The quality and reliability of short videos about premature ovarian failure on Bilibili and TikTok: Cross-sectional study

Abstract

Background

Premature ovarian failure (POF) is a condition where ovarian function ceases before age 40, leading to infertility and other health issues. As public health awareness increases, platforms like TikTok and Bilibili have become key sources for health-related content. However, the absence of peer review and regulatory oversight on short-video platforms may result in the spread of inaccurate or incomplete health information. This study evaluates the quality of videos related to POF on these platforms.

Methods

A cross-sectional study was conducted, analyzing 187 POF-related videos from TikTok and Bilibili. The modified DISCERN and Global Quality Score (GQS) assessment tools were used to evaluate video reliability and quality. Data on video characteristics, including engagement metrics and content, were also collected. Statistical analyses were performed to assess differences between platforms, video sources, and video quality.

Result

TikTok is more popular than Bilibili. Videos on both platforms related to POF had suboptimal quality, with TikTok's GQS median at 3 (2–3) and Bilibili's at 2 (1–3), showing a significant difference (p < 0.05). However, both had median modified DISCERN scores of 2 (1–3), with no significant difference (p > 0.05). On Bilibili, video duration was positively correlated with quality scores (p < 0.001), but no such correlation was found on TikTok. Symptoms of POF were the most common topic, with 29% of videos providing detailed explanations. Diagnosis and treatment were discussed in 16% and 7.4% of videos, respectively. Expert-uploaded videos demonstrated significantly higher quality than those from non-experts and personal users, with GQS and modified DISCERN scores of 3 (2–4) and 2 (1–3), respectively.

Conclusions

TikTok had higher engagement and better quality than Bilibili, but both platforms had inadequate video quality and reliability on POF. Expert videos were more reliable. These findings highlight the need for better regulation and monitoring of health content on short-video platforms.

Keywords

Premature ovarian failure short videos information quality social media TikTok Bilibili Global Quality Score modified DISCERN reliability

Introduction

Premature ovarian failure (POF) refers to the loss of ovarian function before the age of 40, resulting in the cessation of menstruation and infertility, with an increasing prevalence in recent years.¹ Beyond fertility issues, POF is associated with menopausal symptoms, such as hot flashes, osteoporosis, and an elevated risk of cardiovascular diseases, all of which significantly impact the overall health and quality of life of affected individuals.^2,3 Additionally, the onset of POF is often accompanied by psychological health issues, including anxiety and depression, making it a multidimensional public health challenge.^4,5 Therefore, early identification, intervention, and raising public awareness about POF are crucial for improving patient health and societal well-being.

In the digital age, the way the public accesses health information has undergone a significant transformation.^6,7 While traditional textual and academic resources remain authoritative, they are often difficult for the general public to understand. In contrast, short-video platforms like TikTok and Bilibili have emerged as key channels for disseminating health information due to their simplicity, vivid visuals, and interactivity.⁸ By presenting complex health issues in an easily understandable format and providing quick access to information, short videos cater to the public's demand for convenience and immediacy in health-related content.⁹ However, alongside the benefits of these platforms in spreading health information, users also face challenges regarding the quality and reliability of the content.¹⁰ Due to the lack of peer review and stringent regulatory mechanisms, many videos are of varying quality and may even propagate misleading or false information, which could mislead viewers’ understanding of diseases, treatments, and health practices.¹¹ This poses a significant risk of misinforming the public and negatively influencing health behaviors.^12–14 Despite the growing role of short-video platforms in health communication, the quality and reliability of content related to medical topics, such as POF, remain underexplored.

The rationale for our study lies in addressing this gap and understanding the quality of POF-related information on short-video platforms, which has become a primary source of health information for many individuals. Given the increasing reliance on online education for health matters, it is imperative to evaluate the accuracy and reliability of such content. Online education, especially through short videos, plays a crucial role in shaping public health knowledge and behaviors. Therefore, ensuring the quality of this educational content is of paramount importance.

This study aims to fill this gap by conducting a comprehensive evaluation of POF-related videos on TikTok and Bilibili. We hypothesize that short videos on POF may have suboptimal quality and reliability, potentially misleading viewers’ understanding of the condition. The objective is to analyze the scientific accuracy, reliability, and potential impact of these videos on public health education. By providing scientific evidence and a systematic analysis, this study seeks to contribute to improving the quality of health information on short-video platforms and support the development of policies and regulatory frameworks for platform governance.

Materials and methods

Search strategy and data extraction

The primary goal of this study was to gather short-video data on the topic of “Premature Ovarian Failure” from Bilibili (https://www.bilibili.com) and TikTok (https://www.douyin.com). To ensure the comprehensiveness and representativeness of the data, the Chinese term for “Premature Ovarian Failure” was used as the search keyword. Data collection was carried out separately on both platforms. To minimize bias from personalized recommendations, all searches were conducted under newly created accounts, and searches were performed in an incognito mode, ensuring that no historical data or personalized algorithms influenced the results. Initially, videos ranked in the top 100 on each platform were collected based on the default sorting order. Videos were included if they directly addressed the etiology, symptoms, diagnosis, treatment, or prevention of POF and were publicly accessible (excluding private or restricted content). Videos were excluded if they were identified as advertisements or promotional material, unrelated to POF or only mentioned POF superficially without substantive information, uploaded within the last week prior to data collection (to ensure data stability and timeliness), or duplicates/reuploads of previously analyzed content. During data extraction, the following variables were recorded: video source (Bilibili or TikTok), upload time, video duration (in seconds), number of likes, comments, shares, and collections (S1 Appendix). All data were extracted using the public APIs provided by the platforms, ensuring accuracy and consistency. These variables provided strong support for subsequent content analysis.

Video quality assessment

To evaluate the reliability and quality of the collected short videos, this study employed the modified DISCERN and Global Quality Score (GQS) assessments. Using the modified DISCERN, evaluators assessed whether each video met the following criteria: clarity, relevance, traceability, robustness, and fairness. Each criterion was rated as “yes” (1 point) or “no” (0 points), with a cumulative score calculated (ranging from 0 to 5 points).¹⁵ The GQS utilized a 5-point Likert scale to evaluate the overall quality of the video, with scores ranging from 1 (very poor) to 5 (very good). This scale considered aspects such as the professionalism of the video, the comprehensiveness of the information, the clarity of the presentation, and the viewer's ability to understand the content.¹⁶ Furthermore, the completeness of the videos was assessed based on whether they included the following information: epidemiology, etiology, symptoms, diagnosis, treatment, and prevention (S2 Appendix). Videos were also evaluated based on how thoroughly the uploader explained the relevant topics, with three possible ratings: no explanation (0 points), partial explanation (1 point), and full explanation (2 points). Specialists, including gynecologists and reproductive medicine doctors, were distinguished from non-specialists, who were mainly from other medical fields or traditional Chinese medicine. All evaluations were conducted by two assessors with relevant medical backgrounds, who underwent unified training prior to scoring to ensure consistency in evaluation standards and minimize bias.

Statistical analysis

This study employed descriptive statistics and nonparametric tests to analyze video characteristics and quality metrics. Categorical variables were presented as frequencies and percentages, whereas continuous variables were described using medians with interquartile ranges. Chi-square tests or Fisher's exact tests were utilized to assess differences in platform distributions, while Mann–Whitney U tests compared interaction data and quality scores between TikTok and Bilibili. For multi-group comparisons among expert, non-expert, and individual user videos, Kruskal–Wallis tests were applied, followed by post-hoc Dunn's tests for pairwise comparisons. Cohen's kappa coefficients evaluated inter-rater reliability for GQS and modified DISCERN scores, with kappa values ≥0.8 indicating excellent agreement. Spearman correlation analysis explored relationships between GQS and modified DISCERN scores. Statistical computations were conducted using IBM SPSS Statistics 27.0, while GraphPad Prism 10.4 generated visualizations. A significance threshold of p < 0.05 was adopted, with no Bonferroni correction applied to post-hoc comparisons to preserve exploratory power. This analytical framework integrated robust nonparametric methods with advanced statistical software, ensuring alignment with data distributions and clinical research standards.

Results

Video characteristics

Based on the established inclusion and exclusion criteria, the top 100 videos from TikTok and Bilibili were screened, resulting in a final sample of 187 videos. The detailed selection process is illustrated in Figure 1, while Table 1 presents the characteristics of the included videos. In terms of platform distribution, TikTok contributed 97 videos, while Bilibili provided 90. As for the creators, expert-uploaded videos were dominant, accounting for 61%, followed by non-expert videos at 19.8%, and personal user videos at 19.3% (Figure 2). Overall, the videos showed modest engagement metrics. The median number of likes, comments, collections, and shares per video were 73 (11–224), 11 (1–56), 22 (6–89), and 22 (3–52). The median video duration was 67 s (43–136). In terms of video quality, the median GQS score was 2 (1–3), and the median modified DISCERN score was also 2 (1–3). Inter-rater consistency was excellent, with Cohen's Kappa values of 0.916 for GQS and 0.938 for modified DISCERN scores.

Figure 1.

The flow chart of this study.

Figure 2.

Distribution of videos by source type on TikTok and Bilibili platforms.

Table 1.

Video characteristics.

Characteristic		N = 187
Short-video sharing platforms [n (%)]
	TikTok	97(51.9)
	Bilibili	90(48.1)
Video source [n (%)]
	Specialists	114(61)
	Non-specialists	37(19.8)
	Individual user	36(19.3)
Number of likes [median (IQR)]		73(11–224)
Number of comments [median (IQR)]		11(1–56)
Number of collections [median (IQR)]		22(6–89)
Number of shares [median (IQR)]		22(3–52)
Video duration [s, median (IQR)]		67(43–136)
GQS scores [median (IQR)]		2(1–3)
modified DISCERN scores [median (IQR)]		2(1–3)

GQS: Global Quality Score; IQR: interquartile range.

Video content

POF symptoms were the most frequently discussed topic, with 29% of videos providing detailed explanations. Diagnosis and treatment were covered in 16% and 7.4% of videos, respectively. Detailed information on epidemiology and etiology was rarely included, with only a few videos briefly mentioning these aspects (Table 2).

Table 2.

Completeness of video content.

Video contents	Not involve (0 points)	Partial explanation (1 point)	Full explanation (2 points)
Epidemiology, n (%)	171（91.9）	14（7.4）	1（0.5）
Etiology, n (%)	135（72.1）	33（17.6）	19（10.1）
Symptoms, n (%)	57（30.4）	101（54.0）	29（15.5）
Diagnosis, n (%)	74（39.5）	83（44.3）	30（16.0）
Treatment, n (%)	93（49.7）	80（42.7）	14（7.4）
Prevention, n (%)	121（64.7）	54（28.8）	12（6.4）

Comparison of features across platforms

Table 3 provides a detailed comparison of video features and quality between TikTok and Bilibili. The two platforms showed clear differences. On Bilibili, personal user-uploaded videos accounted for 34.4%, while on TikTok, this figure was only 5.2%. In terms of engagement, TikTok videos demonstrated significantly higher audience interaction than those on Bilibili. Specifically, the median number of likes, comments, collections, and shares on TikTok were 137 (62–331), 36 (10–79), 34 (11–115), and 31 (13–75), respectively, compared to Bilibili's median values of 12 (2–98), 2 (1–11), 15 (2–78), and 7 (1–32). Regarding video quality, TikTok videos had significantly higher GQS scores compared to Bilibili videos, although there were no statistical differences in the modified DISCERN scores between the two platforms (Figure 3).

Figure 3.

Comparison of video quality scores between TikTok and Bilibili platforms.

Table 3.

Comparison of characteristics between different short-video platforms.

Variables	TikTok (N = 97)	Bilibili (N = 90)	p-valve
Video source [n (%)]			<0.001
Specialists	66(68)	48(53.3)
Non-specialists	26(26.8)	11(12.2)
Individual user	5(5.2)	31(34.4)
Number of likes [median (IQR)]	137(62–331)	12(2–98)	<0.001
Number of comments [median (IQR)]	36(10–79)	2(1–11)	<0.001
Number of collections [median (IQR)]	34(11–115)	15(2–78)	0.001
Number of shares [median (IQR)]	31(13–75)	7(1–32)	<0.001
Video duration [s, median (IQR)]	53(37–73)	122(63–244)	<0.001
GQS scores [median (IQR)]	3(2–3)	2(1–3)	0.004
modified DISCERN scores [median (IQR)]	2(1–3)	2(1–3)	0.317

GQS: Global Quality Score; IQR: interquartile range.

Comparison of features across different video sources

We further compared the features and quality of videos based on their source. In terms of video popularity, no statistically significant differences were observed in the number of likes, comments, collections, or shares across expert, non-expert, and personal user-uploaded videos (Table 4). However, significant differences were found in video length and quality. Expert-uploaded videos performed notably better in quality, with GQS and modified DISCERN scores of 3 (2–4) and 2 (1–3), respectively. Expert-uploaded videos had significantly higher GQS scores compared to non-expert and personal user-uploaded videos, with no significant differences between non-expert and personal user videos. As for modified DISCERN scores, expert-uploaded videos scored significantly higher than non-expert videos, though no significant difference was observed between expert and personal user-uploaded videos (Figure 4).

Figure 4.

Comparison of video quality scores by video source.

Table 4.

Comparison of different video source.

	Specialists (n = 114)	Non-specialists (n = 37)	Individual user (n = 36)	p-valve
Video duration [s, median (IQR)]	64(45–113)	54(37–85)	190(64–393)	<0.001
Number of likes [median (IQR)]	69(14–202)	97(29–196)	48(4–817)	0.659
Number of collections [median (IQR)]	23(5–81)	21(8–74)	27(8–857)	0.334
Number of comments [median (IQR)]	10(1–56)	19(4–47)	10(0–81)	0.610
Number of shares [median (IQR)]	22(2–48)	22(9–51)	21(2–178)	0.793
GQS scores [median (IQR)]	3(2–4)	2(1–2)	2(1–3)	<0.001
modified DISCERN scores [median (IQR)]	2(1–3)	1(0–2)	2(1–2)	<0.001

GQS: Global Quality Score; IQR: interquartile range.

Correlation analysis between video features and quality

Figure 5 presents the correlation analysis between video features and video quality across both TikTok and Bilibili platforms. On both platforms, a significant correlation was found between GQS and the modified DISCERN scores (p < 0.001). On Bilibili, video duration was positively correlated with both GQS and modified DISCERN scores (p < 0.001), whereas no significant correlation was observed between engagement metrics (likes, comments, collections, shares) and quality scores on either platform.

Figure 5.

Correlation matrix of video engagement metrics and quality scores on TikTok and Bilibili.

Discussion

Our cross-sectional study indicated that expert-produced health videos achieved higher scores on the GQS and modified DISCERN scales, suggesting that the uploader's professional background significantly impacts content quality and credibility. This aligns with previous studies indicating that expert content is more scientific and standardized.^17–19 Compared to Bilibili videos, TikTok videos showed higher user engagement but similar scientific reliability and lower educational value. Spearman correlation analysis disclosed that high user interaction on TikTok didn't directly enhance video quality or scientific accuracy. In contrast, longer videos on Bilibili might offer more valuable health information, thus improving overall video quality.

Several concerns regarding the scientific accuracy and misleading nature of the content about POF on short-video platforms were also identified. For instance, some videos exaggerated the effectiveness of certain treatments, such as herbal medicine and massage, which lack scientific evidence, potentially leading viewers to abandon conventional medical interventions. Additionally, some videos provided overly simplistic lifestyle or dietary advice, neglecting individual differences, the multifactorial nature of diseases, and the importance of medical interventions. Furthermore, a number of videos discussed symptoms of POF, such as irregular menstruation, without providing a comprehensive diagnostic process, which might lead viewers to misinterpret general physiological changes as POF, inducing unnecessary anxiety and panic. These misleading contents not only affect the public's understanding of POF but may also lead to misguided health decisions and treatment delays. Therefore, ensuring the scientific accuracy and evidence-based nature of health information disseminated on short-video platforms is critical.^20,21

With the rise of social media, especially short-video platforms, the way the public accesses health information has undergone a significant transformation.^22,23 Platforms like TikTok and Bilibili have become major channels for the dissemination of health-related content, attracting a large user base.^24,25 Although the scientific literature on social media is expanding, research on female reproductive health remains limited. Available articles have presented results consistent with ours. Jenny Wu et al. analyzed TikTok videos related to endometriosis. They found that healthcare professionals accounted for 17% of content creators, with physicians making up 5%. The average health information quality score of the videos was 2.2 out of 5 (based on the DISCERN scale, with a median of 2 and a range of 1–4). Regarding the Patient Education Materials Assessment Tool (PEMAT), the average understandability was 75.3% and the average actionability was 10.7%. Despite high understandability scores on PEMAT, the low scores on DISCERN indicate that while these videos are accessible to viewers, they may not provide reliable educational content.²⁶ Ayhan Atigan et al. analyzed 198 YouTube videos related to polycystic ovary syndrome exercise. The average video power index was 97.82, GQS 3.89, and DISCERN score 33.62. They found that videos uploaded by professionals were of higher quality, yet overall video quality was inconsistent. Despite high viewership and interaction, many videos fell short in delivering reliable educational content.²⁷ Ayşe Gül Güven et al. assessed 109 YouTube videos on adolescent abnormal uterine bleeding for reliability, quality, and accuracy. They found that most videos were created by non-professionals, with half exhibiting poor quality per DISCERN and only 36.2% deemed good quality by Journal of the American Medical Association (JAMA) standards, while professional-uploaded videos were of better quality.²⁸

The main significance of this study lies in its provision of a comprehensive method for evaluating the quality of health information on short-video platforms. By comparing TikTok and Bilibili, we have revealed how platform characteristics, video sources, and interaction data influence the quality of health information dissemination. Furthermore, by integrating the GQS and modified DISCERN tools, this study offers a more systematic and objective evaluation of health information videos, providing valuable insights for public health policy-making, content regulation on short-video platforms, and health education initiatives.^19,29 The results suggest that significant differences exist in user interaction and video quality across platforms, and that the professional background of video uploaders plays a decisive role in determining content quality.³⁰ These findings provide empirical support for optimizing health information dissemination strategies.

While this study offers valuable empirical data on the quality of health information on short-video platforms, it does have some limitations. First, this study focused only on TikTok and Bilibili due to their dominance in the Chinese market and their unique short-video formats.^31,32 YouTube's minimal penetration in China limits its relevance to our target population. However, we acknowledge that future studies should include cross-platform comparisons with global platforms like YouTube. Second, as short-video content is constantly being updated, the data used in this study may not fully reflect the latest trends and developments. Third, video quality ratings are somewhat subjective, and although consistency tests were conducted to ensure inter-rater reliability, the application of the rating tools may still be influenced by the evaluators’ personal interpretations.^33,34 Additionally, this study primarily analyzed videos in Chinese, and future research could consider cross-linguistic and cross-cultural comparisons to explore how health information is disseminated in different cultural contexts. Lastly, the integration of AI-driven analysis tools could provide more objective and efficient solutions for video content analysis, a direction that future research may wish to explore.

Conclusion

This study assessed the quality of 187 videos related to ovarian insufficiency on TikTok and Bilibili using the GQS and modified DISCERN scoring systems. Overall, the videos on both platforms were found to be unsatisfactory in terms of quality and reliability. Notably, videos on TikTok were slightly better than those on Bilibili. Videos shared by specialists demonstrated higher quality and reliability, offering viewers more valuable information. Given the rise of short videos, it is crucial for healthcare professionals and institutions to ensure the availability of high-quality content related to ovarian insufficiency. Furthermore, short-video platforms should strengthen their monitoring and review mechanisms. Patients should approach videos on Bilibili and TikTok with caution when seeking medical information.

Supplemental Material

sj-xlsx-1-dhj-10.1177_20552076251351077 - Supplemental material for The quality and reliability of short videos about premature ovarian failure on Bilibili and TikTok: Cross-sectional study

Supplemental material, sj-xlsx-1-dhj-10.1177_20552076251351077 for The quality and reliability of short videos about premature ovarian failure on Bilibili and TikTok: Cross-sectional study by Ren Xu, Yanan Ren, Xinjun Li, Luyang Su and Jianzhi Su in DIGITAL HEALTH

Supplemental Material

sj-docx-2-dhj-10.1177_20552076251351077 - Supplemental material for The quality and reliability of short videos about premature ovarian failure on Bilibili and TikTok: Cross-sectional study

Supplemental material, sj-docx-2-dhj-10.1177_20552076251351077 for The quality and reliability of short videos about premature ovarian failure on Bilibili and TikTok: Cross-sectional study by Ren Xu, Yanan Ren, Xinjun Li, Luyang Su and Jianzhi Su in DIGITAL HEALTH

Footnotes

Acknowledgements

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

ORCID iD

Jianzhi Su

Ethical considerations

The data used in this study were sourced from publicly available video content published on platforms such as Bilibili and TikTok. These videos are publicly accessible, and no personal privacy information was involved during the data collection process. All analyzed content was publicly available, and the study did not involve the collection or processing of users’ private information. In accordance with relevant ethical review guidelines, ethical approval for this study was not required.

Author contributions statement

RX did conceptualization and writing the original draft. JS did conceptualization, formal analysis, and validation. YR performed data curation and formal analysis. LS data curation. XL was responsible for the software and visualization. All authors contributed to the article and approved the submitted version.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Department of Health of Hebei Province, (grant number Medical Science Research Project of Hebei (No 2024).

Declaration of conflicting interests

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

Supplemental material

Supplemental material for this article is available online.

References

Chen

Zhou

Zhang

, et al. Discovery of novel serum metabolic biomarkers in patients with polycystic ovarian syndrome and premature ovarian failure. Bioengineered 2021; 12: 8778–8792.

Chen

Guo

, et al. Electro-acupuncture regulates metabolic disorders of the liver and kidney in premature ovarian failure mice. Front Endocrinol (Lausanne) 2022; 13: 882214.

Wang

Xiang

. Mechanisms of ovarian aging in women: a review. J Ovarian Res 2023; 16: 67.

Zhang

, et al. Nrf2 signaling pathway mediates the protective effects of daphnetin against D-galactose induced-premature ovarian failure. Front Pharmacol 2022; 13: 810524.

Ghahremani-Nasab

Ghanbari

Jahanbani

, et al. Premature ovarian failure and tissue engineering. J Cell Physiol 2020; 235: 4217–4226.

Wang

Song

, et al. The reliability and quality of short videos as a source of dietary guidance for inflammatory bowel disease: cross-sectional study. J Med Internet Res 2023; 25: e41518.

Mohamed

Shoufan

. Users’ experience with health-related content on YouTube: an exploratory study. BMC Public Health 2024; 24: 86.

Liu

Peng

, et al. Assessment of the reliability and quality of breast cancer related videos on TikTok and Bilibili: cross-sectional study in China. Front Public Health 2023; 11: 1296386.

Sun

Zheng

. Quality of information in gallstone disease videos on TikTok: cross-sectional study. J Med Internet Res 2023; 25: e39162.

10.

Chen

Wang

. Social media use for health purposes: systematic review. J Med Internet Res 2021; 23: e17917.

11.

Memioglu

Ozyasar

. Analysis of YouTube videos as a source of information for myocarditis during the COVID-19 pandemic. Clin Res Cardiol 2022; 111: 1113–1120.

12.

D'Ambrosi

Hewett

. Meniscus-related videos on TikTok are widely viewed and shared but the educational quality for patients is poor. Arthrosc Sports Med Rehabil 2024; 6: 100927.

13.

D'Ambrosi

Hewett

. Validity of material related to the anterior cruciate ligament on TikTok. Orthop J Sports Med 2024; 12: 23259671241228543.

14.

D'Ambrosi

Bellato

Bullitta

, et al. Tiktok content as a source of health education regarding epicondylitis: a content analysis. J Orthop Traumatol 2024; 25: 14.

15.

Kanzow

Büttcher

Wiegand

, et al. Quality of information regarding repair restorations on dentist websites: systematic search and analysis. J Med Internet Res 2020; 22: e17250.

16.

Yin

Zhu

, et al. Evaluation of medical information on male sexual dysfunction on baidu encyclopedia and wikipedia: comparative study. J Med Internet Res 2022; 24: e37339.

17.

Shi

El Haddad

Cai

, et al. Mpox (monkeypox) information on TikTok: analysis of quality and audience engagement. BMJ Glob Health 2023; 8: e011138.

18.

Matthews

Abdulbaki

Ryan

, et al. Oropharyngeal cancer and the HPV vaccine: analysis of social media content. Laryngoscope 2025: 1–7.

19.

Song

Xue

Zhao

, et al. Short-video apps as a health information source for chronic obstructive pulmonary disease: information quality assessment of TikTok videos. J Med Internet Res 2021; 23: e28318.

20.

Aktas

Demirel

Celikkaleli

, et al. YouTube™ as a source of information on prostatitis: a quality and reliability analysis. Int J Impot Res 2024; 36: 242–247.

21.

Househ

Borycki

Kushniruk

. Empowering patients through social media: the benefits and challenges. Health Informatics J 2014; 20: 50–58.

22.

Liu

Zheng

, et al. Health communication through news Media during the early stage of the COVID-19 outbreak in China: digital topic modeling approach. J Med Internet Res 2020; 22: e19118.

23.

Yuan

Wang

. Characteristics, hotspots, and prospects of short video research: a review of papers published in China from 2012 to 2022. Heliyon 2024; 10: e24885.

24.

Liang

Wang

Song

, et al. Quality and audience engagement of Takotsubo syndrome-related videos on TikTok: content analysis. J Med Internet Res 2022; 24: e39360.

25.

Han

, et al. A study on satisfaction evaluation of Chinese mainstream short video platforms based on grounded theory and CRITIC-VIKOR. Heliyon 2024; 10: e30050.

26.

Greene

Bickett

, et al. Period pain is not normal: a content analysis of endometriosis-related videos on the social media platform TikTok. Bjog 2023; 130: 1555–1556.

27.

Atigan

. Polycystic ovary syndrome and exercise: evaluation of YouTube videos. Cureus 2023; 15: e35093.

28.

Güven

Kızılkan

Taşar

, et al. An assessment of the quality of YouTube videos as a resource for adolescents experiencing abnormal uterine bleeding. J Pediatr Adolesc Gynecol 2024; 37: 137–141.

29.

Alhlayl

Alzghaibi

. Evaluating the effectiveness and limitations of online health information tools in assessing the quality of medication-related content. Front Public Health 2025; 13: 1460202.

30.

Enver

Doruk

Kara

, et al. YouTube™ as an information source for larynx cancer: a systematic review of video content. Eur Arch Otorhinolaryngol 2020; 277: 2061–2069.

31.

Ferhatoglu

Kartal

Ekici

, et al. Evaluation of the reliability, utility, and quality of the information in sleeve gastrectomy videos shared on open access video sharing platform YouTube. Obes Surg 2019; 29: 1477–1484.

32.

Prestin

Vieux

Chou

. Is online health activity alive and well or flatlining? Findings from 10 years of the health information national trends survey. J Health Commun 2015; 20: 790–798.

33.

Mueller

Hongler

VNS

Jungo

, et al. Fiction, falsehoods, and few facts: cross-sectional study on the content-related quality of atopic eczema-related videos on YouTube. J Med Internet Res 2020; 22: e15599.

34.

Guan

Xia

Zhao

, et al. Videos in short-video sharing platforms as sources of information on colorectal polyps: cross-sectional content analysis study. J Med Internet Res 2024; 26: e51655.

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