ABSTRACT
Online social media represent a fundamental shift of how information is being produced, transferred and consumed. User generated content in the form of blog posts, comments, and tweets establishes a connection between the producers and the consumers of information. Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better. For consumers, the abundance of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions.
The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization.
- L. A. Adamic and N. Glance. The political blogosphere and the 2004 u.s. election: divided they blog. In LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery, pages 36--43, 2005. Google ScholarDigital Library
- L. A. Adamic, J. Zhang, E. Bakshy, and M. S. Ackerman. Knowledge sharing and yahoo answers: everyone knows something. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 665--674, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- E. Adar and L. A. Adamic. Tracking information epidemics in blogspace. In Web Intelligence, pages 207--214, 2005. Google ScholarDigital Library
- E. Adar, L. Zhang, L. A. Adamic, and R. M. Lukose. Implicit structure and the dynamics of blogspace. In Workshop on the Weblogging Ecosystem, 2004.Google Scholar
- E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. Finding high quality content in social media, with an application to community-based question answering. In WSDM '08: ACM International Conference on Web Search and Data Minig, pages 183--194, 2008. Google ScholarDigital Library
- M. De Choudhury, Y.-R. Lin, H. Sundaram, K. S. Candan, L. Xie, and A. Kelliher. How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? In ICWSM '10: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, 2010.Google Scholar
- D. Fisher, M. Smith, and H. T. Welser. You Are Who You Talk To: Detecting Roles in Usenet Newsgroups. In HICSS '06: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, volume 3, page 59b, 2006. Google ScholarDigital Library
- E. Gilbert and K. Karahalios. Predicting tie strength with social media. In CHI '09: Proceedings of the 27th international conference on Human factors in computing systems, pages 211--220, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- N. Glance, M. Hurst, K. Nigam, M. Siegler, R. Stockton, and T. Tomokiyo. Deriving marketing intelligence from online discussion. In KDD '05: Proceeding of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pages 419--428, 2005. Google ScholarDigital Library
- M. Goetz, J. Leskovec, M. Mcglohon, and C. Faloutsos. Modeling blog dynamics. In International Conference on Weblogs and Social Media, May 2009.Google Scholar
- M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010. Google ScholarDigital Library
- D. Gruhl, R. Guha, R. Kumar, J. Novak, and A. Tomkins. The predictive power of online chatter. In KDD '05: Proceeding of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pages 78--87, 2005. Google ScholarDigital Library
- D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In WWW '04: Proceedings of the 13th international conference on World Wide Web, pages 491--501, 2004. Google ScholarDigital Library
- R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In WWW '04: Proceedings of the 13th international conference on World Wide Web, pages 403--412, 2004. Google ScholarDigital Library
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65. ACM, 2007. Google ScholarDigital Library
- D. Kempe, J. M. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03: Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137--146, 2003. Google ScholarDigital Library
- R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. In WWW '02: Proceedings of the 11th international conference on World Wide Web, pages 568--576, 2003. Google ScholarDigital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a Social Network or a News Media? In WWW'10: Proceedings of the 19th International World Wide Web Conference, April 2010. Google ScholarDigital Library
- J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 497--506, 2009. Google ScholarDigital Library
- J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In WWW '10: Proceedings of the 19th International Conference on World Wide Web, 2010. Google ScholarDigital Library
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In KDD '07: Proceeding of the 13th ACM SIGKDD international conference on Knowledge discovery in data mining, 2007. Google ScholarDigital Library
- J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs. In SDM '07: Proceedings of the SIAM Conference on Data Mining, 2007.Google ScholarCross Ref
- J. Leskovec, A. Singh, and J. M. Kleinberg. Patterns of influence in a recommendation network. In PAKDD '06: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 380--389, 2006. Google ScholarDigital Library
- S. Myers and J. Leskovec. On the convexity of latent social network inference. In NIPS '10: Advances in Neural Information Processing Systems, 2010.Google Scholar
- S. Sadikov, M. Medina, J. Leskovec, and H. Garcia-Molina. Correcting for missing data in information cascades. In WSDM '11: ACM International Conference on Web Search and Data Minig, 2011. Google ScholarDigital Library
- J. Yang and Leskovec. Modeling information diffusion in implicit networks. In ICDM '10: IEEE International Conference On Data Mining, 2010. Google ScholarDigital Library
- J. Yang and Leskovec. Patterns of temporal variation in online media. In WSDM '11: ACM International Conference on Web Search and Data Minig, 2011. Google ScholarDigital Library
Index Terms
Social media analytics: tracking, modeling and predicting the flow of information through networks
Recommendations
What is Twitter, a social network or a news media?
WWW '10: Proceedings of the 19th international conference on World wide webTwitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal ...
Information diffusion and external influence in networks
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data miningSocial networks play a fundamental role in the diffusion of information. However, there are two different ways of how information reaches a person in a network. Information reaches us through connections in our social networks, as well as through the ...
The Primer of Social Media Analytics
This article is intended to serve as a primer of social media analytics. The paper explores different dimensions of social media analytics by drawing on a review of the literature. Specifically, the paper sheds light on the definitional aspects, types ...
Comments