This document discusses analyzing YouTube videos about diabetes as reliable health sources. It finds that while YouTube contains useful health information, some videos may contain misleading information. The document then explores using social features like likes, dislikes, comments and author reputation to identify reliable health videos. It searches for diabetes videos from organizations on a healthcare social media list and finds few in top results. A new approach is proposed that ranks videos from hospitals, organizations and active users higher to improve reliability of search results. Future work would analyze interactions and metadata of these videos and users.

1. ANALYSIS OF HOSPITAL YOUTUBE
VIDEOS AS HEALTH RELIABLE
SOURCES
CASE OF STUDY: DIABETES

2. YOUTUBE
• The most important video-sharing website
• 800 million unique visitors per month
• 72 hours of video uploaded each minute
• Social Networking tools
• Users can interact:
• Upload, watch and share videos
• Rate and comment videos
• Create channels

3. YOUTUBE & HEALTH
• Advantages
• Substantial amount of useful information
• Used as a source of health care information
• Social networking tools
• Disadvantages
• Risk of dissemination of misleading information
• Not up to date information

4. YOUTUBE & HEALTH
• Disadvantages
• Diabetes search
• Previous analysis

5. HEALTH TRUST PROJECT
• Tromsø Telemedicine Laboratory & Computer
Science Department, University of Tromsø.
• Objective: Retrieve good-content online health
videos
• How does HealthTrust work?
• A metric, called HealthTrust, that estimates the trustworthiness of
social media content.
• Calculates reputation in an online health community
• Based on link analysis

6. YOUTUBE & HEALTH
• Objective:
• Extract reliable health videos from YouTube
• Idea:
• If we can rely on the source (author)  we can rely on the
video
• Can we identify reliable videos using their social networking
features?
• Searching for:
• Interactions between users and videos.
• Analysis of the videos rates likes, dislikes and views
• Number of comments
• Analysis of health videos from reliable sources

7. THE HEALTH CARE SOCIAL MEDIA LIST
• The Health Care Social Media List.
• A compilation of health-related organizations actively using
social networking sites and maintaining officially sponsored
accounts.
• Mayo Clinic Center for Social Media
• 1475 Health care organizations (September 2012)
• YouTube
• 631 YouTube channels
• > 50000 videos

8. THE HEALTH CARE SOCIAL MEDIA LIST
• Case of study: Diabetes
• Search words: “diabetes” or “diabetic” in the video title or the
video description
• Results
• 1295 videos related to diabetes
• 823 unavailable interaction
• 475 available
• 196 videos 1 or more comments
• 456 videos 1 or more likes
• 68 videos 1 or more dislikes
• Conclusion: Low interaction between users and videos

9. YOUTUBE & HEALTH
• What we have?
• A list of hospitals
• Insufficient metadata from these hospital’s videos
• What we want
• Extract reliable videos from YouTube
• Change focus
• Assumption:
• Health knowledge comes from health care institutions: hospitals,
health centers, etc. Search these videos.

10. YOUTUBE & SOCIAL MEDIA CARE LIST
• How can we do it?
• Search YouTube videos related to diabetes
• Good videos  Authors are in the Social Media Care list.
• YouTube many results
• Top-100 results
• Methodology
• 20 queries adopted from HealthTrust project related to
diabetes
• For each query
1.
2.
Extract Top-100 YouTube results (which includes all the videos
metadata)
Search the videos whose authors are in Social Media Care list

11. YOUTUBE & SOCIAL MEDIA CARE LIST
Results
• How many videos, whose authors are in the Social Media
Care list, are in the Top-20?
• 4 videos  Can be an explanation about why the metadata
from Social Media Care list is so poor.
• How many of them are in the Top-100?
• 26 videos.
• Minimum changes if we make a new ranking
4; 1%
26; 6%
Hospitals
396;
99%
rest
Hospitals
374;
94%
rest

12. YOUTUBE & SOCIAL MEDIA CARE LIST
• How can we improve the results?
• What do we have now?
• Hospitals from a Social Media Care list
• 2000 diabetes videos and metadata
• Assumption: Health knowledge comes from health
institutions
• Why don’t we try to extract videos from active
users, and organizations, related to the disease?

13. YOUTUBE & HOSPITALS & ORGANIZATIONS&
ACTIVE USERS
New ranking
1. First of all must be the hospitals and health institutions
videos as the source of health knowledge
2. The second approach contains associations &
Organizations
3. Active users
• Filters:
• Syntactic analysis
• Metadata analysis
• Relation between the user videos related to the disease and the
other videos uploaded by the user
• Are any of they favourite video authors in the Social Media Care list
or in in the organizations list?

14. FUTURE WORK
Analyse Results from new ranking
• How many of the “new ranking” videos are in the Top-20?
• 43 videos
• How many of the “new ranking” videos are in the Top-100?
• 143 videos
Top -20 Ranking results
43; 11%
Videos
classified as
Reliable
357;
89%
rest
143;
36%
257;
64%
Videos
classified as
Reliable
rest

15. FUTURE WORK
• We have 3 list of users
• Hospital Users
• Organizations users
• Reliable users
• Data mining on the 3 list of users and their videos
Metadata (such as Social Media Care List)
• What about the new results?
• Searching for
• Relevant interaction between users and videos.
• Analysis these videos likes and dislikes.
• Number of comments of these videos

16. FINAL
• Thanks for your attention.
• Questions
• José Enrique Borrás Morell
• jobormo@itaca.upv.es