healthtrust: a phd dissertation on the retrieval of trustworthy health social media
DESCRIPTION
This is the slides of my PhD defence about information retrieval of health social media.TRANSCRIPT
HealthTrust: A PhD Dissertation on the Retrieval of Trustworthy Health Social Media
Luis Fernandez Luque (@luisluque), eHealth Researcher, Norut Tromsø (Norway)
PhD Defence, 24th October 2014
“How can computing techniques support the retrieval of trustworthy
health social media?“
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
2
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
3
“Do not search: Twin-to-Twin transfusion syndrome”
4Part 1- A personal example
Searching
5Part 1- A personal example
Searching: a needle in a haystack
6Part 1- A personal example
Results
• Hospitals: out-dated focused on worse case scenarios
• Research literature: focused on complicated cases
• Social Media of Patients: obituaries • Social Media of Hospitals: to the point accurate
information
7Part 1- A personal example
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
8
Health Social Media: The Perfect Storm
9Part 2 - Introduction
10Part 2 - Introduction
Main open questions
• How to find the “good” content?• What is “good” content?• Why sometimes “Google” is failing?• Is it just content? Is it content-based
communities?• How is bad content disseminated or filtered? And
good content?
11Part 2 - Introduction
Research Gaps
• Lack of knowledge about health social media: motivations, dynamics, harmful content.
• Lack of information about technical solutions for finding health social media: new techniques were emerging for retrieving social media, but none specialized in the health context
• Lack of trust-based approaches for retrieving health social media: previous online information retrieval tools focused on metadata and not leverage in trust from online health communities.
12Part 2 - Introduction
Research Questions
How can computing techniques support the retrieval of trustworthy health social media?
•RQ1) What are the characteristics of health social videos?
•RQ2) Are there technical solutions for modelling health social media?
•RQ3) How can Social Network Analysis be used to extract information about the characteristics of health social media?
•RQ4) Can trust-based metrics improve the retrieval of social videos about diabetes?
13Part 2 - Introduction
Study Design
14Part 2 - Introduction
Multidisciplinary Research
15Part 2 - Introduction
Papers IRQ1.Paper 1: Gómez-Zúñiga B, Fernandez-Luque L, Pousada M, Hernández-Encuentra E, Armayones M. ePatients on YouTube: Analysis of Four Experiences From the Patients' Perspective. Med 2.0 2012;1(1):e1
RQ1.Paper 2: Fernandez-Luque L, Elahi N, Grajales FJ 3rd. An analysis of personal medical information disclosed in YouTube videos created by patients with multiple sclerosis. Stud Health Technol Inform. 2009;150:292-6.
RQ1.Paper 3: S Konstantinidis, L Fernandez-Luque, P Bamidis, R Karlsen. The Role of Taxonomies in Social Media and the Semantic Web for Health Education. Methods Inf Med 2013; 52
RQ1.Paper 4: E Gabarron, L Fernandez-Luque, M Armayones, A YS Lau. Identifying measures used for assessing quality of YouTube videos with patient health information: A Review of Current Literature. Interact J Med Res 2013;2(1):
RQ1.Paper 5: Syed-Abdul S, Fernandez-Luque L, Jian WS, Li YC, Crain S, Hsu MH, Wang YC, Khandregzen D, Chuluunbaatar E, Nguyen PA, Liou DM. Misleading health-related information promoted through video-based social media: anorexia on YouTube. J Med Internet Res. 2013 Feb 13;15(2):e30.
16Part 2 - Introduction
Papers IIRQ2.Paper 1: Fernandez-Luque L, Karlsen R, Bonander J. Review of extracting information from the Social Web for health personalization. J Med Internet Res. 2011 Jan 28;13(1):e15. doi: 10.2196/jmir.1432.
RQ3.Paper 1: Yom-Tov E, Fernandez-Luque L, Weber I, Crain SP. Pro-anorexia and pro-recovery photo sharing: a tale of two warring tribes. J Med Internet Res. 2012 Nov 7;14(6):e151. doi: 10.2196/jmir.2239.
RQ3.Paper 2: Chomutare T, Arsand E, Fernandez-Luque L, Lauritzen J, Hartvigsen G. Inferring community structure in healthcare forums. An empirical study. Methods Inf Med. 2013;52(2):160-7. Epub 2013 Feb 8.
RQ4.Paper 1: Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: Trust-based Retrieval of YouTube's Diabetes Channels, 2011, 20th ACM international conference on Information and knowledge management.
RQ4.Paper 2: Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res. 2012 Jan 31;14(1):e22.
17Part 2 - Introduction
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
18
RQ1: What are the characteristics of health social videos?
• RQ1.1: Does the online community influence the motivation of people with chronic conditions to publish videos about their health?
• RQ1.2: Do health videos contain relevant medical vocabulary in their textual metadata?
• RQ1.3: What are the quality features of online health videos?
• RQ1.4: Do misleading and informative online videos on the topic of anorexia have different characteristics?
19Part 3 - RQ1 Health Videos
RQ1.Study 1: Characteristics of metadata in health social videos
S Konstantinidis, L Fernandez-Luque, P Bamidis, R Karlsen. The Role of Taxonomies in Social Media and the Semantic Web for Health Education. Methods Inf Med 2013; 52
Fernandez-Luque L, Elahi N, Grajales FJ 3rd. An analysis of personal medical information disclosed in YouTube videos created by patients with multiple sclerosis. Stud Health Technol Inform. 2009;150:292-6.
20Part 3 - RQ1 Health Videos
RQ1.Study 2: What is quality of health social videos?
E Gabarron, L Fernandez-Luque, M Armayones, A YS Lau. Identifying measures used for assessing quality of YouTube videos with patient health information: A Review of Current Literature. Interact J Med Res 2013;2(1):e6
RQ1.Study 3: Motivations of patients sharing videos
Gómez-Zúñiga B, Fernandez-Luque L, Pousada M, Hernández-Encuentra E, Armayones M. ePatients on YouTube: Analysis of Four Experiences From the Patients' Perspective. Med 2.0 2012;1(1):e1
...And part of why I started my blog in the first place was because, even though I’ve lived with diabetes for such a long time and I didn’t known (sic) anyone else who had it, and I literally felt like the only diabetic on the planet. [KS]
I met so many people from all over the world that I would never have been able to talk to, before the Internet of course, and then now, with the MS community on YouTube it’s incredible. [VB]
RQ1.Study 4: Study about pro- and anti- anorexia videos
Syed-Abdul S, Fernandez-Luque L, Jian WS, Li YC, et al. Misleading health-related information promoted through video-based social media: anorexia on YouTube. J Med Internet Res. 2013 Feb 13;15(2):e30.
RQ1.Study 4: Study about pro- and anti- anorexia videos
Syed-Abdul S, Fernandez-Luque L, Jian WS, Li YC, et al. Misleading health-related information promoted through video-based social media: anorexia on YouTube. J Med Internet Res. 2013 Feb 13;15(2):e30.
RQ1: Characterizing Health Social Media
Key Findings
• Social interaction is one of the main driving forces behind those publishing videos about their health.
• Textual metadata can be of very heterogeneous quality, but still contains a lot of relevant health information for modeling.
• The quality of health videos is a multidimensional concept. Reliability of the content and its provider are very important quality criteria according the literature.
25Part 3 - RQ1 Health Videos
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
26
Modeling Health Social Media
RQ2: Are there technical solutions for modeling health social media?
27Part 4- RQ2 Modeling Health Social Media
RQ2.Study 1: Review on techniques for modeling health social media
Fernandez-Luque L, Karlsen R, Bonander J. Review of extracting information from the Social Web for health personalization. J Med Internet Res. 2011 Jan 28;13(1):e15. doi: 10.2196/jmir.1432.
RQ2: Extracting Information from Health Social Media
• Most technical solutions for modeling social media will have shortcomings in the health domain due to text analysis complexities.
• Questions about privacy issues.• Link and Social Network Analysis is promising but
has not been studied in detail in the health domain.
Key Findings
Fernandez-Luque L, Karlsen R, Bonander J. Review of extracting information from the Social Web for health personalization. J Med Internet Res. 2011 Jan 28;13(1):e15. doi: 10.2196/jmir.1432.
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
30
RQ3.Study 1: Structure of Pro-anorexia & pro-recovery groups in Flickr
Yom-Tov E, Fernandez-Luque L, Weber I, Crain SP Pro-Anorexia and Pro-Recovery Photo Sharing: A Tale of Two Warring Tribes J Med Internet Res 2012;14(6):e151
RQ3.Study 1: Structure of Pro-anorexia & pro-recovery groups in Flickr
Yom-Tov E, Fernandez-Luque L, Weber I, Crain SP Pro-Anorexia and Pro-Recovery Photo Sharing: A Tale of Two Warring Tribes J Med Internet Res 2012;14(6):e151
Figure 4. Network graphs according to four connection types (from top left, clockwise): Contacts, Favorites, Tags, Comments.
RQ3.Study 2: Structure of diabetes communities
Chomutare T, Arsand E, Fernandez-Luque L, Lauritzen J, Hartvigsen G. Inferring community structure in healthcare forums. An empirical study. Methods Inf Med. 2013;52(2):160-7. Epub 2013 Feb 8 .
RQ3: Social Network Analysis for characterizing Health Social Media.
• On a photo-sharing site, the best predictors of users belonging to the sub-community promoting anorexia are social network metrics. Tag-based classification was less accurate.
• Most centric members on online diabetes communities had longer experience living with the disease.
Key Findings
34Part 5 - SNA Health Communities
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
35
HealthTrust - a trust-based metric for retrieving diabetes videos
Online search of health diabetes videos
36
Online Search: PageRank & TKC effect (Tightly Knit Community)
37
http://en.wikipedia.org/wiki/PageRank
R. Lempel and S. Moran. 2000. The stochastic approach for link-structure analysis (SALSA) and the TKC effect. Comput. Netw. 33, 1-6 (June 2000), 387-401. DOI=10.1016/S1389-1286(00)00034-7 http://dx.doi.org/10.1016/S1389-1286(00)00034-7
Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 1-7 (April 1998), 107-117. DOI=10.1016/S0169-7552(98)00110-X http://dx.doi.org/10.1016/S0169-
7552(98)00110-X
RQ4: HealthTrust - a trust-based metric for retrieving diabetes videos
Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res. 2012 Jan 31;14(1):e22.
RQ4: HealthTrust - a trust-based metric for retrieving diabetes videos
Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res. 2012 Jan 31;14(1):e22.
Yo
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be’s A
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Videos, Tags, Users
RQ4: HealthTrust - a trust-based metric for retrieving diabetes videos
Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res. 2012 Jan 31;14(1):e22.
RQ4: HealthTrust - a trust-based metric for retrieving diabetes videos
Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res. 2012 Jan 31;14(1):e22.
RQ4: HealthTrust - a trust-based metric for retrieving diabetes videos
• In diabetes online communities the most reputable members are those with more experience with diabetes.
• The HealthTrust metric based on Social Network Analysis to infer quality of health videos performs well for filtering misleading content compared to YouTube searches.
Key Findings
Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res. 2012 Jan 31;14(1):e22.
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
43
Claimed contributions• C1: increase in the knowledge about health
social videos.– Published results have been cited more 400 times since 2009.
– Startups and journalists have requested interviews to share my knowledge. Also keynotes in Taiwan and Norway.
• C2: increased knowledge on the challenges related to model health social media– The RQ2.P1 is the first paper that systematically reviews the
challenges of modeling health social media. It has been cited 25 times since 2011.
44Part 7 - Discussion
Claimed contributions• C3: social network analysis of online health
communities– Research in this PhD has increased the understanding
of the social dynamics in health related communities (e.g. anorexia, diabetes).
• C4: social network analysis of health social media to infer quality– The algorithm HealthTrust is the first one focused on the use of
social network analysis to retrieve trustworthy health videos for patients.
– The algorithm has been designed, tested and evaluated.
45Part 7 - Discussion
Discussion & Limitations
• Social network features of health communities can provide clues regarding quality and trustworthiness of content.
• Each platform and disease is different. Evaluation was online done in Diabetes in a offline experiment. Can we generalize HealthTrust?
• Social media is becoming more heterogeneous (Twitter, YouTube, etc.), but HealthTrust has been tested only with one type of content (i.e. videos).
46Part 7 - Discussion
Agenda
3 4Why? A personal
example
1 2Modelling Health
Social MediaHealth Social
Media & Online Videos
7 8Social Network
Analysis of Health Communities
5HealthTrust and
Information Retrieval
6Future workDiscussion
Introduction & Overview
47
Future Work
• Creation of a portal (spin-off) to access many users for better experimentation and evaluation.
• To expand our knowledge about why misleading and harmful content is highly visible and ranked.– Better strategies for disseminating good health social
media.– Better information retrieval tools to help finding content.
• Study case: the visibility of the online anti-vaccination movement might be already killing children.
48Part 8 – Future work
Questions ?
Luis Fernandez Luque ([email protected])+34 656 93 09 01
49
http://www.slideshare.net/luis.luque