social information access: a personal update
DESCRIPTION
A Presentation given at Nanjing University of Science and Technology. Summarizes the relevant work developed at IRIS lab at School of Information Sciences, University of Pittsburgh.TRANSCRIPT
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 HDàHQàHVàHSàHD,
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
0
2
4
6
8
10
12
14
16
T1 T2
NQ
Task
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
NewGeneralizationSpecificationReconstruction
0.1
0.2
0.3
0.4
0.5
Task 1 Task 2
NewGeneralizationSpecificationReconstruction
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 Pla\orm 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).
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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