Analysis of Viral Advertisement Re-Posting Activity in Social Media

More and more businesses use social media to advertise their services. Such businesses typically maintain online social network accounts and regularly update their pages with advertisement messages describing new products and promotions. One recent trend in such businesses’ activity is to offer incentives to individual users for re-posting the advertisement messages to their own profiles, thus making it visible to more and more users. A common type of an incentive puts all the re-posting users into a random draw for a valuable gift. Understanding the dynamics of user engagement into the re-posting activity can shed light on social influence mechanisms and help determine the optimal incentive value to achieve a large viral cascade of advertisement. We have collected approximately 1800 advertisement messages from social media site VK.com and all the subsequent reposts of those messages, together with all the immediate friends of the reposting users. In addition to that, approximately 150000 non-advertisement messages with their reposts were collected, amounting to approximately 6.5 M of reposts in total. This paper presents the results of the analysis based on these data. We then discuss the problem of maximizing a repost cascade size under a given budget.

[1]  Jure Leskovec,et al.  Can cascades be predicted? , 2014, WWW.

[2]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[3]  Jiang Li,et al.  The Structure and Evolution of Large Cascades in Online Social Networks , 2015, CSoNet.

[4]  Daniel G. Goldstein,et al.  The structure of online diffusion networks , 2012, EC '12.

[5]  Veronika Karnowski,et al.  News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks , 2015 .

[6]  Jure Leskovec,et al.  Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily , 2015, WWW.

[7]  Michael S. Bernstein,et al.  Quantifying the invisible audience in social networks , 2013, CHI.

[8]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[9]  Lada A. Adamic,et al.  The Anatomy of Large Facebook Cascades , 2013, ICWSM.

[10]  Cécile Favre,et al.  Information diffusion in online social networks: a survey , 2013, SGMD.

[11]  Duncan J. Watts,et al.  The Structural Virality of Online Diffusion , 2015, Manag. Sci..

[12]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[13]  Jari Veijalainen,et al.  A Generic Architecture for a Social Network Monitoring and Analysis System , 2011, 2011 14th International Conference on Network-Based Information Systems.

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

[15]  Eric Sun,et al.  Gesundheit! Modeling Contagion through Facebook News Feed , 2009, ICWSM.