Invited talk by Prof. Niloy Ganguly (IIT KGP): “Opinion and Information Dynamics”
Abstract: Today, social media and online social networks constantly bombard users with informations. In presence of such a heavy information overload, it becomes critical to understand which pieces of information are getting through and which are ignored. The dynamics of these users’ activities in general, is a complex evolving process and encompasses a spectrum of subprocesses. For example, in Twitter, when a user receives a tweet, she may simply re-tweet the message, or modify its content, or gain information and form opinion about a particular topic. However, the dynamics of all these processes can be naturally encapsulated by a potent mathematical devise called ‘temporal point process’, which models the rate of arrival of messages using different functional forms characterizing various phenomenons of interest. In this talk, we would discuss two key dynamical processes in Twitter, opinion dynamics of the users and hashtag propagation.
In this first part, we aim to learn a data-driven model of opinion dynamics that is able to accurately forecast opinions of any user in future. We introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state.
The second part of the talk is more about the complex temporal dynamics that jointly models hashtag reinforcement and hashtag competition. While the existing works have mainly focused on modeling the popularity of individual tweets rather than the popularity of the underlying hashtags, we propose Large Margin Point Process (LMPP), a novel probabilistic framework that integrates hashtag-tweet influence and hashtag-hashtag competitions, the two factors which play important roles in hashtag propagation. Furthermore, while considering the hashtag competitions, LMPP looks into the variations of popularity rankings of the competing hashtags across time.
Biography: Niloy Ganguly is a professor of computer science, IIT Kharagpur whose research interests spans across the related fields of Online Social networks, Network Theory, Mobile Systems. Prior to his current position as a faculty at Indian Institute of Technology, Kharagpur, India, he was working as a Research Scientist in Zentrum für Hochleistungsrechnen, Technische Universität Dresden for two years. He received his Ph.D from IIEST, Shibpur and BTech from IIT Kharagpur.
He is the founder and the head of a very active research group CNeRG (Complex Network Research Group) http://cnerg.org in IIT Kharagpur. He has been collaborating with various national and international universities and research labs including UIUC, TU Dresden, Germany, MPI PKS and MPI SWS, Germany, Microsoft Lab, Adobe Labs, HP Labs, NetApp Labs etc. He currently publishes in various top ranking international journals and conferences including ICDM, EMNLP, ACL, WWW, INFOCOM, SIGIR, SIGKDD, SIGCHI, ICWSM, CSCW, NIPS CCS, PODC, Euro Physics Letters, Physical Review E, ACM and IEEE Transactions, etc.For further information on Dr. Ganguly, please visit his website: http://www.facweb.iitkgp.ernet.in/~niloy/.