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Youtube Health Videos: a trust based search approach

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Luis Fernandez-Luque, Randi Karlsen, and Genevieve B. Melton. 2011. HealthTrust: trust-based retrieval of you tube's diabetes channels. In Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11), Bettina Berendt, Arjen de Vries, Wenfei Fan, Craig Macdonald, Iadh Ounis, and Ian Ruthven (Eds.). ACM, New York, NY, USA, 1917-1920. DOI=http://dx.doi.org/10.1145/2063576.2063854

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Youtube Health Videos: a trust based search approach

  1. 1. A  large  number  of  stakeholders  are  publishing  diabetes  videos  Health  Informa8on  is  moving  to  social  media  pla:orms  [1]     (e.g.  blogs,    TwiCer,  YouTube):   Figh%ng  Irrelevant  Health  Videos  in  YouTube:             a  Social  Network  Analysis  Approach   Luis  Fernandez-­‐Luque  (luis.luque@norut.no),  Northern  Research  Ins8tute  –  Norut,  Tromsø,  Norway   Randi  Karlsen,  Computer  Science  Department,  University  of  Tromsø,  Norway   Genevieve  B  Melton,  University  of  Minnesota  (Ins8tute  for  Health  Informa8cs),  Minneapolis,  USA   Ignacio  Basagoi8,  ITACA-­‐TSB,  Technical  University  of  Valencia,  Spain   Introduc8on   There  are  300+  channels  from   US  hospitals  with  20,000+   videos  (most  of  which  are  on   YouTube).     The  informa8on  society  lives   in  a  constant  state  of   informa8on  overload.   Relevant  videos   Irrelevant  videos   Web  Informa8on  Retrieval  tools  (e.g.  Google,  Bing)  are  widely   used  by  health  informa8on  consumers.       Many  search  algorithms  are  based  on  Social  Network  Analysis   theory.       Hyperlinks  are  used  as  endorsement  indicators.  Hubs  and   PageRank  search  algorithms  are  examples  of  this.       Web  Informa8on  Retrieval   Mo%va%on:  popular  videos,  with  many  incoming  links,  may  not   be  relevant  for  health  informa8on  consumers  (e.g.,  jokes,   polemic  issues,  singers,  spam).           Our  Approach   Two  physicians  evaluated  the  top  20  channels  retrieved  using:   1)  our  approach;  and  2)  YouTube's  search  engine.     Aim:  to  evaluate  whether  or  not  they  would  recommend  each   channel  to  their  pa8ents.     The  agreement  of  the  recommenda8ons  from  both  reviewers   was  evaluated  using  Cohen  Kappa  (0.48-­‐moderate  agreement).   Using  YouTube  API;  we  extracted  217  channels  with  the   keyword  “Diabetes”.  5119  videos  and  525  links  were  extracted   (e.g.,  subscrip8ons,  favorites  and  friendships).       We  ranked  the  top  20  channel  authori8es  with  the  HITS   algorithm  [2]  using  JUNG  API  [3].             Methods   Our  list:  Channel  reviewers  recommended  12  out  of  19  (63%)     of  the  channels  from  our  list.    Only  2  out  of  19  (11%)  channels   from  our  list  were  not  recommended  by  either  physician.     YouTube’s  list:  it  had  10  out  of  19  (53%)  channels   recommended  by  both  reviewers.  7  out  of  19  (37%)  channels   were  not    recommended  by  either  clinician.       Note:  two  YouTube  users  removed  their  channels  during  the  evalua8on  process.     Results   By  analyzing  the  YouTube  Diabetes  Community  we  can  infer   knowledge  on  content  quality.  Specially  to  filter  the  less   recommended  channels.     This  provides  promise  for  new  techniques  based  on   Collabora8ve  Filtering  and  Collec8ve  Intelligence.   Our  future  research  will  look  into  a  new  search  engine  for   health  videos  .       Conclusions   Project  Informa8on             Our  approach:  extracted   informa8on  from  the  diabetes   community  in  YouTube  to  find   "high  quality"  channels.   hCp://commons.wikimedia.org/wiki/File:PageRank-­‐hi-­‐res.png   Favorite   References   [1]  S  Fox,  S  Jones.  The  Social  Life  of  Health  Informa8on.  Pew  Research  Center.  June  2009.    Archived   at:  hCp://www.webcita8on.org/5uSBNoUUr   [2]  Jon  M.  Kleinberg.  1999.  Authorita8ve  sources  in  a  hyperlinked  environment.  J.  ACM  46,  5,   September  1999,  604-­‐632.  DOI=10.1145/324133.324140   hCp://doi.acm.org/10.1145/324133.324140     [3]  HITS  Implementa8on  in  JUNG,  Archived  in  hCp://www.webcita8on.org/5uSAOBYNs   This  project  has  been  co-­‐funded  by  the   Tromsø  Telemedicine  Laboratory,  a   Centre  for  Research-­‐based  Innova8on   supported  by  the  Norwegian  Research   Council.  

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