Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data

Tie strength is an essential concept in identifying different kind of social ties - strong ties and weak ties. Most present studies that evaluated tie strength from social media were carried out in a controlled environment and used private/closed social media data. Even though social media has become a very important way of networking in professional events, access to such private social media data in those events is almost impossible. There is very limited research on how to facilitate networking between event participants and especially on how to automate this networking aspect in events using social media. Tie strength evaluated using social media will be key in automating this process of networking. To create such tie strength based event participant recommendation systems and tools in the future, first, we need to understand how to evaluate tie strength using publicly available social media data. The purpose of this study is to evaluate tie strength from publicly available social media data in the context of a professional event. Our case study environment is community managers’ online discussions in social media (Twitter and Facebook) about the CMAD2016 event in Finland. In this work, we analyzed social media data from that event to evaluate tie strength and compared the social media analysis-based findings with the individuals’ perceptions of the actual tie strengths of the event participants using a questionnaire. We present our findings and conclude with directions for future work.

[1]  Andrea Passarella,et al.  Egocentric online social networks: Analysis of key features and prediction of tie strength in Facebook , 2013, Comput. Commun..

[2]  Tina Eliassi-Rad,et al.  Measuring tie strength in implicit social networks , 2011, WebSci '12.

[3]  Sandra Servia Rodríguez,et al.  A tie strength based model to socially-enhance applications and its enabling implementation: mySocialSphere , 2014, Expert Syst. Appl..

[4]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[5]  Xiaoming Fu,et al.  Triadic Closure Pattern Analysis and Prediction in Social Networks , 2015, IEEE Transactions on Knowledge and Data Engineering.

[6]  Melissa Terras,et al.  Enabled backchannel: conference Twitter use by digital humanists , 2011, J. Documentation.

[7]  Jukka Huhtamäki,et al.  Visualizing informal learning behavior from conference participants' Twitter data with the Ostinato Model , 2016, Comput. Hum. Behav..

[8]  Misha Angrist,et al.  You never call, you never write: why return of 'omic' results to research participants is both a good idea and a moral imperative. , 2011, Personalized medicine.

[9]  Juan A. Recio-García,et al.  Development of a group recommender application in a Social Network , 2014, Knowl. Based Syst..

[10]  Jukka Huhtamäki,et al.  Exploring co-learning behavior of conference participants with visual network analysis of Twitter data , 2015, Comput. Hum. Behav..

[11]  Jose M. Such,et al.  BFF: A tool for eliciting tie strength and user communities in social networking services , 2013, Information Systems Frontiers.

[12]  Eric Gilbert,et al.  Predicting tie strength in a new medium , 2012, CSCW.

[13]  Peter V. Marsden,et al.  Reflections on Conceptualizing and Measuring Tie Strength , 2012 .

[14]  Abid Hussain,et al.  Social Data Analytics Tool: A Demonstrative Case Study of Methodology and Software , 2014 .

[15]  Thrasyvoulos Spyropoulos,et al.  Collection and analysis of multi-dimensional network data for opportunistic networking research , 2012, Comput. Commun..

[16]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[17]  Ji-Hong Park,et al.  The structural effects of sharing function on Twitter networks: Focusing on the retweet function , 2015, J. Inf. Sci..

[18]  Christine Nadel,et al.  Case Study Research Design And Methods , 2016 .

[19]  Hannu Kärkkäinen,et al.  The role of weak ties in enhancing knowledge work , 2017, MindTrek.

[20]  Dylan Walker,et al.  Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment , 2014, Manag. Sci..

[21]  Daniel Z. Levin,et al.  The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer , 2004, Manag. Sci..

[22]  Jennifer Neville,et al.  Using Transactional Information to Predict Link Strength in Online Social Networks , 2009, ICWSM.

[23]  Ravikiran Vatrapu,et al.  Identifying weak ties from publicly available social media data in an event , 2016, MindTrek.

[24]  Andrew B. Whinston,et al.  Content Sharing in a Social Broadcasting Environment: Evidence from Twitter , 2014, MIS Q..

[25]  Jian Yang,et al.  Incorporating Tie Strength in Robust Social Recommendation , 2015, 2015 IEEE International Congress on Big Data.

[26]  Ravikiran Vatrapu,et al.  Social Data Analytics Tool (SODATO) , 2014, DESRIST.

[27]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[28]  Mark F. Hornick,et al.  Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem , 2012, IEEE Transactions on Knowledge and Data Engineering.

[29]  David R. Karger,et al.  Confer: A Conference Recommendation and Meetup Tool , 2016, CSCW Companion.

[30]  J. Whitney Case Study Research , 1999 .

[31]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[32]  Jon M. Kleinberg,et al.  Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook , 2013, CSCW.

[33]  P. V. Marsden,et al.  Measuring Tie Strength , 1984 .

[34]  Michel Walrave,et al.  The Strong, the Weak, and the Unbalanced , 2015 .

[35]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[36]  W. Reinhard,et al.  How people are using Twitter during conferences , 2009 .

[37]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[38]  Dino Pedreschi,et al.  "How Well Do We Know Each Other?" Detecting Tie Strength in Multidimensional Social Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[39]  Tamás Nepusz,et al.  Measuring tie-strength in virtual social networks , 2006 .

[40]  Ana Garcia-Fornes,et al.  Tie and tag: A study of tie strength and tags for photo sharing , 2018, PloS one.