Exploratory analysis of pairwise interactions in online social networks

ABSTRACT In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where “friends” on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users’ interaction via different ways of OSN interaction. In this paper, we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight into OSN interaction for future researches aimed at this field of study.

[1]  Gábor Benedek,et al.  The Importance of Social Embeddedness: Churn Models at Mobile Providers , 2014, Decis. Sci..

[2]  Brian V. Carolan.  Social Network Analysis and Education: Theory, Methods & Applications , 2013 .

[3]  Lei Shi,et al.  Social Network Analysis in Enterprise , 2012, Proceedings of the IEEE.

[4]  Luka Humski,et al.  Applying the binary classification methods for discovering the best friends on an online social network , 2017, 2017 14th International Conference on Telecommunications (ConTEL).

[5]  Nadeem Akhtar,et al.  Analysis of Facebook Social Network , 2013, 2013 5th International Conference on Computational Intelligence and Communication Networks.

[6]  Darko Striga,et al.  How to calculate trust between social network users? , 2012, SoftCOM 2012, 20th International Conference on Software, Telecommunications and Computer Networks.

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

[8]  Veronica D Feeg,et al.  British paediatric and American pediatric nurses: how well do we know each other? , 2002, Journal for specialists in pediatric nursing : JSPN.

[9]  Hüseyin Uzunalioglu,et al.  Prediction of subscriber churn using social network analysis , 2013, Bell Labs Technical Journal.

[10]  Sebastián Ventura,et al.  Data mining in education , 2013, WIREs Data Mining Knowl. Discov..

[11]  Andrea Biancini.  Social Psychology Testing Platform Leveraging Facebook and SNA Techniques , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[12]  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.

[13]  Xiaodong Lin,et al.  Itrust: interpersonal trust measurements from social interactions , 2016, IEEE Network.

[14]  Osmar R. Zaïane,et al.  Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques , 2011, EDM.

[15]  Robin I. M. Dunbar.  Neocortex size as a constraint on group size in primates , 1992 .

[16]  Pasquale De Meo,et al.  Analyzing the Facebook Friendship Graph , 2010, ArXiv.

[17]  Sougata Mukherjea,et al.  Social ties and their relevance to churn in mobile telecom networks , 2008, EDBT '08.

[18]  Luka Humski,et al.  Building implicit corporate social networks: The case of a multinational company , 2013, Proceedings of the 12th International Conference on Telecommunications.

[19]  Mamadou Diaby,et al.  Toward the next generation of recruitment tools: An online social network-based job recommender system , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[20]  Honggang Zhang,et al.  Social interaction based video recommendation: Recommending YouTube videos to facebook users , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[21]  Kate Ehrlich,et al.  SmallBlue: People Mining for Expertise Search , 2008, IEEE MultiMedia.

[22]  Luka Humski,et al.  Proof of Concept for Comparison and Classification of Online Social Network Friends Based on Tie Strength Calculation Model , 2016 .

[23]  Michael Harris Bond,et al.  Understanding social psychology across cultures: Living and working in a changing world. , 2006 .

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

[25]  Luka Humski,et al.  Applying the multiclass classification methods for the classification of online social network friends , 2017, 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[26]  Salvatore Catanese,et al.  Crawling Facebook for social network analysis purposes , 2011, WIMS '11.

[27]  Luka Humski,et al.  Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction , 2018, ArXiv.

[28]  Luka Humski,et al.  Using the interaction on social networks to predict real life friendship , 2014, 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM).