Twitter knows: Understanding the emergence of topics in social networks

Social networks such as Twitter and Facebook are important and widely used communication environments that exhibit scale, complexity, node interaction, and emergent behavior. In this paper, we analyze emergent behavior in Twitter and propose a definition of emergent behavior focused on the pervasiveness of a topic within a community. We extend an existing stochastic model for user behavior, focusing on advocate-follower relationships. The new user posting model includes retweets, replies, and mentions as user responses. To capture emergence, we propose a RPBS (Rising, Plateau, Burst and Stabilization) topic pervasiveness model with a new metric that captures how frequent and in what form the community is talking about a particular topic. Our initial validation compares our model with four Twitter datasets. Our extensive experimental analysis allows us to explore several “what-if” scenarios with respect to topic and knowledge sharing, showing how a pervasive topic evolves given various popularity scenarios.

[1]  Yong Meng Teo,et al.  Post-mortem analysis of emergent behavior in complex simulation models , 2013, SIGSIM PADS '13.

[2]  Yong Meng Teo,et al.  Semantic Validation of Emergent Properties in Component-Based Simulation Models , 2013, Ontology, Epistemology, and Teleology for Modeling and Simulation.

[3]  F. Al-Shamali,et al.  Author Biographies. , 2015, Journal of social work in disability & rehabilitation.

[4]  M. Bedau Weak Emergence * , 1997 .

[5]  Susan T. Dumais,et al.  Characterizing Microblogs with Topic Models , 2010, ICWSM.

[6]  Laurent Magnin,et al.  Elements about the Emergence Issue: A Survey of Emergence Definitions , 2006, Complexus.

[7]  Peter Mika,et al.  Ontologies are us: A unified model of social networks and semantics , 2005, J. Web Semant..

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

[9]  Zhengping Li,et al.  A Survey of Emergent Behavior and Its Impacts in Agent-based Systems , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[10]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[11]  David A. Shamma,et al.  Peaks and persistence: modeling the shape of microblog conversations , 2011, CSCW '11.

[12]  Vince Darley Emergent Phenomena and Complexity , 2004 .

[13]  Christopher D. Clack,et al.  Specifying, detecting and analysing emergent behaviours in multi-level agent-based simulations , 2007, SCSC.

[14]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[15]  Ales Kubík,et al.  Toward a Formalization of Emergence , 2002, Artif. Life.

[16]  Esteban Moro,et al.  Impact of human activity patterns on the dynamics of information diffusion. , 2009, Physical review letters.

[17]  Kristina Lerman,et al.  Social Information Processing in News Aggregation , 2007, IEEE Internet Computing.

[18]  Andrew McAfee,et al.  Enterprise 2.0: the dawn of emergent collaboration , 2006, IEEE Engineering Management Review.

[19]  John H. Holland,et al.  Emergence. , 1997, Philosophica.

[20]  H. Van Dyke Parunak,et al.  Multi-Agent-Based Simulation XIV , 2013, Lecture Notes in Computer Science.

[21]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[22]  Michael Luck,et al.  Verification & Validation of Agent-Based Simulations using Approximate Model Checking , 2013 .

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

[24]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[25]  B. Roy Paradigms and Challenges , 2005 .

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

[27]  Kristina Lerman,et al.  Social Information Processing in Social News Aggregation , 2007, ArXiv.

[28]  Yong Meng Teo,et al.  An integrated approach for the validation of emergence in component-based simulation models , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[29]  Mario Cataldi,et al.  Emerging topic detection on Twitter based on temporal and social terms evaluation , 2010, MDMKDD '10.

[30]  Tad Hogg,et al.  Stochastic Models of User-Contributory Web Sites , 2009, ICWSM.

[31]  Van Jacobson,et al.  The synchronization of periodic routing messages , 1993, SIGCOMM '93.

[32]  Meeyoung Cha,et al.  Modeling the Adoption of Innovations in the Presence of Geographic and Media Influences , 2011, PloS one.

[33]  Alexander Richter,et al.  Oh, SNEP! The Dynamics of Social Network Emergence-the case of Capgemini Yammer , 2012 .

[34]  Krishna P. Gummadi,et al.  The Emergence of Conventions in Online Social Networks , 2012, ICWSM.

[35]  Wai Kin Chan,et al.  Transplanting Social Capital to the Online World: Insights from Two Experimental Studies , 2009, J. Organ. Comput. Electron. Commer..

[36]  Toshiharu Suagwara Emergence of conventions in conflict situations in complex agent network environments , 2014, AAMAS.

[37]  Christopher W. Johnson,et al.  What are emergent properties and how do they affect the engineering of complex systems? , 2006, Reliab. Eng. Syst. Saf..

[38]  Qing Yang,et al.  Trend Analysis of News Topics on Twitter , 2012 .

[39]  Ramasamy Uthurusamy,et al.  EVOLVING DATA MINING INTO SOLUTIONS FOR INSIGHTS , 2002 .

[40]  Jeffrey C. Mogul,et al.  Emergent (mis)behavior vs. complex software systems , 2006, EuroSys.

[41]  Paul K. Davis New paradigms and new challenges [modeling and simulation] , 2005, Proceedings of the Winter Simulation Conference, 2005..

[42]  Ana Paula Appel,et al.  Large-Scale Multi-agent-Based Modeling and Simulation of Microblogging-Based Online Social Network , 2013, MABS.

[43]  Ramasamy Uthurusamy,et al.  Evolving data into mining solutions for insights , 2002, CACM.

[44]  Sergio A. Velastin,et al.  Crowd analysis: a survey , 2008, Machine Vision and Applications.

[45]  Tad Hogg,et al.  Stochastic Models Predict User Behavior in Social Media , 2013, ArXiv.

[46]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.