author = "Vasconcelos, Luiz Eduardo Guarino de and Santos, Eder C. M. and 
                         Figueiredo Neto, M{\'a}rio Lemes de and Ferreira, Nelson Jesus",
                title = "Using Tweets for Rainfall Monitoring",
            booktitle = "Advances in Intelligent Systems and Computing",
            publisher = "Springer International Publishing",
                 year = "2016",
               editor = "Lafiti, Shahram",
                pages = "1157--1167",
                 note = "International Conference on Information Technology, 13",
             keywords = "Twitter, Hashtags, Geolocation, Social media analysis.",
             abstract = "In Brazil, the summer season is the wettest period in which many 
                         disasters can happen, such as landslides and floods. In recent 
                         years, even with the rainy season, the metropolitan region of 
                         S{\~a}o Paulo (Brazil) suffers a severe water crisis. Given this 
                         scenario, monitoring of rainfall is fundamental for taking 
                         preventive actions and planning in the various business branches. 
                         Thus, the use of computers to develop tools that assist the 
                         rainfall monitoring can help extend the coverage of the existing 
                         solutions. Moreover, it is known that, every day, the number of 
                         social media users is increasing, and consequently increases the 
                         amount of content published in these medias. The objective of this 
                         study is to analyze the contents of the Twitter social media, 
                         especially the tweets related to rainfall events in order to 
                         determine whether this information can contribute to the 
                         monitoring of rainfall events in Brazil. More than 1 million 
                         tweets published in Brazil related to rainfall were collected in a 
                         period of 30 days. Gathered tweets were analyzed and evaluated 
                         taking into account the data collected by automatic weather 
                         stations (AWS or EMA). The results were satisfactory and indicate 
                         a relationship between the geolocated tweets and data from AWS.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                  doi = "10.1007/978-3-319-32467-8_100",
                  url = "http://dx.doi.org/10.1007/978-3-319-32467-8_100",
                 isbn = "9783319324661",
                label = "lattes: 8626122636195184 5 VasconcelosSanNetFerVas:2016:UsTwRa",
             language = "pt",
          seriestitle = "Advances in Intelligent Systems and Computing",
                  url = "http://link.springer.com/10.1007/978-3-319-32467-8_100",
               volume = "448",
        urlaccessdate = "23 nov. 2020"