computational methods for intelligent matchmaking for knowledge work
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COMPUTATIONAL METHODS FOR INTELLIGENT MATCHMAKING FOR KNOWLEDGE WORK – CASE CMAD
Jayesh Prakash Gupta, Tampere University of Technology, FinlandJari Jussila, Tampere University of Technology, Finland
Ekaterina Olshannikova, Tampere University of Technology, FinlandKaran Menon, Tampere University of Technology, Finland
Jukka Huhtamäki, Tampere University of Technology, FinlandThomas Olsson, Tampere University of Technology, FinlandProf. Ravi Vatrapu, Copenhagen Business School, Denmark
Prof. Hannu Kärkkäinen, Tampere University of Technology, Finland
ReferencesGranovetter, Mark S. "The strength of weak ties." American journal of sociology 78.6 (1973): 1360-1380.Marsden, Peter V., and Karen E. Campbell. "Measuring tie strength." Soc. F. 63 (1984): 482.Gilbert, Eric, and Karrie Karahalios. "Predicting tie strength with social media." Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 2009.Gupta, Jayesh Prakash, et al. "Identifying weak ties from publicly available social media data in an event." Proceedings of the 20th International Academic Mindtrek Conference. ACM, 2016.
Availability of Big Social DATA
Results• It is possible to identify potential weak ties from using the publically available social
media data• Facebook data was not useful in identifying weak ties• Twitter was helpful in identifying the potential weak ties.• Temporal pattern of different social media channels use was different for Twitter
and FacebookLimitations:• Insufficient number of survey respondents to draw any statistically significant result• Only some of the possible methods for weak tie identification were used
Future Research• Combined other kind of data sources and methods for weak tie identification• Move towards utilizing Big Social Data• Utilize new analytics methods like Social Set Analysis• Validate the results using large number of survey respondents• The next steps:
Please signup to be part of the future studies by providing your details on thefollowing link or by scanning the QR codehttp://bit.ly/2jGOw64
Part of the COBWEB research projectFunded by Academy of Finland (http://www.aka.fi).
Presented Jan 23, 2017 at CMAD2017, University of Tampere, Tampere.
Match Making in Professional Life Some Current Match Making Apps
grip.events shapr.net
Case CMAD 2016 Results
Interaction networks
Possible clusters based on Twitter Data
Modularity class Cluster name
1 Personal branding
2 Employee advocacy
3 Drawings and infographics
5 **Broadly about cmadfi event
6 Community manager
7 Communications
10 *Reporting on CMADFI event
15 Customer service
16 Project
17 *Outsider greetings
18 Tekes
20 Knowledge management
21 Jyväskylän energia
Why is tie strength (weak and strong ties)important? Why social media / BSDanalytics?Examples of matchmaking- related needs (which are rarely made use of e.g.in current matchmaking tools):
Efficient spreading of information/research results/etc. to as large group ofrelevant people as possible (strong ties, authoritative/influencing persons)
Identify new viewpoints and knowledge to problems or for new innovations /new interdisciplinary research (weak ties; both similar and new knowledge)
Find relevant persons in professional events and conferences (find personswith novel / complementary expertise / persons with influence)
Find interesting collaborators to a research project
Different Kind of Ties
• Strong ties: Strong tiesare the ones that aperson really trust. Foreg. family
• Weak ties: Refers toweak links which may beknown.Eg.acquaintances, friend offriend
• Weak ties are veryuseful in spreading novelinformation.
New Possibilities with Big Social DATASocial Media Data , BSD and SNA (SocialNetwork Analysis) in tie strengthevaluation and matchmaking?
Social Media data for example FacebookFriends, Twitter Follower/ Followee, havebeen used to identify the actual socialnetwork and tie strength of a person.
Additionally, it is possible to useconversation data on social media toevaluate tie strength and help inidentifying different kind of ties. SNA isone possible way to do this.
SNA enables in easier identification ofdifferent influential people and thepotential sub- communities in a network.
BSD can e.g. enable using the historicalconversation / other social data aboutconference/s to provide preliminarydeductions about participants tie strengthand the possible useful ties, their commonor complimentary interests. For exampleco-occurrence of conference participants.