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Social Information Access: A Personal Update

The document discusses collaborative exploratory search in social information access, emphasizing the importance of both explicit and implicit collaboration among users in academic social networks. It analyzes search processes, model characteristics, and communication patterns during search tasks, highlighting the benefits and challenges of collaborative searching. Key findings include improved query vocabulary richness in collaborative settings, but lower recall rates and challenges related to communication costs.

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     Social  Informa-on  Access   -­‐-­‐  a  Personal  Update 2014/6/18
Agenda   2 Collaborative Exploratory Search Privacy Concerns in Social-based People Search Some Reflections Conclusions Social Information Access Scholars in Academic Social Networks
Informa-on  Access   —  Information  Access:  an  interactive  process  starts  with  a   user  noticing  his/her  needs  and  ends  with  the  user   obtaining  the  necessary  information   —  Iterative,  multiple  stages,  many  back  loops  
User  Generated  Content   Social  Networks  
Social  Informa-on  Access   —  Social  Information  Access:  information   access  using  “community  wisdom”     —  distilled  from  the  actions  in  real/virtual   community     —  Collaboration  in  explicit  or  implicit  manner   —  Social  information  access  technologies   capitalize  on  the  natural  tendency  of   people  to  follow  direct  and  indirect  cues   of  others’  activities   —  going  to  a  restaurant  that  seems  to  attract  many   customers,  or   —  asking  others  what  movies  to  watch.  
Space  of  Social  Informa-on  Access   —  [Brusilovsky2012]’s    taxonomy  for  social  info  access   6
More  Social  Informa-on  Access   —  Collaboration  can  be  explicit,  not  just  implicit   —  Explicit  Collaboration:  users  work  as  a  team  to  complete  the  same   task   —  Issues:  How  to  model  collaboration?   7 Implicit Collaboration Explicit Collaboration
More  Social  Informa-on  Access   —  Target  can  be  people,  and  people’s  social  connections     are  important   —  Relationship  is  as  important  as  the  documents  generated  by  the  people   —  Issues:  What  are  the  impacts  of  privacy  concerns?   8
More  Social  Informa-on  Access   —  User  generated  content  can  be  generic,  or  academic   —  E-­‐Science  and  CyberScholarship  are  increasingly  popular   —  Issues:  What  are  scientists  doing  online?   9 Popular Social Networks Academic Online Social Networks
Explicit   Collaboration:   Collaborative   Exploratory  Search Collaborate with Zhen Yue, Shuguang Han
Collabora-ve  Exploratory  Search   11 —  Daily  collaborative  Web   search  increased   dramatically   —  0.9%  in  2006  -­‐>  11%  in  2012   (Morris,  2013)   Complex  interactions  -­‐>   difficult  to  study   collaborative  search   processes  
—  Many  studies  on  individual  search  process     —  Well-­‐established  models  such  as  Kuhlthau’s  model  (Kuhlthau,  1991)   and  Marchionini’s  model  (Marchionini,  1995)   —  Many  studies  on  analyzing  the  transition  patterns  of  actions  (Chen   &  Cooper,  2002),  search  tactics  (Xie  &  Joo,  2010)  or  search  strategies   (Belkin,  1995)  in  the  process     —  A  few  studies  on  collaborative  search  process   —  Several  studies  look  into  the  application  of  individual  search   models  in  the  collaborative  environment  (Hyldegard,  2006;  Shah  &   González-­‐ibáñez,  2010).   —  An  investigation  on  before,  during  and  after  search  stages  in  social   search  (Evans  &  Chi,  2008).   Status  on  Search  Processes   12
Research  Ques-ons   —  RQ1.  How  to  model  the  search  states  in  the   collaborative  exploratory  search  process?    What  are  the   characteristics  of  search  states  in  the  collaborative   exploratory  search  process?   —  Search  states:  basic  units  of  a  search  process   —  RQ2.  What  are  the  characteristics  of  query  behaviors  in   the  collaborative  exploratory  search  process?   —  RQ3.  What  are  the  characteristics  of  communications   between  team  members  in  the  collaborative   exploratory  search  process?   13 Holistic view of collaborative search processes Two key components of the collaborative search process
CollabSearch:  a  Collaborative  Search  System     http://crystal.exp.sis.pitt.edu:8080/CollaborativeSearch/ q  Search functions - Web Search - Save/edit/rate/tag Web pages/snippets - Space for search task description q  Collaboration functions - Chat - Share search queries - Share saved Web pages/snippets System:  CollabSearch   14
Experiment  Design   —  Two  Experiment  Conditions   —  COL:  Collaborative  search     –  Two  participants  worked  as  a  team  on  the  same  search  task  simultaneously.     —  IND:  Individual  search     –  One  participant  worked  on  the  search  task  individually.     —  Participants     —  36  participants  (18  pairs)  in  COL  condition   —  18  participants  in  the  IND  condition   —  Two  Exploratory  Search  Tasks  (30mins/task)   —  Information-­‐gathering  task     –  collecting  information  for  writing  a  report  on  the  impact  of  social  network   (Shah  &  Marchionini,  2010).     —  Decision-­‐making  task     –  collecting  information  for  planning  a  trip  to  Finland  (Paul  &  Morris,  2010).     —  The  orders  of  the  two  tasks  were  rotated   15
HMM  Model  of  Search  Process   16
Approaches  for  Study  Search  Processes   —  Analyze  qualitative  constructs  in  the  search  process     —  Kuhlthau,  1991;  Ellis,  1993;  Marchionini,  1995   —  Search  pattern  analysis  based  on  logged  behavior   —  Directly  use  logged  actions  (Holscher  &  Strube,  2000)   —  Ignore  user  intentions  such  as  search  tactics   —  Manually  code  search  tactics/strategies  on  log  data  (Xie&Joo,2010)   —  Time-­‐consuming  and  need  a  theoretical  model  to  generate  codes   —  Our  approach:  Hidden  Markov  Model   —  Model  search  tactics  as  hidden  states   —  An  automatic  approach  for  analyzing  time  sequential  events   17
—  A  two-­‐level  view  of  search  process   —  Higher  level:  search  states  as  hidden  search  tactics  or  strategies   —  Lower  level:  observable  actions   —  Parameters  in  HMM   —  Number  of  hidden  states  N  (N  ≠  M)   —  Transition  probabilities  among  any  two  hidden  states   —  Emission  probability  from  each  state  to  each  action   A Hidden Markov Model for Search States Observable actions Hidden search tactics or strategies Model  Search  States  using  HMM   18 Transition     probabilitie s   Emission     probability     !1   !2   !3   !%   &1   &2   &3   &%  
Categorizing  Observable  Ac-ons   Method Search Scan Select Capture Communicate Object Query Topic statement Item in search result Chat messages List of saved items Single saved item Source Self Partner Shared/mix Inspired by Belkin’s ISSs model (Belkin, 1995) but with modifications to accommodate the context in collaborative web search. 19
Actions Descriptions Search  –  query  –  self  (Q) A  user  issues  a  query Select-­‐  item-­‐self  (V) A  user  clicks  on  a  result  in  the  returned  result  list Capture-­‐item-­‐self  (S) A  user  saves  a  snippet  or  bookmarks  a  webpage Scan-­‐list  of  saved  item  –  mixed  (Wm) A   user   checks   the   workspace   without   clicking   on   any  particular  item. Select  –  single  saved  item  –self  (Ws) A  user  clicks  on  an  item  in  the  workspace  saved  by   him/herself Select  –  single  saved  item  –  partner  (Wp) A  user  clicks  on  an  item  in  the  workspace  saved  by   the  partner Scan-­‐topic  -­‐shared  (T) A  user  clicks  on  the  topic  statement  for  view   Communicate-­‐  messages-­‐self  (Cs) A  user  sends  a  message  to  the  other  user   Communicate-­‐message-­‐partner  (Cp) A  user  receives  a  message  from  the  other  user 20 Ac-ons  Observed  in  this  Study  
Parameters  Selec-on  and  Es-ma-on     —  Determine  number  of  hidden  states  N   —  Bayesian  Information  Criterion  (BIC)   —  Parameter  estimations   —  Baum-­‐Welch  algorithm:  maximize  data  likelihood   —  Random  assign  a  start  probability  π  for  each  state,     —  Estimate  the  transition  probabilities  and  emission  probability  through  a   machine  learning  process     21 6000 6500 7000 7500 8000 2 3 4 5 6 7 26000   27500   29000   30500   32000   4   5   6   7   8   9   10   IND: N=4 COL: N=6 BIC=-2×logL+log(S) ×NP log-likelihood (logL) Number of parameters (NP) sample size(S)
HMM  Output  for  Individual  Search   Query View Save Workspace self Topic HQ 0.99 HV 0.91 HS 0.96 HD 0.57 0.42 Hidden States and Emission Probabilities in IND (values<0.05 are omitted) 22 Search-related hidden states: HQ, HV, HS HQ: hidden states of querying HV: hidden states of viewing a search result HS: hidden states of saving a search result Sensemaking-related hidden states: HD HD: hidden states of defining current search problem
Compare  to  Exis-ng  Search  Models   0.39 0.12 HQ 0.33 HV HS 0.50 HD 0.07 0.85 0.59 0.49 0.26 0.31 0.12 HQ 0.32 HV HS 0.56 HD 0.13* 0.86 0.38 0.53 0.34 0.39 0.23 Sub-processes in the ISP model HMM Define Problem HD Select Source, Formulate Query, Execute Query HQ Examine Results HV Extract Information HS Reflect/Iterate/Stop HD Mapping from sub-process in Marchionini’s ISP model to the hidden states transition probabilities of hidden states in IND The default transition in Marchinoinini’s model can be mapped to into HDàHQàHVàHSàHD, which is also the pattern of the highest probability in HMM results. 23
HMM  Outputs  in  Collabora-ve  Search   Query View Save Workspace mix Workspace self Workspace partner Topic Chat send Chat receive HQ 0.82 0.13 HV 0.87 0.1 HS 0.88 HD 0.36 0.36 0.21 HW 0.37 0.44 0.12 HC 0.44 0.47 Hidden States and Emission Probability in COL Search-related hidden states: HQ, HV, HS Sensemaking-related hidden states: HD, HW, HC HD: hidden states of defining current search problem HW: hidden states of implicit communication HC: hidden states of explicit communication (Paul & Morris, 2010): chat-centric sensemaking (HC) and workspace- centric sensemaking (HW) (Evans & Chi, 2008): search and sensemaking are tightly coupled 24
Detec-ng  Task  Differences  using  HMM   0.11 HQ 0.26 HV HS 0.45 HD 0.06 0.86 0.56 0.31 0.08 0.46 0.30 HC HW 0.56 0.09 0.14 0.16 0.16 0.14 0.15 HQ 0.22 HV HS 0.33** HD 0.12 0.84 0.28** 0.35** 0.20** 0.42 0.24 0.13 HC 0.89 HW 0.48 0.06 0.30** 0.07** 0.33* 0.13** Comparison of Transition Probabilities of Hidden states in COL for the two tasks (red arrows indicate significant difference: *p<0.05, **p<0.01) Cross-category transitions: From search to sensemaking From sensemaking to search Cross-category transitions is more common in collaborative search than in individual search Cross-category transition is more common in decision-making task than in information gathering task. 25 Information-gathering Decision-making
Query  Behaviors  and  Communica-ons   26
27 Search condition (individual or collaborative) Task type (information-gathering or decision-making) Query vocabulary features (number of queries, query vocabulary richness, query diversity) Query reformulation patterns (New, Generalization, Specification, Reconstruction) Query performance (Precision, recall, Successful query rate, user satisfaction, cognitive load) Research  Design   Independent Variables Dependent Variables Communication Timing Communication Content
28 •  Query  Vocabulary  Richness  (QVR)   •  Query  Diversity  (QD)     •  Levenshtein  distance  (Shah  &  González-­‐Ibáñez,  2011)       •  calculate  the  difference  between  a  pair  of  queries.   •  Query  Result  Similarity  (QRS)     Query  Vocabulary  Features   (Kromer, Snasel, & Platos, 2008)
Query  Reformula-on  PaPerns   29 Type Definition New (N) Qi is the first query or does not share any common terms with Qi-1 Generalization (Ge) Qi shares common terms with Qi-1 ; and Qi contains fewer terms than Qi-1 Specialization (Sp) Qi shares common terms with Qi-1 ; and Qi contains more terms than Qi-1 Reconstruction (Rc) Qi shares common terms with Qi-1 ; and Qi has the same length as Qi-1 •  Qi-1 and Qi are two consecutive queries in the same search session •  The patterns are defined based on (He et al. 2002; Jansen et al. 2009)
Query  performance   30 Objective   Measurements   Precision  &   Recall   Successful   Query  Rate   Subjective   Measurements   User   Satisfaction   Cognitive  Load  
Communica-on  Timing  Analysis   —  Before  search:  communications  before  the  first  search   action   —  During  search:  communications  between  the  first  and  last   search  action   —  After  search:  communications  after  the  last  search  action   —  (Search  actions:  issuing  a  query,  viewing  or  saving  a  search  result)   31  Chat    Chat    Chat    Chat   Before   Search During   Search During   Search A<er   Search The first search action The last search action Search action Link to rational
Collabora-on  Benefit  I:  Rich   Vocabularies  and  Diverse  Queries   32 T2Task IND COL 0 0.5 1 1.5 2 2.5 3 T1 T2 QVR Task IND COL Query vocabulary richness Query diversity 0 5 10 15 20 25 30 T1 T2 QD Task IND COL .00 .02 .04 .06 .08 .10 T1 T2 QRS Task IND COL Chat  time   QVR   Total     ↑(p=0.045)   Before  search   -­‐   During  search   -­‐   After  search   -­‐   ü Participants in the collaborative search were able to employ wider range of vocabularies for the queries and the queries were more diverse. ü A positive correlation was found between the total chat time and the query vocabulary richness. *Differences showed in the graph are significant
Collabora-on  Cost  I:  Low  Recall  and   Low  Successful  Query  Rate   33 Recall Successful Query Rate Chat  time   Recall   Total     ↓(p=0.001)   Before  search   ↓(p=0.022)   During  search   -­‐   After  search   ↓(p<0.001)   ü Collaboration takes times and efforts. Participants had less time to devote to search, and they were more stringent on the what documents to save. ü A negative correlation was found between the communication and recall.
Collabora-on  Benefit  II:  People  More   Sa-sfied  and  less  stressed   34 User Satisfaction Cognitive Load *Differences showed in the graph are significant Chat  time   Satisfaction   CogLoad   Task  social   ↑(p=0.017)   ↓(p=0.022)   Task   coordination   -­‐   -­‐   Task  content     -­‐   ↑(p=0.008)   Non-­‐task   -­‐   -­‐   ü Participants in collaborative search are more satisfied with the performance and have lower cognitive load. ü A positive correlation was found between the task social communication and satisfaction (negative for Cogload).
Collabora-on  Affects  the  PaPerns  of   Query  Reformula-on   35 0.1 0.2 0.3 0.4 0.5 IND COL New Generalization Specification Reconstruction 0.1 0.2 0.3 0.4 0.5 Task  1 Task  2 New Generalization Specification Reconstruction ü Higher percentages of New and Specialization and lower percentage of Reconstruction in the collaborative search. ü Participants in collaborative search were able to explore the divided subtopics in depth while the participant in the individual search owns the entire search topic and the scope maybe the first priority.
Communica-on  PaPerns   36 Proportion of each communication content type within stage (Left: T1; Right: T2) ü Communication is common in any of three stages. ü The communication content varies in the three stages. The before search stage communication is more focused on the task coordination. The during search stage communication is more focused on the task content. Task social communication is more common in the before search and after search stage than in the during search stage.
Implica-ons   37
What  We  Learned   —  Collaborative  search  process  have  patterns   —  More  collaboration-­‐oriented  actions  as  the  collaboration  level   increase   —  Transitions  within  search-­‐oriented  actions  and  within   collaboration-­‐oriented  actions  are  more  frequent  than   between  them  in  all  three  conditions.     —  Explicit    and  implicit  communication  has  potential   benefit  on  helping  using  generating  query  ideas.   38
Implica-ons  for  Collabora-ve  Search   Research   —  Search  activities  and  sensemaking  activities  are  tightly   coupled  in  the  collaborative  search.   —  The  studies  of  collaborative  search  should  not  just   concentrate  on  the  effectiveness  of  search,  but  also  on   the  users’  perception  of  their  search  experiences,   particularly  their  satisfaction  and  cognitive  load.   —  The  wider  range  of  query  vocabulary  in  collaborative   search  did  not  necessarily  lead  to  a  more  effective   search  outcomes.   39
Implica-ons  for  Collabora-ve   Search  System  Design   —  It’s  important  to  design  interface-­‐mediated  support  for   the  coordination  among  team  members  as  the   coordination  through  communication  is  costly.   —  Provide  targeted  algorithm-­‐mediated  query   suggestions  based  on  the  findings  of  how  users   reformulate  queries  in  the  collaborative  search.     —  Designers  need  to  make  a  balance  between  the  support   for  fulfilling  the  search  task  and  the  support  for  social   interactions  among  team  members.   40
Implica-ons  for  Other  Researchers   —  Researchers  in  CSCW  and  CSCL  can  draw  insights  on   —  Sense-­‐making  intertwined  with  the  activities  that  directly  aim  for   fulfilling  the  task  requirements.     —  Consider  the  social  gain  and  emotional  support  when  evaluating  the   team  effectiveness.     —  The  findings  on  the  differences  between  information-­‐gathering  and   decision-­‐making  collaboration  tasks.     —  Researchers,  try  HMM  if  you   —  are  analyzing  complex  interactive  process  in  a  time  sequence   —  don’t  have  a  theoretical  model  to  start  with   —  want  to  see  the  patterns  of  your  data  before  applying  time-­‐consuming   qualitative  annotation   —  are  interested  in  the  hidden  strategies  or  tactics  underneath  the   observable  actions  of  users   41
Privacy  Concerns  in   Social  Match-­‐based   People  Search Collaborate with Shuguang Han, Zhen Yue
43 Document  Retrieval  
44 People  Retrieval  
Social  Match   is  Important  in   People  Search   45 because a tighter social similarity make it easier for people to connect Then Need the users’ social networks to return the potential candidates who have either direct or indirect connections with the given users.
But  Privacy  is  a  BIG  Concern   46 —  users  in  many  social  network   services  often  either  opt  out   from  certain  social  networks   or  provide  incomplete  or  even   fake  information  on  those   networks.     —  many  data  mining  algorithms   may  not  work  or  even  harm   the  user  experience  when   equipped  with  such   incomplete  and  noisy  social   information  
People  Search  Use  Co-­‐Author  Network   47 Which  has  the  advantage     of  lacking  privacy  concerns       But  this  limits     the  type  of  people  search   being  studied,     So  should  study     other  social  networks   which  has  privacy  concerns        
Our  Goals   —  interested  in  the  privacy  related  issues  in  people  search   and  the  impacts  of  these  issues  on  the  performance  of   people  search  systems.     —  users  in  many  social  network  services  are  able  to  keep  both  their   profile  and  social  connections  private   —  we  focus  on  the  privacy  issues  of  sharing  social  connections   —  simulating  the  privacy-­‐concerned  social  network  by   using  the  public  available  coauthor  networks.   —  Critical  need  for  privacy-­‐concerned  social  network  as  test  bed   —  Difficult  to  finding  an  open  privacy-­‐concerned  social  network  or  very   expensive  to  such  a  network  from  scratch  for  research  purpose,     48
Key  Assump-ons   —  a  coauthor  network  is  the  same  or  similar  with  a   privacy-­‐concerned  social  network,  because  studies   show   —  many  real-­‐world  social  networks  (including  coauthor  networks  and   many  other  privacy-­‐concerned  networks  such  as  Facebook  social   networks)  share  the  same  patterns   —  All  small-­‐world  networks  and  their  degree  distributions  are  highly  skewed.     —  assortative  patterns  (the  preferences  of  connecting  people  who  share   the  similar  features)  of  social  networks  are  all  assortatively  mixed,     —  whereas  the  technological  and  biological  seems  to  be  disassortative.   —  so  studying  academic  coauthor  networks,  which  are   publically  available,  can  be  the  surrogate  for  studying   privacy-­‐  preserving  social  networks     49
Research  Focuses   —  Identify  two  types  of  features,  both  used  in  people   search   —  Global  network  features:  the  features  that  are  propagated  through  the   whole  networks     —  measured  by  the  PageRank  value  running  on  the  whole  social  networks   —  Local  network  features:  the  features  that  are  directly  related  to  the   ego-­‐network  of  the  querying  user     —  measured  by  the  proportion  of  common  social  connections   50
Research  Ques-ons   —  RQ1:  How  to  properly  simulate  different  types  of   privacy-­‐preserving  social  networks?     —  RQ2:  How  does  each  privacy-­‐preserving  network  affect   the  global  and  local  network  features?     —  RQ3:  How  does  the  obtained  global  and  local  network   features  further  affect  the  people  search  performance?   That  is,  what  are  the  impacts  of  these  features  derived   from  privacy-­‐preserving  networks  on  the  search  process   of  finding  the  best  candidates  when  comparing  with  the   use  of  full  network  information?     51
Data   —  Academic  publication  collection   —  containing  219,677  conference  papers  from  the  ACM  Digital  Library.     —  between  1990  and  2013.     —  Only  public  available  information  of  a  paper:  the  title,  abstract  and  authors   —  No  further  author  disambiugation  besides  ACM  Digital  Library  author  ID   —  In  total,  the  collection  contains  253,390  unique  authors  and  953,685  coauthor   connection  instances.   —  Users’  people  search  activities:  Han  et  al.  [5]  evaluation  of  a  people   search  system.     —  four  different  people  search  tasks,  each  aimed  to  search  for  5  candidates.     —  A  baseline  plain  content-­‐based  people  search  system     —  An  experimental  system  that  enhances  people  search  with  three  interactive  facets:   content  relevance,  social  similarity  between  the  user  and  a  candidate  (the  local   network  feature)  and  the  authority  of  a  candidate  (the  global  network  feature).     —  The  experiment  system  allowed  the  querying  users  to  tune  the  value  associated  with  each  facet   in  order  to  generate  a  better  candidate  search  results.     —  24  participants  were  recruited  for  the  user  study.     —  At  the  beginning  of  the  user  study,  each  participant  was  asked  to  provide  their  publications  and   their  close  social  connections  (such  as  advisors).     —  In  the  post-­‐task  questionnaire,  the  participants  were  asked  to  rate  the  relevance  of  each  marked   candidate  in  a  Five-­‐point  Likert  scale  (1  as  non-­‐relevant  and  5  as  the  highly  relevant).   52
Formulas   53 pi is the probability of a given user has privacy concern, di is the degree of association of a user and dmax. Is the maximized degree in the network, λ helps to establish different selection strategies Mean Absolute Error (MAE) between new authority and ground-truth authority over all of the authors
Results   54
Results   55
Results   56
Results   57
Insights   —  Both  the  local  and  global  network  features  are  important  for  the   performance  of  people  search  (compare  to  not  using  social   network).     —  Comparing  to  the  global  network  feature,  the  local  network  feature  is  more   important.     —  Privacy-­‐concerns  reflected  in  local  and  global  network  features  can   significantly  influences  on  the  performance  of  people  search   —  The  privacy  concerns  from  the  high-­‐degree  candidates  in  the  network  will  have   more  impacts  on  global  features.     —  The  local  network  feature  is  related  to  both  the  querying  users  and  the  candidates   in  the  networks.     —  the  privacy  concerns  from  both  of  them  have  significant  impact  on  the  people  search   performance.     —  The  privacy  concerns  from  high-­‐degree  candidates  have  bigger  influences  on  the  people   search  than  that  of  the  lower-­‐degree  candidates,  especially  when  those  high-­‐degree   candidates  are  related  to  the  querying  user.     —  We  also  find  that  if  the  querying  users  provide  more  social  connections,  the  search   performance  would  increase  steadily.   58
Scholars  on  Academic   Social  Network   Services Collaborate with Wei Jeng and Jiepu Jiang
Formal scholarly communication describes activities or scholarly outcomes that can be viable over time to an extended audience. This availability over long periods of time, also known as permanent access, traditionally referred to publications in books or peer-reviewed journals.
Informal scholarly communication is made scholarly outcomes “available to a restricted audience only” (as cited in Borgman, 2007, p. 49), such as self-publishing, Listerv, mails, or a “coffee break” in a conferences where scholars can exchange information.
Academic  Social  Networking  Service   (ASNS)   —  The  term  academic  social  networking  service  as  a  broad   term  that  refers  to  an  online  service,  tool,  or  platform   that  can  help  scholars  to  build  their  professional   networks  with  other  researchers  and  facilitate  their   various  activities  when  conducting  research.     —  Some  well-­‐known  examples  of  ASNSs  include     —  ResearchGate.net  (http://www.researchgate.net/)   —  Academia.edu  (http://www.academia.edu/)     —  Mendeley.com  (http://www.mendeley.com/)  
Features  on  ASNSs   —  ASNSs  allow  users  to     —  create  profiles  with  academic  properties   —  upload  theirs  publications   —  create  online  groups     —  Some  ASNS,  such  as  Mendeley  and  Zotero,  even  offer   software  applications,  such  as  bibliographic  tools  to   support  scholars  in  managing  their  documents  and   citations.  
If academic social network sites are providing an alternative channel to support informal scholarly communication, then it is important to study: What the implications we can learn from analyzing academic users’ actual usages on those sites. 65
Our  Research  Ques-ons   — RQ1:  Who  are  the  users  of  an  academic  social   networking  service  (ASNS)  that  supports  open   groups?     — RQ2:  In  what  ways,  and  how  often,  do  such   group  participants  use  an  ASNS?     — RQ3:  What  motivates  ASNS  users  to  utilize   social  or  research  features  on  an  ASNS?    
Research  Site:  Mendeley   —  Launched  in  2008,  Mendeley  (http:// www.mendeley.com/)  is  one  of  the  most  popular  ASNSs   and  has  more  than  two  million  users.   —  Mendeley  allows  users  to  build  their  own  digital   research  library  by  importing  PDF  files  from  their  local   devices.   —  There  are  three  common  ways  to  use  social  features  on   Mendeley:  maintain  a  profile,  manage  existing  contacts,   and  make  more  connections.  
The  profile  page  
The  group  page  
Our  Samples:  Mendeley  Group   —  Mendeley  allows  users  to  start  groups  to  share  what   they  are  interested  in  and  what  they  are  reading  about.     —  Two  types  of  groups  supported  on  the  site:     —  private  groups  that  are  only  visible  to  the  members;   —  public  groups  that  are  publicly  visible  and  can  be  searched  in   Mendeley’s  group  list.   —  We  adopted  a  representative  sampling  method  to   identify  Mendeley’s  large  open  group  users.    
Method  and  data  collec-ng   —  We  chose  a  cross-­‐sectional  survey  as  the  research   method  to  answer  these  research  questions  and   developed  a  questionnaire  with  30  questions.     —  The  questionnaire  was  distributed  to  97  open  groups  in   Mendeley,  one  of  the  most  popular  ASNSs.  
The  instrument-­‐I   —  Basic  Information     —  The  Extent  of  Use:  Questions  that  aim  to  determine  the   extent  of  participants’  account  activities  on  Mendeley   —  Common  Ways  to  Use:     —  as  a  document  management  tool,     —  a  reference  manager,     —  a  scholarly  search  engine,     —  an  online  portfolio,     —  a  friend  management  tool,  and     —  a  socialization  tool  
The  instrument-­‐II   —  The  Extent  of  Group  Use   —  Motivation:   —  Information     —  Networking   —  Visibility   —  Altruistic  
Result:  Overview   —  We  received  188  responses  via  the  questionnaire,  but   only  146  users  completed  the  entire  questionnaire.     —  The  average  age  of  the  participants  was  35.04  years   (SD=10.81),  and  64%  of  them  were  male  (N=94).   —  We  obtained  responses  from  users  in  20  disciplines  in   Mendeley.     —  The  top  three  disciplines  represented  were  computer   and  information  science  (N=43),  biological  science   (N=24),  and  social  science  (N=17).    
The  Par-cipants   — Distribution  of  academic  disciplines   —  Early  adaptor:  Biomedicine  users     —  Newcomers:  Social  Sciences   —  Lack  of  humanities,  literature,  philosophy,  and  design  users   — Distribution  of  academic  positions  
Frequency  of  Account  Ac-vi-es     ASNS  users  are  not  as  active  as  general  SNS  users:   —  53%  of  respondents  visited  their  accounts  on  a  weekly   basis,  while  36%  of  them  accessed  the  site  at  least  once   per  month.   —  Also  more  than  half  (53%)  of  the  participants  reported   that  they  were  checking  the  news  feeds  only  on  a   monthly  basis.  
Ways  to  Use  Mendeley   —  Participants  primarily  used  Mendeley  as     —  a  document  management  tool   —  citation  management  software   —  The  portion  of  those  using  Mendeley  as  a  social   networking  site  was  relatively  low:  Only  11%  of   respondents  used  Mendeley  to  manage  their  existing   academic  friends  and  to  expand  their  professional   networks.     —  These  results  indicate  that  most  of  our  participants   use  Mendeley  for  its  research  features,  rather  than   social  features.    
Mo-va-ons  for  Joining  Groups   Mo-va-on   Motivation  Items     M   Information Keep  up  with  a  user’s  research  domain 4.30 Get  research-­‐related  questions  answered 3.41 Follow  topics  that  community  is  paying  attention   to 3.98 Networking Connect  with  people  who  have  similar   research  interests   3.91 Expand  current  social  network 3.23 Meet  more  academic  people 3.27 Keep  in  touch  with  people  one  already  knows 3.08 Visibility Gain  professional  visibility 3.48 Be  present  in  current  discussions 3.27 Altruistic   Contribute  to  the  reading  list   3.62 Table: Users’ motivation in terms of joining a Mendeley group created by others.
Factors  that  may  influence  on  the  outcome  of   group  joining  mo-ves   —  Results  suggest  that  people  are  most  motivated  by   visibility  and  altruism  when  considering  whether  to   join  or  follow  more  groups.   —  Even  if  users  frequently  and  regularly  engaged  in   research-­‐based  activities  on  Mendeley  (such  as  a   document  management  or  citation  management  tool)  ,   it  would  not  make  any  difference  in  terms  of  their   intentions  of  joining  groups.  
Insights  Based  on  the  Findings   — What  is  Mendeley  exactly?     — Discipline  Distribution  and  Development  on   Mendeley   — Academic  Networking  or  Social  Networking?   — The  incentives  of  group  joining  
Mendeley:  A  Plaorm  for  Higher   Educa-on  Users   —  Our  findings  confirmed  that  the  majority  of  Mendeley   users  were  from  the  higher  education  environment.   More  specifically,  junior  researchers  (i.e.,  doctoral   students,  post-­‐doctoral  fellows,  and  graduate  students).     —  For  those  researchers  who  would  like  to  study  junior   scholars’  information  behaviors  or  run  a  survey  on  a   wide  range  of  online  scholars,  we  believe  that  an  ASNS   such  as  Mendeley  are  the  right  platforms  to  use  to   reach  those  types  of  participants.    
Discipline  Distribu-on  and   Development  on  Mendeley   —  Our  results  show  that  the  discipline  development  in   Mendeley  is  uneven.     —  Early  users  in  Mendeley  groups  mostly  came  from  the   fields  of  computer  &  information  science  and   biomedicine,  whereas  more  recent  users  are  mostly   from  the  fields  of  social  science,  education  and   psychology.     —  We  do  not  see  many  group  users  from  the  humanities   and  other  related  fields.    
Academic  Networking  vs.  Social   Networking   —  “Academic”  but  not  “Social”?   —  Users  of  Mendeley  seem  to  mainly  concentrate  on  the   utilities  directly  related  to  their  research  work,  while   mostly  ignoring  its  social  features,  such  as  “friend   making”.   —  Warning  for  ASNS  developers:  Do  not  simply  adopt   “Facebook-­‐like”  or  “LinkedIn-­‐like”  social  elements   when  designing  an  ASNS  platform  for  academic  users.  
Join  a  Group?  Show  Me  the  Incen-ve.   —  A  gap  between  motivation  and  incentive:  The  altruistic   motivation  was  one  of  the  most  critical  reasons   associated  with  their  group  engagement,  yet  none  of   current  features  of  Mendeley  reward  scholars  for  their   altruistic  activities.   —  Possible  incentive  mechanisms  to  encourage   interactions  :   —  providing  of  affective  feedback  by  group  owners  or  members,  to  users   who  contributed   —  establishing  level-­‐based  honor  system  or  badging  system  
Limita-ons   —  We  sampled  only  open  and  large  groups  on  Mendeley   instead  of  a  random  sample.     —  The  discipline  and  position  distribution  of  the   participants  may  be  biased  towards  users  who  are  the   use  group  of  social  feature  and  highly  engaged  in  group   activities.   —  If  researchers  would  like  to  investigate  the  wider   landscape  of  ASNS  users,  larger-­‐scaled  and  random   sampling  approaches  are  needed.  
Closing  Remarks
87
Challenges  
Challenges  
Challenges  
Challenges   —  Know  the  boundary  of   Social  Information   Access   —  How  to  identify  which  tasks   are  good  for  social   information  access?   —  How  to  effectively  integrate   social  networking,  direct   messaging,  and  social   recommendations  with   current  search  facilities.  
Related  Publica-ons   —  Z.  Yue,  S.  Han,  D.  He.  J.  Jiang.  Influences  on  Query  Reformulation   in  Collaborative  Web  Search.  IEEE  Compute,  2014  (3):46-­‐53.   —  Z.  Yue,  S.  Han,  D.  He.  Modeling  Search  Processes  using  Hidden   States  in  Collaborative  Exploratory  Web  Search.  The  17th  ACM   Conference  on  Computer  Supported  Cooperative  Work  and  Social   Computing  (CSCW  2014).   —  Z.  Yue,  S.  Han,  D.  He.  An  Investigation  on  the  Query  Behavior  in   Task-­‐based  Collaborative  Exploratory  Web  Search.  The  76th   Annual  Meeting  of  the  Association  for  Information  Science  and   Technology.  (ASIST  2013).   —  Jeng,  W.,  He,  D.,  &  Jiang,  J.  (In  press).  User  Participation  in  an   Academic  Social  Networking  Service:  A  Survey  of  Open  Group   Users  on  Mendeley.  Journal  of  the  Association  for  Information   Science  and  Technology.   92
93 Really Tough Questions Please!!!
Acknowledgement     —  The  work  presented  here  were  conducted  by  faculty  and   students  in  Information  Retrieval,  Integration  and   Synthesis  Lab  at  School  of  Information  Sciences   —  Other  people  participated  in  these  works  are   —  Prof.  Peter  Brusilovsky,  Prof  Dan  Wu  etc.   —  These  work  are  partially  supported  by  the  National   Science  Foundation  

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Social Information Access: A Personal Update

  • 1.
         Social  Informa-on  Access   -­‐-­‐  a  Personal  Update 2014/6/18
  • 2.
    Agenda   2 Collaborative ExploratorySearch Privacy Concerns in Social-based People Search Some Reflections Conclusions Social Information Access Scholars in Academic Social Networks
  • 3.
    Informa-on  Access   — Information  Access:  an  interactive  process  starts  with  a   user  noticing  his/her  needs  and  ends  with  the  user   obtaining  the  necessary  information   —  Iterative,  multiple  stages,  many  back  loops  
  • 4.
    User  Generated  Content   Social  Networks  
  • 5.
    Social  Informa-on  Access   —  Social  Information  Access:  information   access  using  “community  wisdom”     —  distilled  from  the  actions  in  real/virtual   community     —  Collaboration  in  explicit  or  implicit  manner   —  Social  information  access  technologies   capitalize  on  the  natural  tendency  of   people  to  follow  direct  and  indirect  cues   of  others’  activities   —  going  to  a  restaurant  that  seems  to  attract  many   customers,  or   —  asking  others  what  movies  to  watch.  
  • 6.
    Space  of  Social  Informa-on  Access   —  [Brusilovsky2012]’s    taxonomy  for  social  info  access   6
  • 7.
    More  Social  Informa-on  Access   —  Collaboration  can  be  explicit,  not  just  implicit   —  Explicit  Collaboration:  users  work  as  a  team  to  complete  the  same   task   —  Issues:  How  to  model  collaboration?   7 Implicit Collaboration Explicit Collaboration
  • 8.
    More  Social  Informa-on  Access   —  Target  can  be  people,  and  people’s  social  connections     are  important   —  Relationship  is  as  important  as  the  documents  generated  by  the  people   —  Issues:  What  are  the  impacts  of  privacy  concerns?   8
  • 9.
    More  Social  Informa-on  Access   —  User  generated  content  can  be  generic,  or  academic   —  E-­‐Science  and  CyberScholarship  are  increasingly  popular   —  Issues:  What  are  scientists  doing  online?   9 Popular Social Networks Academic Online Social Networks
  • 10.
    Explicit   Collaboration:   Collaborative   Exploratory  Search Collaborate with Zhen Yue, Shuguang Han
  • 11.
    Collabora-ve  Exploratory  Search   11 —  Daily  collaborative  Web   search  increased   dramatically   —  0.9%  in  2006  -­‐>  11%  in  2012   (Morris,  2013)   Complex  interactions  -­‐>   difficult  to  study   collaborative  search   processes  
  • 12.
    —  Many  studies  on  individual  search  process     —  Well-­‐established  models  such  as  Kuhlthau’s  model  (Kuhlthau,  1991)   and  Marchionini’s  model  (Marchionini,  1995)   —  Many  studies  on  analyzing  the  transition  patterns  of  actions  (Chen   &  Cooper,  2002),  search  tactics  (Xie  &  Joo,  2010)  or  search  strategies   (Belkin,  1995)  in  the  process     —  A  few  studies  on  collaborative  search  process   —  Several  studies  look  into  the  application  of  individual  search   models  in  the  collaborative  environment  (Hyldegard,  2006;  Shah  &   González-­‐ibáñez,  2010).   —  An  investigation  on  before,  during  and  after  search  stages  in  social   search  (Evans  &  Chi,  2008).   Status  on  Search  Processes   12
  • 13.
    Research  Ques-ons   — RQ1.  How  to  model  the  search  states  in  the   collaborative  exploratory  search  process?    What  are  the   characteristics  of  search  states  in  the  collaborative   exploratory  search  process?   —  Search  states:  basic  units  of  a  search  process   —  RQ2.  What  are  the  characteristics  of  query  behaviors  in   the  collaborative  exploratory  search  process?   —  RQ3.  What  are  the  characteristics  of  communications   between  team  members  in  the  collaborative   exploratory  search  process?   13 Holistic view of collaborative search processes Two key components of the collaborative search process
  • 14.
    CollabSearch:  a  Collaborative  Search  System     http://crystal.exp.sis.pitt.edu:8080/CollaborativeSearch/ q  Search functions - Web Search - Save/edit/rate/tag Web pages/snippets - Space for search task description q  Collaboration functions - Chat - Share search queries - Share saved Web pages/snippets System:  CollabSearch   14
  • 15.
    Experiment  Design   — Two  Experiment  Conditions   —  COL:  Collaborative  search     –  Two  participants  worked  as  a  team  on  the  same  search  task  simultaneously.     —  IND:  Individual  search     –  One  participant  worked  on  the  search  task  individually.     —  Participants     —  36  participants  (18  pairs)  in  COL  condition   —  18  participants  in  the  IND  condition   —  Two  Exploratory  Search  Tasks  (30mins/task)   —  Information-­‐gathering  task     –  collecting  information  for  writing  a  report  on  the  impact  of  social  network   (Shah  &  Marchionini,  2010).     —  Decision-­‐making  task     –  collecting  information  for  planning  a  trip  to  Finland  (Paul  &  Morris,  2010).     —  The  orders  of  the  two  tasks  were  rotated   15
  • 16.
    HMM  Model  of  Search  Process   16
  • 17.
    Approaches  for  Study  Search  Processes   —  Analyze  qualitative  constructs  in  the  search  process     —  Kuhlthau,  1991;  Ellis,  1993;  Marchionini,  1995   —  Search  pattern  analysis  based  on  logged  behavior   —  Directly  use  logged  actions  (Holscher  &  Strube,  2000)   —  Ignore  user  intentions  such  as  search  tactics   —  Manually  code  search  tactics/strategies  on  log  data  (Xie&Joo,2010)   —  Time-­‐consuming  and  need  a  theoretical  model  to  generate  codes   —  Our  approach:  Hidden  Markov  Model   —  Model  search  tactics  as  hidden  states   —  An  automatic  approach  for  analyzing  time  sequential  events   17
  • 18.
    —  A  two-­‐level  view  of  search  process   —  Higher  level:  search  states  as  hidden  search  tactics  or  strategies   —  Lower  level:  observable  actions   —  Parameters  in  HMM   —  Number  of  hidden  states  N  (N  ≠  M)   —  Transition  probabilities  among  any  two  hidden  states   —  Emission  probability  from  each  state  to  each  action   A Hidden Markov Model for Search States Observable actions Hidden search tactics or strategies Model  Search  States  using  HMM   18 Transition     probabilitie s   Emission     probability     !1   !2   !3   !%   &1   &2   &3   &%  
  • 19.
    Categorizing  Observable  Ac-ons   Method Search Scan Select Capture Communicate Object Query Topic statement Item in search result Chat messages List of saved items Single saved item Source Self Partner Shared/mix Inspired by Belkin’s ISSs model (Belkin, 1995) but with modifications to accommodate the context in collaborative web search. 19
  • 20.
    Actions Descriptions Search  –  query  –  self  (Q) A  user  issues  a  query Select-­‐  item-­‐self  (V) A  user  clicks  on  a  result  in  the  returned  result  list Capture-­‐item-­‐self  (S) A  user  saves  a  snippet  or  bookmarks  a  webpage Scan-­‐list  of  saved  item  –  mixed  (Wm) A   user   checks   the   workspace   without   clicking   on   any  particular  item. Select  –  single  saved  item  –self  (Ws) A  user  clicks  on  an  item  in  the  workspace  saved  by   him/herself Select  –  single  saved  item  –  partner  (Wp) A  user  clicks  on  an  item  in  the  workspace  saved  by   the  partner Scan-­‐topic  -­‐shared  (T) A  user  clicks  on  the  topic  statement  for  view   Communicate-­‐  messages-­‐self  (Cs) A  user  sends  a  message  to  the  other  user   Communicate-­‐message-­‐partner  (Cp) A  user  receives  a  message  from  the  other  user 20 Ac-ons  Observed  in  this  Study  
  • 21.
    Parameters  Selec-on  and  Es-ma-on     —  Determine  number  of  hidden  states  N   —  Bayesian  Information  Criterion  (BIC)   —  Parameter  estimations   —  Baum-­‐Welch  algorithm:  maximize  data  likelihood   —  Random  assign  a  start  probability  π  for  each  state,     —  Estimate  the  transition  probabilities  and  emission  probability  through  a   machine  learning  process     21 6000 6500 7000 7500 8000 2 3 4 5 6 7 26000   27500   29000   30500   32000   4   5   6   7   8   9   10   IND: N=4 COL: N=6 BIC=-2×logL+log(S) ×NP log-likelihood (logL) Number of parameters (NP) sample size(S)
  • 22.
    HMM  Output  for  Individual  Search   Query View Save Workspace self Topic HQ 0.99 HV 0.91 HS 0.96 HD 0.57 0.42 Hidden States and Emission Probabilities in IND (values<0.05 are omitted) 22 Search-related hidden states: HQ, HV, HS HQ: hidden states of querying HV: hidden states of viewing a search result HS: hidden states of saving a search result Sensemaking-related hidden states: HD HD: hidden states of defining current search problem
  • 23.
    Compare  to  Exis-ng  Search  Models   0.39 0.12 HQ 0.33 HV HS 0.50 HD 0.07 0.85 0.59 0.49 0.26 0.31 0.12 HQ 0.32 HV HS 0.56 HD 0.13* 0.86 0.38 0.53 0.34 0.39 0.23 Sub-processes in the ISP model HMM Define Problem HD Select Source, Formulate Query, Execute Query HQ Examine Results HV Extract Information HS Reflect/Iterate/Stop HD Mapping from sub-process in Marchionini’s ISP model to the hidden states transition probabilities of hidden states in IND The default transition in Marchinoinini’s model can be mapped to into HDàHQàHVàHSàHD, which is also the pattern of the highest probability in HMM results. 23
  • 24.
    HMM  Outputs  in  Collabora-ve  Search   Query View Save Workspace mix Workspace self Workspace partner Topic Chat send Chat receive HQ 0.82 0.13 HV 0.87 0.1 HS 0.88 HD 0.36 0.36 0.21 HW 0.37 0.44 0.12 HC 0.44 0.47 Hidden States and Emission Probability in COL Search-related hidden states: HQ, HV, HS Sensemaking-related hidden states: HD, HW, HC HD: hidden states of defining current search problem HW: hidden states of implicit communication HC: hidden states of explicit communication (Paul & Morris, 2010): chat-centric sensemaking (HC) and workspace- centric sensemaking (HW) (Evans & Chi, 2008): search and sensemaking are tightly coupled 24
  • 25.
    Detec-ng  Task  Differences  using  HMM   0.11 HQ 0.26 HV HS 0.45 HD 0.06 0.86 0.56 0.31 0.08 0.46 0.30 HC HW 0.56 0.09 0.14 0.16 0.16 0.14 0.15 HQ 0.22 HV HS 0.33** HD 0.12 0.84 0.28** 0.35** 0.20** 0.42 0.24 0.13 HC 0.89 HW 0.48 0.06 0.30** 0.07** 0.33* 0.13** Comparison of Transition Probabilities of Hidden states in COL for the two tasks (red arrows indicate significant difference: *p<0.05, **p<0.01) Cross-category transitions: From search to sensemaking From sensemaking to search Cross-category transitions is more common in collaborative search than in individual search Cross-category transition is more common in decision-making task than in information gathering task. 25 Information-gathering Decision-making
  • 26.
    Query  Behaviors  and  Communica-ons   26
  • 27.
    27 Search condition (individual or collaborative) Tasktype (information-gathering or decision-making) Query vocabulary features (number of queries, query vocabulary richness, query diversity) Query reformulation patterns (New, Generalization, Specification, Reconstruction) Query performance (Precision, recall, Successful query rate, user satisfaction, cognitive load) Research  Design   Independent Variables Dependent Variables Communication Timing Communication Content
  • 28.
    28 •  Query  Vocabulary  Richness  (QVR)   •  Query  Diversity  (QD)     •  Levenshtein  distance  (Shah  &  González-­‐Ibáñez,  2011)       •  calculate  the  difference  between  a  pair  of  queries.   •  Query  Result  Similarity  (QRS)     Query  Vocabulary  Features   (Kromer, Snasel, & Platos, 2008)
  • 29.
    Query  Reformula-on  PaPerns   29 Type Definition New (N) Qi is the first query or does not share any common terms with Qi-1 Generalization (Ge) Qi shares common terms with Qi-1 ; and Qi contains fewer terms than Qi-1 Specialization (Sp) Qi shares common terms with Qi-1 ; and Qi contains more terms than Qi-1 Reconstruction (Rc) Qi shares common terms with Qi-1 ; and Qi has the same length as Qi-1 •  Qi-1 and Qi are two consecutive queries in the same search session •  The patterns are defined based on (He et al. 2002; Jansen et al. 2009)
  • 30.
    Query  performance   30 Objective   Measurements   Precision  &   Recall   Successful   Query  Rate   Subjective   Measurements   User   Satisfaction   Cognitive  Load  
  • 31.
    Communica-on  Timing  Analysis   —  Before  search:  communications  before  the  first  search   action   —  During  search:  communications  between  the  first  and  last   search  action   —  After  search:  communications  after  the  last  search  action   —  (Search  actions:  issuing  a  query,  viewing  or  saving  a  search  result)   31  Chat    Chat    Chat    Chat   Before   Search During   Search During   Search A<er   Search The first search action The last search action Search action Link to rational
  • 32.
    Collabora-on  Benefit  I:  Rich   Vocabularies  and  Diverse  Queries   32 T2Task IND COL 0 0.5 1 1.5 2 2.5 3 T1 T2 QVR Task IND COL Query vocabulary richness Query diversity 0 5 10 15 20 25 30 T1 T2 QD Task IND COL .00 .02 .04 .06 .08 .10 T1 T2 QRS Task IND COL Chat  time   QVR   Total     ↑(p=0.045)   Before  search   -­‐   During  search   -­‐   After  search   -­‐   ü Participants in the collaborative search were able to employ wider range of vocabularies for the queries and the queries were more diverse. ü A positive correlation was found between the total chat time and the query vocabulary richness. *Differences showed in the graph are significant
  • 33.
    Collabora-on  Cost  I:  Low  Recall  and   Low  Successful  Query  Rate   33 Recall Successful Query Rate Chat  time   Recall   Total     ↓(p=0.001)   Before  search   ↓(p=0.022)   During  search   -­‐   After  search   ↓(p<0.001)   ü Collaboration takes times and efforts. Participants had less time to devote to search, and they were more stringent on the what documents to save. ü A negative correlation was found between the communication and recall.
  • 34.
    Collabora-on  Benefit  II:  People  More   Sa-sfied  and  less  stressed   34 User Satisfaction Cognitive Load *Differences showed in the graph are significant Chat  time   Satisfaction   CogLoad   Task  social   ↑(p=0.017)   ↓(p=0.022)   Task   coordination   -­‐   -­‐   Task  content     -­‐   ↑(p=0.008)   Non-­‐task   -­‐   -­‐   ü Participants in collaborative search are more satisfied with the performance and have lower cognitive load. ü A positive correlation was found between the task social communication and satisfaction (negative for Cogload).
  • 35.
    Collabora-on  Affects  the  PaPerns  of   Query  Reformula-on   35 0.1 0.2 0.3 0.4 0.5 IND COL New Generalization Specification Reconstruction 0.1 0.2 0.3 0.4 0.5 Task  1 Task  2 New Generalization Specification Reconstruction ü Higher percentages of New and Specialization and lower percentage of Reconstruction in the collaborative search. ü Participants in collaborative search were able to explore the divided subtopics in depth while the participant in the individual search owns the entire search topic and the scope maybe the first priority.
  • 36.
    Communica-on  PaPerns   36 Proportionof each communication content type within stage (Left: T1; Right: T2) ü Communication is common in any of three stages. ü The communication content varies in the three stages. The before search stage communication is more focused on the task coordination. The during search stage communication is more focused on the task content. Task social communication is more common in the before search and after search stage than in the during search stage.
  • 37.
  • 38.
    What  We  Learned   —  Collaborative  search  process  have  patterns   —  More  collaboration-­‐oriented  actions  as  the  collaboration  level   increase   —  Transitions  within  search-­‐oriented  actions  and  within   collaboration-­‐oriented  actions  are  more  frequent  than   between  them  in  all  three  conditions.     —  Explicit    and  implicit  communication  has  potential   benefit  on  helping  using  generating  query  ideas.   38
  • 39.
    Implica-ons  for  Collabora-ve  Search   Research   —  Search  activities  and  sensemaking  activities  are  tightly   coupled  in  the  collaborative  search.   —  The  studies  of  collaborative  search  should  not  just   concentrate  on  the  effectiveness  of  search,  but  also  on   the  users’  perception  of  their  search  experiences,   particularly  their  satisfaction  and  cognitive  load.   —  The  wider  range  of  query  vocabulary  in  collaborative   search  did  not  necessarily  lead  to  a  more  effective   search  outcomes.   39
  • 40.
    Implica-ons  for  Collabora-ve   Search  System  Design   —  It’s  important  to  design  interface-­‐mediated  support  for   the  coordination  among  team  members  as  the   coordination  through  communication  is  costly.   —  Provide  targeted  algorithm-­‐mediated  query   suggestions  based  on  the  findings  of  how  users   reformulate  queries  in  the  collaborative  search.     —  Designers  need  to  make  a  balance  between  the  support   for  fulfilling  the  search  task  and  the  support  for  social   interactions  among  team  members.   40
  • 41.
    Implica-ons  for  Other  Researchers   —  Researchers  in  CSCW  and  CSCL  can  draw  insights  on   —  Sense-­‐making  intertwined  with  the  activities  that  directly  aim  for   fulfilling  the  task  requirements.     —  Consider  the  social  gain  and  emotional  support  when  evaluating  the   team  effectiveness.     —  The  findings  on  the  differences  between  information-­‐gathering  and   decision-­‐making  collaboration  tasks.     —  Researchers,  try  HMM  if  you   —  are  analyzing  complex  interactive  process  in  a  time  sequence   —  don’t  have  a  theoretical  model  to  start  with   —  want  to  see  the  patterns  of  your  data  before  applying  time-­‐consuming   qualitative  annotation   —  are  interested  in  the  hidden  strategies  or  tactics  underneath  the   observable  actions  of  users   41
  • 42.
    Privacy  Concerns  in   Social  Match-­‐based   People  Search Collaborate with Shuguang Han, Zhen Yue
  • 43.
  • 44.
  • 45.
    Social  Match   is  Important  in   People  Search   45 because a tighter social similarity make it easier for people to connect Then Need the users’ social networks to return the potential candidates who have either direct or indirect connections with the given users.
  • 46.
    But  Privacy  is  a  BIG  Concern   46 —  users  in  many  social  network   services  often  either  opt  out   from  certain  social  networks   or  provide  incomplete  or  even   fake  information  on  those   networks.     —  many  data  mining  algorithms   may  not  work  or  even  harm   the  user  experience  when   equipped  with  such   incomplete  and  noisy  social   information  
  • 47.
    People  Search  Use  Co-­‐Author  Network   47 Which  has  the  advantage     of  lacking  privacy  concerns       But  this  limits     the  type  of  people  search   being  studied,     So  should  study     other  social  networks   which  has  privacy  concerns        
  • 48.
    Our  Goals   — interested  in  the  privacy  related  issues  in  people  search   and  the  impacts  of  these  issues  on  the  performance  of   people  search  systems.     —  users  in  many  social  network  services  are  able  to  keep  both  their   profile  and  social  connections  private   —  we  focus  on  the  privacy  issues  of  sharing  social  connections   —  simulating  the  privacy-­‐concerned  social  network  by   using  the  public  available  coauthor  networks.   —  Critical  need  for  privacy-­‐concerned  social  network  as  test  bed   —  Difficult  to  finding  an  open  privacy-­‐concerned  social  network  or  very   expensive  to  such  a  network  from  scratch  for  research  purpose,     48
  • 49.
    Key  Assump-ons   — a  coauthor  network  is  the  same  or  similar  with  a   privacy-­‐concerned  social  network,  because  studies   show   —  many  real-­‐world  social  networks  (including  coauthor  networks  and   many  other  privacy-­‐concerned  networks  such  as  Facebook  social   networks)  share  the  same  patterns   —  All  small-­‐world  networks  and  their  degree  distributions  are  highly  skewed.     —  assortative  patterns  (the  preferences  of  connecting  people  who  share   the  similar  features)  of  social  networks  are  all  assortatively  mixed,     —  whereas  the  technological  and  biological  seems  to  be  disassortative.   —  so  studying  academic  coauthor  networks,  which  are   publically  available,  can  be  the  surrogate  for  studying   privacy-­‐  preserving  social  networks     49
  • 50.
    Research  Focuses   — Identify  two  types  of  features,  both  used  in  people   search   —  Global  network  features:  the  features  that  are  propagated  through  the   whole  networks     —  measured  by  the  PageRank  value  running  on  the  whole  social  networks   —  Local  network  features:  the  features  that  are  directly  related  to  the   ego-­‐network  of  the  querying  user     —  measured  by  the  proportion  of  common  social  connections   50
  • 51.
    Research  Ques-ons   — RQ1:  How  to  properly  simulate  different  types  of   privacy-­‐preserving  social  networks?     —  RQ2:  How  does  each  privacy-­‐preserving  network  affect   the  global  and  local  network  features?     —  RQ3:  How  does  the  obtained  global  and  local  network   features  further  affect  the  people  search  performance?   That  is,  what  are  the  impacts  of  these  features  derived   from  privacy-­‐preserving  networks  on  the  search  process   of  finding  the  best  candidates  when  comparing  with  the   use  of  full  network  information?     51
  • 52.
    Data   —  Academic  publication  collection   —  containing  219,677  conference  papers  from  the  ACM  Digital  Library.     —  between  1990  and  2013.     —  Only  public  available  information  of  a  paper:  the  title,  abstract  and  authors   —  No  further  author  disambiugation  besides  ACM  Digital  Library  author  ID   —  In  total,  the  collection  contains  253,390  unique  authors  and  953,685  coauthor   connection  instances.   —  Users’  people  search  activities:  Han  et  al.  [5]  evaluation  of  a  people   search  system.     —  four  different  people  search  tasks,  each  aimed  to  search  for  5  candidates.     —  A  baseline  plain  content-­‐based  people  search  system     —  An  experimental  system  that  enhances  people  search  with  three  interactive  facets:   content  relevance,  social  similarity  between  the  user  and  a  candidate  (the  local   network  feature)  and  the  authority  of  a  candidate  (the  global  network  feature).     —  The  experiment  system  allowed  the  querying  users  to  tune  the  value  associated  with  each  facet   in  order  to  generate  a  better  candidate  search  results.     —  24  participants  were  recruited  for  the  user  study.     —  At  the  beginning  of  the  user  study,  each  participant  was  asked  to  provide  their  publications  and   their  close  social  connections  (such  as  advisors).     —  In  the  post-­‐task  questionnaire,  the  participants  were  asked  to  rate  the  relevance  of  each  marked   candidate  in  a  Five-­‐point  Likert  scale  (1  as  non-­‐relevant  and  5  as  the  highly  relevant).   52
  • 53.
    Formulas   53 pi isthe probability of a given user has privacy concern, di is the degree of association of a user and dmax. Is the maximized degree in the network, λ helps to establish different selection strategies Mean Absolute Error (MAE) between new authority and ground-truth authority over all of the authors
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
    Insights   —  Both  the  local  and  global  network  features  are  important  for  the   performance  of  people  search  (compare  to  not  using  social   network).     —  Comparing  to  the  global  network  feature,  the  local  network  feature  is  more   important.     —  Privacy-­‐concerns  reflected  in  local  and  global  network  features  can   significantly  influences  on  the  performance  of  people  search   —  The  privacy  concerns  from  the  high-­‐degree  candidates  in  the  network  will  have   more  impacts  on  global  features.     —  The  local  network  feature  is  related  to  both  the  querying  users  and  the  candidates   in  the  networks.     —  the  privacy  concerns  from  both  of  them  have  significant  impact  on  the  people  search   performance.     —  The  privacy  concerns  from  high-­‐degree  candidates  have  bigger  influences  on  the  people   search  than  that  of  the  lower-­‐degree  candidates,  especially  when  those  high-­‐degree   candidates  are  related  to  the  querying  user.     —  We  also  find  that  if  the  querying  users  provide  more  social  connections,  the  search   performance  would  increase  steadily.   58
  • 59.
    Scholars  on  Academic   Social  Network   Services Collaborate with Wei Jeng and Jiepu Jiang
  • 60.
    Formal scholarly communicationdescribes activities or scholarly outcomes that can be viable over time to an extended audience. This availability over long periods of time, also known as permanent access, traditionally referred to publications in books or peer-reviewed journals.
  • 61.
    Informal scholarly communicationis made scholarly outcomes “available to a restricted audience only” (as cited in Borgman, 2007, p. 49), such as self-publishing, Listerv, mails, or a “coffee break” in a conferences where scholars can exchange information.
  • 63.
    Academic  Social  Networking  Service   (ASNS)   —  The  term  academic  social  networking  service  as  a  broad   term  that  refers  to  an  online  service,  tool,  or  platform   that  can  help  scholars  to  build  their  professional   networks  with  other  researchers  and  facilitate  their   various  activities  when  conducting  research.     —  Some  well-­‐known  examples  of  ASNSs  include     —  ResearchGate.net  (http://www.researchgate.net/)   —  Academia.edu  (http://www.academia.edu/)     —  Mendeley.com  (http://www.mendeley.com/)  
  • 64.
    Features  on  ASNSs   —  ASNSs  allow  users  to     —  create  profiles  with  academic  properties   —  upload  theirs  publications   —  create  online  groups     —  Some  ASNS,  such  as  Mendeley  and  Zotero,  even  offer   software  applications,  such  as  bibliographic  tools  to   support  scholars  in  managing  their  documents  and   citations.  
  • 65.
    If academic socialnetwork sites are providing an alternative channel to support informal scholarly communication, then it is important to study: What the implications we can learn from analyzing academic users’ actual usages on those sites. 65
  • 66.
    Our  Research  Ques-ons   — RQ1:  Who  are  the  users  of  an  academic  social   networking  service  (ASNS)  that  supports  open   groups?     — RQ2:  In  what  ways,  and  how  often,  do  such   group  participants  use  an  ASNS?     — RQ3:  What  motivates  ASNS  users  to  utilize   social  or  research  features  on  an  ASNS?    
  • 67.
    Research  Site:  Mendeley   —  Launched  in  2008,  Mendeley  (http:// www.mendeley.com/)  is  one  of  the  most  popular  ASNSs   and  has  more  than  two  million  users.   —  Mendeley  allows  users  to  build  their  own  digital   research  library  by  importing  PDF  files  from  their  local   devices.   —  There  are  three  common  ways  to  use  social  features  on   Mendeley:  maintain  a  profile,  manage  existing  contacts,   and  make  more  connections.  
  • 68.
  • 69.
  • 70.
    Our  Samples:  Mendeley  Group   —  Mendeley  allows  users  to  start  groups  to  share  what   they  are  interested  in  and  what  they  are  reading  about.     —  Two  types  of  groups  supported  on  the  site:     —  private  groups  that  are  only  visible  to  the  members;   —  public  groups  that  are  publicly  visible  and  can  be  searched  in   Mendeley’s  group  list.   —  We  adopted  a  representative  sampling  method  to   identify  Mendeley’s  large  open  group  users.    
  • 71.
    Method  and  data  collec-ng   —  We  chose  a  cross-­‐sectional  survey  as  the  research   method  to  answer  these  research  questions  and   developed  a  questionnaire  with  30  questions.     —  The  questionnaire  was  distributed  to  97  open  groups  in   Mendeley,  one  of  the  most  popular  ASNSs.  
  • 72.
    The  instrument-­‐I   — Basic  Information     —  The  Extent  of  Use:  Questions  that  aim  to  determine  the   extent  of  participants’  account  activities  on  Mendeley   —  Common  Ways  to  Use:     —  as  a  document  management  tool,     —  a  reference  manager,     —  a  scholarly  search  engine,     —  an  online  portfolio,     —  a  friend  management  tool,  and     —  a  socialization  tool  
  • 73.
    The  instrument-­‐II   — The  Extent  of  Group  Use   —  Motivation:   —  Information     —  Networking   —  Visibility   —  Altruistic  
  • 74.
    Result:  Overview   — We  received  188  responses  via  the  questionnaire,  but   only  146  users  completed  the  entire  questionnaire.     —  The  average  age  of  the  participants  was  35.04  years   (SD=10.81),  and  64%  of  them  were  male  (N=94).   —  We  obtained  responses  from  users  in  20  disciplines  in   Mendeley.     —  The  top  three  disciplines  represented  were  computer   and  information  science  (N=43),  biological  science   (N=24),  and  social  science  (N=17).    
  • 75.
    The  Par-cipants   — Distribution  of  academic  disciplines   —  Early  adaptor:  Biomedicine  users     —  Newcomers:  Social  Sciences   —  Lack  of  humanities,  literature,  philosophy,  and  design  users   — Distribution  of  academic  positions  
  • 76.
    Frequency  of  Account  Ac-vi-es     ASNS  users  are  not  as  active  as  general  SNS  users:   —  53%  of  respondents  visited  their  accounts  on  a  weekly   basis,  while  36%  of  them  accessed  the  site  at  least  once   per  month.   —  Also  more  than  half  (53%)  of  the  participants  reported   that  they  were  checking  the  news  feeds  only  on  a   monthly  basis.  
  • 77.
    Ways  to  Use  Mendeley   —  Participants  primarily  used  Mendeley  as     —  a  document  management  tool   —  citation  management  software   —  The  portion  of  those  using  Mendeley  as  a  social   networking  site  was  relatively  low:  Only  11%  of   respondents  used  Mendeley  to  manage  their  existing   academic  friends  and  to  expand  their  professional   networks.     —  These  results  indicate  that  most  of  our  participants   use  Mendeley  for  its  research  features,  rather  than   social  features.    
  • 78.
    Mo-va-ons  for  Joining  Groups   Mo-va-on   Motivation  Items     M   Information Keep  up  with  a  user’s  research  domain 4.30 Get  research-­‐related  questions  answered 3.41 Follow  topics  that  community  is  paying  attention   to 3.98 Networking Connect  with  people  who  have  similar   research  interests   3.91 Expand  current  social  network 3.23 Meet  more  academic  people 3.27 Keep  in  touch  with  people  one  already  knows 3.08 Visibility Gain  professional  visibility 3.48 Be  present  in  current  discussions 3.27 Altruistic   Contribute  to  the  reading  list   3.62 Table: Users’ motivation in terms of joining a Mendeley group created by others.
  • 79.
    Factors  that  may  influence  on  the  outcome  of   group  joining  mo-ves   —  Results  suggest  that  people  are  most  motivated  by   visibility  and  altruism  when  considering  whether  to   join  or  follow  more  groups.   —  Even  if  users  frequently  and  regularly  engaged  in   research-­‐based  activities  on  Mendeley  (such  as  a   document  management  or  citation  management  tool)  ,   it  would  not  make  any  difference  in  terms  of  their   intentions  of  joining  groups.  
  • 80.
    Insights  Based  on  the  Findings   — What  is  Mendeley  exactly?     — Discipline  Distribution  and  Development  on   Mendeley   — Academic  Networking  or  Social  Networking?   — The  incentives  of  group  joining  
  • 81.
    Mendeley:  A  Plaorm  for  Higher   Educa-on  Users   —  Our  findings  confirmed  that  the  majority  of  Mendeley   users  were  from  the  higher  education  environment.   More  specifically,  junior  researchers  (i.e.,  doctoral   students,  post-­‐doctoral  fellows,  and  graduate  students).     —  For  those  researchers  who  would  like  to  study  junior   scholars’  information  behaviors  or  run  a  survey  on  a   wide  range  of  online  scholars,  we  believe  that  an  ASNS   such  as  Mendeley  are  the  right  platforms  to  use  to   reach  those  types  of  participants.    
  • 82.
    Discipline  Distribu-on  and   Development  on  Mendeley   —  Our  results  show  that  the  discipline  development  in   Mendeley  is  uneven.     —  Early  users  in  Mendeley  groups  mostly  came  from  the   fields  of  computer  &  information  science  and   biomedicine,  whereas  more  recent  users  are  mostly   from  the  fields  of  social  science,  education  and   psychology.     —  We  do  not  see  many  group  users  from  the  humanities   and  other  related  fields.    
  • 83.
    Academic  Networking  vs.  Social   Networking   —  “Academic”  but  not  “Social”?   —  Users  of  Mendeley  seem  to  mainly  concentrate  on  the   utilities  directly  related  to  their  research  work,  while   mostly  ignoring  its  social  features,  such  as  “friend   making”.   —  Warning  for  ASNS  developers:  Do  not  simply  adopt   “Facebook-­‐like”  or  “LinkedIn-­‐like”  social  elements   when  designing  an  ASNS  platform  for  academic  users.  
  • 84.
    Join  a  Group?  Show  Me  the  Incen-ve.   —  A  gap  between  motivation  and  incentive:  The  altruistic   motivation  was  one  of  the  most  critical  reasons   associated  with  their  group  engagement,  yet  none  of   current  features  of  Mendeley  reward  scholars  for  their   altruistic  activities.   —  Possible  incentive  mechanisms  to  encourage   interactions  :   —  providing  of  affective  feedback  by  group  owners  or  members,  to  users   who  contributed   —  establishing  level-­‐based  honor  system  or  badging  system  
  • 85.
    Limita-ons   —  We  sampled  only  open  and  large  groups  on  Mendeley   instead  of  a  random  sample.     —  The  discipline  and  position  distribution  of  the   participants  may  be  biased  towards  users  who  are  the   use  group  of  social  feature  and  highly  engaged  in  group   activities.   —  If  researchers  would  like  to  investigate  the  wider   landscape  of  ASNS  users,  larger-­‐scaled  and  random   sampling  approaches  are  needed.  
  • 86.
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
    Challenges   —  Know  the  boundary  of   Social  Information   Access   —  How  to  identify  which  tasks   are  good  for  social   information  access?   —  How  to  effectively  integrate   social  networking,  direct   messaging,  and  social   recommendations  with   current  search  facilities.  
  • 92.
    Related  Publica-ons   — Z.  Yue,  S.  Han,  D.  He.  J.  Jiang.  Influences  on  Query  Reformulation   in  Collaborative  Web  Search.  IEEE  Compute,  2014  (3):46-­‐53.   —  Z.  Yue,  S.  Han,  D.  He.  Modeling  Search  Processes  using  Hidden   States  in  Collaborative  Exploratory  Web  Search.  The  17th  ACM   Conference  on  Computer  Supported  Cooperative  Work  and  Social   Computing  (CSCW  2014).   —  Z.  Yue,  S.  Han,  D.  He.  An  Investigation  on  the  Query  Behavior  in   Task-­‐based  Collaborative  Exploratory  Web  Search.  The  76th   Annual  Meeting  of  the  Association  for  Information  Science  and   Technology.  (ASIST  2013).   —  Jeng,  W.,  He,  D.,  &  Jiang,  J.  (In  press).  User  Participation  in  an   Academic  Social  Networking  Service:  A  Survey  of  Open  Group   Users  on  Mendeley.  Journal  of  the  Association  for  Information   Science  and  Technology.   92
  • 93.
  • 94.
    Acknowledgement     — The  work  presented  here  were  conducted  by  faculty  and   students  in  Information  Retrieval,  Integration  and   Synthesis  Lab  at  School  of  Information  Sciences   —  Other  people  participated  in  these  works  are   —  Prof.  Peter  Brusilovsky,  Prof  Dan  Wu  etc.   —  These  work  are  partially  supported  by  the  National   Science  Foundation