Evaluating User Behaviour in a Cooperative Environment

Abstract

Big Data, as a new paradigm, has forced both researchers and industries to rethink data management techniques which has become inadequate in many contexts. Indeed, we deal everyday with huge amounts of collected data about user suggestions and searches. These data require new advanced analysis strategies to be devised in order to profitably leverage this information. Moreover, due to the heterogeneous and fast changing nature of these data, we need to leverage new data storage and management tools to effectively store them. In this paper, we analyze the effect of user searches and suggestions and try to understand how much they influence a user’s social environment. This task is crucial to perform efficient identification of the users that are able to spread their influence across the network. Gathering information about user preferences is a key activity in several scenarios like tourism promotion, personalized marketing, and entertainment suggestions. We show the application of our approach for a huge research project named D-ALL that stands for Data Alliance. In fact, we tried to assess the reaction of users in a competitive environment when they were invited to judge each other. Our results show that the users tend to conform to each other when no tangible rewards are provided while they try to reduce other users’ ratings when it affects getting a tangible prize.

A survey of Big Data dimensions vs Social Networks analysis

“…Moreover, social network analysis can be classified by considering the level where the analysis take place, i.e., we could be interested in micro analysis that can be done with a subset of the social data of interest. By this kind of analysis, we can obtain recommendations based on user preferences, or if the target are small communities found in a large social network (Barbieri et al 2017 ), how those communities trust each other (Costa et al 2014 ), how people in each community interact (Cassavia et al 2018 ) and become influencers for their peers (Bazzi et al 2018 ) and eventual user non-progressive features. Finally, an increasing effort has been devoted to Fake news approaches (Bondielli and Marcelloni 2019a ).…”

Special Issue on Advanced Learning Methods for Complex Data

“…Finally, the third paper "Evaluating User Behaviour in a Cooperative Environment", by Enrico Bazzi, Nunziato Cassavia, Davide Chiggiato, Elio Masciari, Domenico Saccà, Alessandra Spada, and Irina Trubitsyna [11], addresses the problem of understanding the influence of user behaviours such as searches and suggestions across a social network. Network dynamics are measured using three different metrics, and a number of techniques are used to set up an experimental evaluation that empirically shows how users influence each other in a cooperative environment when a kind of reward is provided.…”