Segment-Centric Geo-Competitive Environment of a Tourism Destination (A Case of Georgia)

This work aims to introduce a conceptual approach to determining the competitive environment for a particular tourist destination by considering popular outbound destinations of its leading segments.This approach we name as a concept of a segment-centered geo-competitive environment of a tourism destination(SGE-TD).The applied methodology includes consideration of the popularity of tourist destinations for each selected segment and the indicators of leading segments of the studied destination.The practical application of the proposed concept is examined in the case of Georgia as a tourist destination by selecting its leading segments and identifying their popular travel destinations.The integrated consideration and application of the mentioned indicators define the competitive position of a destination(in this case Georgia)among the specified tourism destinations,considered as the geo-competitive environment.This research suggests an innovative version of the universal conceptual approach to identify the leading competing destinations for a specific studied one.It fills the gap in similar studies where competing destinations for the analysis are selected based on specific research objectives,missing the universal conceptual approach in this regard.

Creating Optimal Language Learning Environment

From the viewpoint of psycholinguistics, this paper concerns how to create an optimal language learning environment in language learning, to stimulate students enthusiasm to participate in classroom activities and to make language learning easier and more pleasant.

A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics

Online social networks are increasingly connecting people around the world.Influence maximization is a key area of research in online social networks,which identifies influential users during information dissemination.Most of the existing influence maximization methods only consider the transmission of a single channel,but real-world networks mostly include multiple channels of information transmission with competitive relationships.The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information,so that it can avoid the influence of other information,and ultimately affect the largest set of nodes in the network.In this paper,the influence calculation of nodes is achieved according to the local community discovery algorithm,which is based on community dispersion and the characteristics of dynamic community structure.Furthermore,considering two various competitive information dissemination cases as an example,a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known,and a novel influence maximization algorithm of node avoidance based on user interest is proposed.Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.