HealthTrust: A PhD Dissertation on the Retrieval of Trustworthy Health Social Media

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



7 8Social Network


5HealthTrust and




3

“Do not search: Twin-to-Twin transfusion syndrome”

4Part 1- A personal example

Searching


Searching: a needle in a haystack


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


Agenda

3 4Why? A personal

example

1 2Modelling Health



7 8Social Network


5HealthTrust and




8

Health Social Media: The Perfect Storm

9Part 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?


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.


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?


Study Design


Multidisciplinary Research


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.


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.


Agenda

3 4Why? A personal

example

1 2Modelling Health



7 8Social Network


5HealthTrust and




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.


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.


Agenda

3 4Why? A personal

example

1 2Modelling Health



7 8Social Network


5HealthTrust and




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



7 8Social Network


5HealthTrust and




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



7 8Social Network


5HealthTrust and




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.



Yo

uTu

be’s A

PI

Videos, Tags, Users






• 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


Agenda

3 4Why? A personal

example

1 2Modelling Health



7 8Social Network


5HealthTrust and




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.


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).


Agenda

3 4Why? A personal

example

1 2Modelling Health



7 8Social Network


5HealthTrust and




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

Health & Medicine

HealthTrust: A PhD Dissertation on the Retrieval of Trustworthy Health Social Media