Ego network structure in online social networks and its impact on information diffusion

In the last few years, Online Social Networks (OSNs) attracted the interest of a large number of researchers, thanks to their central role in the society. Through the analysis of OSNs, many social phenomena have been studied, such as the viral diffusion of information amongst people. What is still unclear is the relation between micro-level structural properties of OSNs (i.e. the properties of the personal networks of the users, also known as ego networks) and the emergence of such phenomena. A better knowledge of this relation could be essential for the creation of services for the Future Internet, such as highly personalised advertisements fitted on users' needs and characteristics. In this paper, we contribute to bridge this gap by analysing the ego networks of a large sample of Facebook and Twitter users. We show that micro-level structural properties of OSNs are interestingly similar to those found in social networks formed offline. In particular, online ego networks show the same structure found offline, with social contacts arranged in layers with compatible size and composition. From the analysis of Twitter ego networks, we have been able to find a direct impact of tie strength and ego network circles on the diffusion of information in the network. Specifically, there is a high correlation between the frequency of direct contact between users and her friends in Twitter (a proxy for tie strength), and the frequency of retweets made by the users from tweets generated by their friends. We analysed the correlation for each ego network layer identified in Twitter, discovering their role in the diffusion of information.

[1]  Ling Huang,et al.  Evolution of social-attribute networks: measurements, modeling, and implications using google+ , 2012, Internet Measurement Conference.

[2]  Muhammad Abulaish,et al.  A generic statistical approach for spam detection in Online Social Networks , 2013, Comput. Commun..

[3]  Hans-Peter Kriegel,et al.  Density‐based clustering , 2011, WIREs Data Mining Knowl. Discov..

[4]  Fei Wang,et al.  Cascading outbreak prediction in networks: a data-driven approach , 2013, KDD.

[5]  Marco Conti,et al.  Computer communications: Present status and future challenges , 2014, Comput. Commun..

[6]  Thrasyvoulos Spyropoulos,et al.  Understanding the effects of social selfishness on the performance of heterogeneous opportunistic networks , 2014, Comput. Commun..

[7]  Thomas V. Pollet,et al.  Exploring variation in active network size: Constraints and ego characteristics , 2009, Soc. Networks.

[8]  Marco Conti,et al.  Why Do I Retweet It? An Information Propagation Model for Microblogs , 2013, SocInfo.

[9]  Wenzhong Li,et al.  Rethinking routing information in mobile social networks: Location-based or social-based? , 2014, Comput. Commun..

[10]  Muthucumaru Maheswaran,et al.  Using community structure to control information sharing in online social networks , 2014, Comput. Commun..

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

[12]  Yipeng Zhou,et al.  Video Browsing - A Study of User Behavior in Online VoD Services , 2013, 2013 22nd International Conference on Computer Communication and Networks (ICCCN).

[13]  Didier Sornette,et al.  Discrete hierarchical organization of social group sizes , 2004, Proceedings of the Royal Society B: Biological Sciences.

[14]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[15]  R. Burt.  Structural Holes versus Network Closure as Social Capital , 2001 .

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

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Ben Y. Zhao,et al.  Beyond Social Graphs: User Interactions in Online Social Networks and their Implications , 2012, TWEB.

[19]  Alessandro Vespignani,et al.  Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number , 2011, PloS one.

[20]  Marco Conti,et al.  Information diffusion in OSNs: the impact of nodes' sociality , 2014, SAC.

[21]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[22]  Christopher J. Fariss,et al.  Inferring Tie Strength from Online Directed Behavior , 2013, PloS one.

[23]  Franco Zambonelli,et al.  Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber-physical convergence , 2012, Pervasive Mob. Comput..

[24]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[25]  Robin I. M. Dunbar.  Social Brain Hypothesis , 1998, Encyclopedia of Evolutionary Psychological Science.

[26]  Yipeng Zhou,et al.  A study of user behavior in online VoD services , 2014, Comput. Commun..

[27]  S. Roberts.  Constraints on Social Networks , 2010 .

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

[29]  Marco Conti,et al.  Information diffusion in distributed OSN: The impact of trusted relationships , 2015, Peer-to-Peer Networking and Applications.

[30]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[31]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[32]  Haizhou Wang,et al.  Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming , 2011, R J..

[33]  Marco Conti,et al.  The Role of Trusted Relationships on Content Spread in Distributed Online Social Networks , 2014, Euro-Par Workshops.

[34]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[35]  Laura Ricci,et al.  Epidemic Diffusion of Social Updates in Dunbar-Based DOSN , 2014, Euro-Par Workshops.

[36]  Marco Conti,et al.  Dynamics of personal social relationships in online social networks: a study on twitter , 2013, COSN '13.

[37]  Marco Conti,et al.  The structure of online social networks mirrors those in the offline world , 2015, Soc. Networks.

[38]  Daniele Quercia,et al.  The Social World of Twitter: Topics, Geography, and Emotions , 2012, ICWSM.

[39]  Jong Kim,et al.  Early filtering of ephemeral malicious accounts on Twitter , 2014, Comput. Commun..

[40]  Ben Y. Zhao,et al.  Multi-scale dynamics in a massive online social network , 2012, Internet Measurement Conference.

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

[42]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[44]  Víctor M. Eguíluz,et al.  Entangling Mobility and Interactions in Social Media , 2013, PloS one.

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

[46]  Ravi Sundaram,et al.  SamaritanCloud: Secure infrastructure for scalable location-based services , 2015, Comput. Commun..

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

[48]  Marco Conti,et al.  Analysis of Ego Network Structure in Online Social Networks , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[49]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[50]  Marco Conti,et al.  Modelling inter-contact times in social pervasive networks , 2011, MSWiM '11.

[51]  Cecilia Mascolo,et al.  The Length of Bridge Ties: Structural and Geographic Properties of Online Social Interactions , 2012, ICWSM.

[52]  Marco Conti,et al.  Characterising Aggregate Inter-contact Times in Heterogeneous Opportunistic Networks , 2011, Networking.

[53]  Jens F. Binder,et al.  Relationships and the social brain: integrating psychological and evolutionary perspectives. , 2012, British journal of psychology.

[54]  Krishna P. Gummadi,et al.  The World of Connections and Information Flow in Twitter , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[55]  Marco Conti,et al.  Ego networks in Twitter: An experimental analysis , 2013, INFOCOM.

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

[57]  Sushil Jajodia,et al.  Who is tweeting on Twitter: human, bot, or cyborg? , 2010, ACSAC '10.

[58]  Jure Leskovec,et al.  Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network , 2007, WWW 2008.