2.00 (1.00,3.00)	1.00 (0.00,1.00)	χ²=49.62#	<.001

#: Kruskal-waills test, χ²: Chi-square test, -: Fisher exact.

M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile.

3.5. Spearman’s correlation analysis

Spearman correlation analysis was employed to investigate the associations between various video features and quality scores of gallbladder cancer-related videos (Figure 4). Results revealed strong positive correlations among user interaction metrics (likes, collections, comments, and shares). Additionally, moderate-to-strong positive correlations were observed among the three video quality scores (GQS, mDISCERN, CS). Video duration showed moderate positive correlations with both GQS scores (correlation coefficient: 0.38) and CS scores (correlation coefficient: 0.46). Subgroup analysis indicates that the results of video relevance analysis on TikTok and Bilibili platforms are largely consistent with the overall study findings (Supplementary Material S2, Figure S3 and Figure S4).

Figure 4.

Spearman correlation analysis between various video features and quality scores (GQS, mDISCERN, and CS) across all platforms (TikTok and Bilibili).

4. Discussion

This study represents the first systematic comparison and evaluation of content characteristics, information quality and reliability, and content completeness in gallbladder cancer-related videos across China’s two major short-video platforms—TikTok and Bilibili. Our findings reveal complex associations between platform characteristics, uploader types, content types, and the quality of short video information related to gallbladder cancer, offering significant implications for the dissemination and application of digital health information.

4.1. Platform ecosystem and content differentiation

One of the key findings of this study is that TikTok and Bilibili exhibit fundamental differences in their content ecosystems for gallbladder cancer-related videos. TikTok videos are characterized by high interactivity and short duration, with content primarily created by certified physicians and focused on medical science communication and authentic outpatient scenarios. This phenomenon aligns closely with TikTok’s algorithm-driven, fast-consumption model, which favors promoting content capable of rapidly capturing attention.^23,24 In contrast, Bilibili exhibits an ecosystem centered around knowledge disseminators, featuring significantly longer videos. Its content demonstrates markedly superior information completeness compared to TikTok. This aligns with Bilibili’s positioning as a knowledge community platform and its inclusivity toward medium-to-long-form videos, as its user base exhibits higher receptivity to in-depth educational content.^8,25–27

4.2. Multidimensional evaluation of information quality

This study employed GQS, mDISCERN, and CS scores to conduct a multidimensional assessment of video quality, revealing the strengths and weaknesses of different platforms across various quality dimensions. A key finding is that TikTok videos scored slightly higher than Bilibili on the mDISCERN reliability metric, while Bilibili achieved significantly higher CS scores. This suggests that short videos may focus more on conveying core, verifiable conclusions within limited time frames, resulting in acceptable reliability.²⁸ However, their inherent brevity restricts content breadth and depth, leading to insufficient content integrity.^29–31 This paradox highlights the limitations of using single-score tools when evaluating health information in short videos, whereas integrating multiple metrics provides more comprehensive insights.

Notably, user engagement metrics (likes, shares, etc.) showed no robust positive correlation with objective quality scores, consistent with prior research findings.^32–34 This indicates that a video’s popularity cannot reliably substitute for its scientific quality. Audiences may be drawn to content based on its entertainment value, emotional resonance, or perceived authority, rather than its intrinsic informational merit.^35,36

4.3. The core role of content creators

Our findings strongly confirm that the identity of content creators is one of the most critical determinants of information quality. Compared to videos created by general users, those produced by specialist physicians and non-specialist physicians consistently scored higher on both GQS and CS metrics. This finding aligns with the conclusions of numerous previous studies on other diseases, underscoring the importance of professional expertise in ensuring health information accuracy.^37–39 Particularly noteworthy is the group of knowledge disseminators, who play a vital role on the Bilibili platform and whose videos achieved the highest median CS scores. This indicates that science communicators bridge the gap in in-depth science communication provided by specialists and non-specialists by repurposing, organizing, and summarizing professional courses, academic conference proceedings, and other materials to translate and systematically integrate complex medical knowledge.⁹ However, the reliability scores (mDISCERN) for this group’s videos did not correspondingly stand out. This suggests the platform should establish mechanisms to encourage clearer citation of video sources, thereby further enhancing content credibility.⁴⁰

4.4. Professional specialization in content types and clinical gaps

This study’s analysis of video content types reveals potential trends in cross-platform professional specialization. The dominance of medical science communication content on both platforms reflects creators’ widespread efforts to translate professional knowledge into language accessible to the general public.⁴¹ However, significant differentiation exists between platforms: The popularity of authentic outpatient scenarios videos on TikTok may satisfy patients’ curiosity and emotional need for “real-world” doctor-patient interactions and treatment processes. Yet their shorter duration and lower CS scores indicate this content prioritizes process demonstration over in-depth education. In contrast, Bilibili’s unique category of knowledge-based videos (professional knowledge), while offering the highest information completeness, holds an extremely low share and sees mediocre user engagement. This highlights the significant challenge deep, specialized content faces in attracting a mass audience.⁴² A particularly critical finding is the severe lack of coverage regarding the “Epidemiology” and “Prognosis” of gallbladder cancer across all platforms and content types. This reflects how existing short-form content tends to prioritize immediate topics like “Diagnosis” and “Treatment,” while generally neglecting the disease’s broader context and long-term management.⁴³ This content gap may lead to public misunderstanding regarding disease incidence, risk factors, and post-treatment quality of life and lifestyle adjustments, hindering the establishment of early prevention and comprehensive health management awareness.⁴⁴

4.5. Clinical implications and policy implications

From a clinical practice perspective, the findings of this study underscore the necessity for healthcare providers to proactively understand patients’ habits of obtaining information from social media. Given the distinct content focuses and varying quality levels across the two platforms, physicians should guide patients to critically evaluate short-video information and recommend high-quality content published by certified medical professionals or authoritative institutions.

For platform operators, we recommend: (1) Establishing and promoting a certification system for healthcare content creators, verifying the credentials of licensed physicians and prioritizing their content in search results. (2) Optimizing recommendation algorithms by incorporating quality-based weighting into health content distribution, moving beyond reliance on engagement metrics alone to curb the over-circulation of low-quality information. (3) Encouraging the production of continuous, structured medical science content and establish relevant quality standards to overcome the inherent limitations of short videos in conveying in-depth information.

For health authorities and academic institutions: (1) Taking the lead in developing guidelines for disseminating medical science communication through short videos, providing authoritative direction for content creators. (2) Enhancing training in science communication for healthcare professionals, encouraging their participation in health communication. (3) Raising public awareness of public health issues and caution against overly simplistic health claims.

4.6. Implications for public health communication and cancer awareness

The findings hold broader implications for public health communication. Short-video platforms serve as potent tools for health education, demonstrating particularly significant impact among younger demographics⁷. However, our research reveals a disconnect between content popularity and scientific rigour—engaging material does not guarantee accuracy. This presents both opportunity and challenge: whilst platforms possess unprecedented reach, targeted strategies are needed to ensure evidence-based content competes with misleading information. For gallbladder cancer, the absence of epidemiological, symptomatic, and prognostic information signifies missed opportunities for early intervention; public health campaigns should prioritise coverage of these neglected topics. Finally, the disconnect between content popularity and quality underscores the imperative for media literacy interventions. Cancer awareness programmes should incorporate the educational principle of ‘prioritising creator credentials over engagement metrics’. Collaborative efforts between health authorities, platforms, and content creators are essential to empower the public in making informed health decisions.

4.7. Limitations and future directions

This study has several limitations. First, the cross-sectional design reflects only the content landscape at a specific point in time, whereas short-video platforms update content extremely rapidly. Future research could adopt longitudinal designs to track the evolution of content quality. Second, because video selection relied on platform ranking algorithms—which prioritize engagement over clinical accuracy—algorithm-driven visibility bias in content selection cannot be excluded. Concurrently, the researchers’ interpretations also introduced bias in both video selection and content analysis. Third, despite employing validated tools and ensuring good inter-rater reliability, quality assessment inevitably carries some degree of subjectivity. Currently, the field of health information quality assessment urgently requires the development of more objective, quantifiable standards. A promising future direction involves integrating artificial intelligence with natural language processing technologies. Fourth, all analyses in this study are based solely on the researchers’ perspective. Future research should incorporate user perspectives through methods like semi-structured interviews or surveys to explore how diverse audiences—including patients, family members, and the general public—perceive and evaluate video content. Finally, this study focuses on content from Chinese-language platforms, and the generalizability of its findings across different linguistic and cultural contexts requires further validation.

5. Conclusion

In summary, this study indicates that TikTok and Bilibili have become significant channels for the public to access gallbladder cancer information, yet they exhibit distinctly different content ecosystems and quality characteristics. TikTok excels in high interactivity and abundant certified physician resources, while Bilibili holds advantages in content depth and systematic presentation. Nevertheless, both platforms face multiple challenges: insufficient video information integrity, inconsistent quality, and the need to effectively enhance content appeal and public acceptability while improving video quality. The identity of content creators serves as a core predictor of video quality, and a video’s popularity does not reliably indicate its scientific accuracy. Users should exercise caution when seeking gallbladder cancer information on TikTok and Bilibili. It is recommended to prioritize content created by certified physicians over videos with higher user engagement metrics to avoid being misled by inaccurate information.

Supplemental material

Supplemental material - Health information quality of gallbladder cancer videos on TikTok and Bilibili in China: A cross-sectional content analysis

Supplemental material for Health information quality of gallbladder cancer videos on TikTok and Bilibili in China: A cross-sectional content analysis by Tao Sun, Ruping Zhao, Muyuan Guo, Yuanzhe Zhu, Yuling Zheng, Yaling Zhang in DIGITAL HEALTH

Supplemental material

Supplemental material - Health information quality of gallbladder cancer videos on TikTok and Bilibili in China: A cross-sectional content analysis

Footnotes

ORCID iDs

Tao Sun

Muyuan Guo

Author contributions

Tao Sun and Ruping Zhao designed this study and contributed to manuscript writing. Data collection was performed by Muyuan Guo, Ruping Zhao, and Yuanzhe Zhu. Tao Sun and Ruping Zhao conducted data analysis and visualization. Yaling Zhang and Yuling Zheng supervised the study implementation and performed the final review of the manuscript. All authors participated in editing and approved the final version submitted for publication.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The 4th National Chinese Medicine Expert Inheritance Studio Project (NO. [2022]75); Collaborative Innovation Center of Prevention and Treatment of Major Diseases by Chinese and Western Medicine, Henan Province (NO.[2023]413); Henan Province Natural Science Foundation Project (NO. 242300420435); Henan Traditional Chinese Medicine Research Special Project (NO. 2023ZY2018).

Declaration of conflicting interests

All authors declare no conflicts of interest that are directly relevant to the content of this manuscript.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.*

Supplemental material

Supplemental material for this article is available online.

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