Suman Kalyan Maity

Emergence of fast agreement in an overhearing population: The case of the naming game

The naming game (NG) describes the agreement dynamics of a population of N agents interacting locally in pairs leading to the emergence of a shared vocabulary. This model has its relevance in the novel fields of semiotic dynamics and specifically to opinion formation and language evolution. The application of this model ranges from wireless sensor networks as spreading algorithms, leader election algorithms to user-based social tagging systems. In this paper, we introduce the concept of overhearing (i.e., at every time step of the game, a random set of N? individuals are chosen from the population who overhear the transmitted word from the speaker and accordingly reshape their inventories). When ??=?0 one recovers the behavior of the original NG. As one increases ?, the population of agents reaches a faster agreement with a significantly low-memory requirement. The convergence time to reach global consensus scales as logN as ? approaches 1.

An automatic approach to identify word sense changes in text media across timescales

In this paper, we propose an unsupervised and automated method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books and millions of tweets posted per day. We construct distributional-thesauribased networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we propose a split/join based approach to compare the sense clusters at two different time points to find if there is ‘birth’ of a new sense. The approach also helps us to find if an older sense was ‘split’ into more than one sense or a newer sense has been formed from the ‘join’ of older senses or a particular sense has undergone ‘death’. We use this completely unsupervised approach (a) within the Google books data to identify word sense differences within a media, and (b) across Google books and Twitter data to identify differences in word sense distribution across different media. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet.

Detection of Sockpuppets in Social Media

Online deception is a prevalent phenomena in social media. Creation of sockpuppets are one of the ways of online deception. The detection of such accounts are very important and crucial. We study various tweets and profile based features and propose an automated framework to early detect sockpuppet accounts in Twitter. We obtain high accuracy of 90.98% and a high recall of 0.88 in detecting the sockpuppet accounts.

Customization of regional climate model (RegCM4) over Indian region

The regional climate model (RegCM4) is customized for 10-year climate simulation over Indian region through sensitivity studies on cumulus convection and land surface parameterization schemes. The model is configured over 30° E–120° E and 15° S–45° N at 30-km horizontal resolution with 23 vertical levels. Six 10-year (1991–2000) simulations are conducted with the combinations of two land surface schemes (BATS, CLM3.5) and three cumulus convection schemes (Kuo, Grell, MIT). The simulated annual and seasonal climatology of surface temperature and precipitation are compared with CRU observations. The interannual variability of these two parameters is also analyzed. The results indicate that the model simulated climatology is sensitive to the convection as well as land surface parameterization. The analysis of surface temperature (precipitation) climatology indicates that the model with CLM produces warmer (dryer) climatology, particularly over India. The warmer (dryer) climatology is due to the higher sensible heat flux (lower evapotranspiration) in CLM. The model with MIT convection scheme simulated wetter and warmer climatology (higher precipitation and temperature) with smaller Bowen ratio over southern India compared to that with the Grell and Kuo schemes. This indicates that a land surface scheme produces warmer but drier climatology with sensible heating contributing to warming where as a convection scheme warmer but wetter climatology with latent heat contributing to warming. The climatology of surface temperature over India is better simulated by the model with BATS land surface model in combination with MIT convection scheme while the precipitation climatology is better simulated with BATS land surface model in combination with Grell convection scheme. Overall, the modeling system with the combination of Grell convection and BATS land surface scheme provides better climate simulation over the Indian region.

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.

Performance of cumulus parameterization schemes in the simulation of Indian Summer Monsoon using RegCM4

The Indian Summer Monsoon (ISM) is driven by organized large-scale convection; hence, its simulation is expected to depend on an appropriate representation of cumulus convection in the model. In the present study, the performance of different cumulus parameterization schemes is examined towards simulations of the ISM. The Regional Climate Model (RegCM4) is coupled with the Community Land Model (CLM 3.5) at 30 km resolution for the period May 1-September 30 for seasonal simulation of the ISM in three consecutive years, 2007, 2008, and 2009. Five numerical experiments with five convection schemes (Kuo, Grell, MIT, GO_ML [Grell over ocean and MIT over land], GL_MO [Grell over land and MIT over ocean]) are conducted for each of these three years. Some important features of the ISM simulated by the model, viz. low level westerly jet, upper level easterly jet, heat low, Tibetan high, etc., are analyzed and compared with that of the National Center for Environmental Prediction (NCEP) reanalysis. We found that the heat low over northwest India and Pakistan in all the three years is better simulated by the model with the MIT convection scheme compared to other convection schemes, whereas spatial distribution and accuracy of surface temperature is better simulated using GL_MO rather than MIT. The low level westerly jet is well captured by the model with MIT with slightly weaker strength compared to the National Center for Environmental Prediction (NCEP) reanalysis. The location and strength of the tropical easterly jet is well predicted in each simulation with some uncertainty in strength, and are better simulated with MIT. The comparison of the model simulated rainfall with 0.5o × 0.5o datasets from the Climate Research Unit (CRU TS3.22) indicates that seasonal and monthly average rainfall are well simulated with MIT and GO_ML; however, the same over central and western India is significantly underestimated by the model with all the convection schemes. Comparatively, higher sensible heat flux and lower latent heat flux are noticed in the model simulation with all schemes. This change of fluxes affects surface temperature and rainfall simulation significantly. The statistical analysis indicates that surface temperature and rainfall are well reproduced by the model with GL_MO and GO_ML, but circulation is better simulated with MIT only. It is observed that although the bias in the model with MIT is slightly higher than that of the two mixed schemes, the spatial distribution and other synoptic features of surface temperature and rainfall during ISM are well simulated. Thus, considering overall performances, the RegCM4 with MIT the cumulus convection scheme provides better simulation of seasonal and monthly features of the monsoon.

Understanding How Dominance Affects the Emergence of Agreement in a Social Network: The Case of Naming Game

We study the dynamics of the Naming Game as an opinion formation model on social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of dominance of certain opinions over others in pursuit of faster agreement on social networks. We propose two models to incorporate dominance of the opinions. We observe that both these models lead to faster agreement among the agents on an opinion as compared to the base case reported in the literature. We perform extensive simulations on computer-generated networks as well as on a real online social network (Facebook) and in both cases the dominance based models converge significantly faster than the base case.

ENWalk: Learning Network Features for Spam Detection in Twitter

Social medias are increasing their influence with the vast public information leading to their active use for marketing by the companies and organizations. Such marketing promotions are difficult to identify unlike the traditional medias like TV and newspaper. So, it is very much important to identify the promoters in the social media. Although, there are active ongoing researches, existing approaches are far from solving the problem. To identify such imposters, it is very much important to understand their strategies of social circle creation and dynamics of content posting. Are there any specific spammer types? How successful are each types? We analyze these questions in the light of social relationships in Twitter. Our analyses discover two types of spammers and their relationships with the dynamics of content posts. Our results discover novel dynamics of spamming which are intuitive and arguable. We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media. We learn the feature representations using the random walks biased on the spam dynamics. Experimental results on large-scale twitter network and the corresponding tweets show the effectiveness of our approach that outperforms the existing approaches.

A Large-scale Analysis of the Marketplace Characteristics in Fiverr

Crowdsourcing platforms have become quite popular due to the increasing demand of human computation-based tasks. Though the crowdsourcing systems are primarily demand-driven like MTurk, supply-driven marketplaces are becoming increasingly popular. Fiverr is a fast growing supply-driven marketplace where the sellers post micro-tasks (gigs) and users purchase them for prices as low as

Book Reading Behavior on Goodreads Can Predict the Amazon Best Sellers

5. In this paper, we study the Fiverr platform as a unique marketplace and characterize the sellers, buyers and the interactions among them. We find that sellers are more appeasing in their interactions and try to woo their buyers into buying their gigs. There are many small tightly-knit communities existing in the seller-seller network who support each other. We also study Fiverr as a seller-driven marketplace in terms of sales, churn rates, competitiveness among various sub-categories etc. and observe that while there are certain similarities with common marketplaces there are also many differences.