Modeling Adoption and Usage of Competing Products

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use

Submodular Inference of Diffusion Networks from Multiple Trees

Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.Comment: To appear in the 29th International Conference on Machine Learning (ICML), 2012. Website: http://www.stanford.edu/~manuelgr/network-inference-multitree

Structure and Dynamics of Information Pathways in Online Media

Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network. We develop an on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. Information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major social movements and events involving civil population, such as the Libyan's civil war or Syria's uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.Comment: To Appear at the 6th International Conference on Web Search and Data Mining (WSDM '13

Quantifying Information Overload in Social Media and its Impact on Social Contagions

Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users' information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.Comment: To appear at ICSWM '1

Uncovering the Temporal Dynamics of Diffusion Networks

Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data.Comment: To appear in the 28th International Conference on Machine Learning (ICML), 2011. Website: http://www.stanford.edu/~manuelgr/netrate

Modeling Information Propagation with Survival Theory

Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.Comment: To appear at ICML '1

Productivity and Quality-Environmental Changes in Marketing Co-operatives: An Analysis on the Horticultural Sector

The object of the present paper is to analyse productivity incorporating quality-environmental changes in marketing co-operatives. Firstly, it reviews competitiveness factors in the current European agri-food market, especially in relation to the fruit and vegetables sector. Secondly, the productivity trend is studied empirically using nonparametric methods (Malmquist indices) and taking as reference panel data of Andalusian horticultural co-operatives for the period 1994-2001. For this purpose productivity is decomposed into technological change, efficiency and quality-environmental change. Additionally, the correlation of these results with other economic variables is analysed. The indicators obtained show a relevant increase in efficiency for the period under study and a high relationship between the results and product quality-environmental improvement.productivity, quality-environment, efficiency, marketing co-operative, horticultural sector, Agribusiness, Productivity Analysis, D24, Q13, Q21, L15,

Productivity and Environmental Performance in Marketing Cooperatives: Incentive Schemes on the Horticultural Sector

The object of the present paper is to analyze the productivity of marketing cooperatives incorporating environmental inputs/outputs. In the European agricultural policy, expectations for attaining sustainable and competitive agriculture lie to a great extent on the cooperative sector's ability to adapt to the new market conditions. These challenges have led marketing cooperatives in the fruit and vegetables sector to consider improvement in productivity and sound environmental performance. In this sector environmental management was intensified by the Common Agrarian Policy (CAP) through incentives on the so-called Operative Programs (OP). The present study analyses the total factor productivity (TFP) related to environmental variables in this sector using a parametric-stochastic approach and taking as reference a panel data of Spanish cooperatives for the period 1994-2002. Additionally, the determinants of productivity environmental indices are examined econ ometrically. The estimates obtained show a relevant increase in the efficiency component for the period under study and a relatively low impact of incentive schemes. However, they also show a relationship between productivity changes and several management factors in cooperatives, such as labor quality, capital intensity and environmental spillover.Productivity, environmental performance, parametric approach, efficiency, marketing cooperative, horticultural sector, Agribusiness, Environmental Economics and Policy, Productivity Analysis, D24, Q13, Q21, L15,

Environmental and Quality Improvement Practices: Their Analysis as Components of the Value Added in Horticultural Firms

This paper analyses the effect of environmental and quality improvement practices on the value added of the fruit and vegetable sector. These practices form part of the incentive-based programmes established by the Common Agricultural Policy. Taking the investment in quality-environmental activities as knowledge capital, we propose a specific analysis that evaluates the effect of the factors of the production function and of the current subsidies over the value added. In general, the share of quality environmental activities in the rise of the product's market value is quite high. The analysis reflects that the expenditure on these activities is still higher than their benefit, and that the current subsidies can hardly be considered encouraging factors for the development of the above-mentioned practices.Quality-environmental practices, investment incentives, horticultural firms, value added, Environmental Economics and Policy,