Andréa do Nascimento Barbosa Cacho

Social Smart Destination: A Platform to Analyze User Generated Content in Smart Tourism Destinations

The purpose of this paper is to present a platform that uses social media as a data source to support the decisions of policymakers in the context of smart tourism destinations initiatives. The proposed platform was implemented and tested during the 2014 FIFA World Cup. In total were analyzed 7.5 million tweets. The results show that it is possible to identify the nationality, language of the posts, points of agglomeration and concentration of visitors. Overall the initial results suggest that data collected from Twitter posts can be applicable to the effective management of smart tourism destinations.

Mobile tourist guide supporting a smart city initiative: a Brazilian case study

Purpose – The purpose of this paper is to describe a smart city initiative presenting a mobile tourist guide developed for Natal, Brazil. Design/methodology/approach – This study has employed an exploratory case study approach to gain more knowledge about a smart city initiative and a mobile tourist guide in Brazil. The city of Natal was selected for this case study since it was one of the host cities during the FIFA World Cup 2014. The collected data for this research came from government (e.g. Natal Smart City plan), academic sources (e.g. Metropole Digital Institute (IMD)), and tourists’ information from the mobile tourist guide application. Findings – The smart city initiative of Natal, and the mobile tourist guide (named Find Natal) responsible for collecting, processing, sharing, storing and analysing the tourist behaviour were detailed in the paper. The Smart City Consortium in Natal is developing an interoperable and distributed infrastructure that is advancing the state-of-the-art in information ...

Using Social Network to Support Smart City Initiatives

A central issue in the context of smart cities is for one to be able to acquire timely information about city events for purposes ranging from being able to act promptly in response to events to just monitoring and collecting statistics about them. This paper describes an initial framework focused on processing messages posted in the Twitter social network. Key issues are the high throughput -- a large volume of data per second that needs to be processed, and the need to process ill formed natural language texts. With these in mind the framework has pipelined modules for robust, fast, real time tweet acquisition and storage, filtering of several kinds, natural language processing and sentiment analysis, that feed a final analysis and visualization module. A case study of sentiment analysis during the FIFA World Cup 2014 in Brazil is used to validate the effort made so far.