Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions

Estimating mobility of tourists. New Twitter-based procedure

“…The developed algorithm exploits Twitter's functionalities to find a greater number of tweets with geographical location than methods that relied only on point geotagged and bounding box. They are methods, used previously by researchers to analyze mobility [33] , [31] , [41] , given that they offer positional coordinates of tweets - very accurate in the former and approximates in the latter -, but with the limitations on the amount of tweets available. Thus, by the end of the procedure, each geolocalized tweet can have one of these kinds of geographical tags: point geotagged, bounding box, local tweet and QCA tweet.…”

“…In particular, Twitter was selected among other possible data sources due to its free and heterogeneous nature (collecting people movements, regardless of transportation mode), ease of access to data, and being less intrusive than options such as those focused on mobile GPS data [28] , [29] , [30] . Tweet geotagging has been used in the study of human mobility at international scale [31] , [32] , national scale [33] , [34] , [35] and in broad time [36] , [37] . Twitter data also have proven to be useful in the analysis of local mobility patterns in specific issues: college football events [38] , the migration from Puerto Rico due to a hurricane [39] , and the communication ecosystem during a typhoon for foreigners [40] .…”

Comparing spatial and content analysis of residents and tourists using Geotagged Social Media Data. The Historic Neighbourhood of Alfama (Lisbon), a case study

“…Studies that require higher precision have applied algorithms to identify the locations where the user is the most active, as it is recorded on social networks. Brogueira et al (2016) used frequency to identify the regions in Portugal from which national tourists in the Algarve came, Salas Olmedo et al (2018) defined tourists in Madrid as those who tweeted from there for less than one week within a year, while Manca et al (2017) rised the time frame up to 20 days and García-Palomares et al (2015) up to a month. These approaches have provided a more accurate portait of the tourism footprint, and have confirmed that visitors make a selective use of space.…”

“…However, the connection between content and spatial analysis is less often found in the literature. The tweet content analysis has been used to determine a place of destination's image (Brogueira et al, 2016;Garay & Morales Pérez, 2017;Williams et al, 2017), which does not fully explore the potentials of spatial resolution.…”

An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico

“…We focused on phenomena related to the interaction of the users on Twitter. These interactions allow to carry out studies like geolocated data (Brogueira, Batista and Carvalho, 2016), collaborative education (Kim, Hwang and Rho, 2016), sentiment analysis (Baydogan and Alatas, 2018), food industries (Singh, Shukla and Mishra, 2018) characterization of users (Lee, Wakamiya and Sumiya, 2013), disaster response (Xie and Yang, n.d.), drug war (Monroy-Hernández, Kiciman and Counts, 2015) and more. In this paper, we examine Twitter as the primary source to try to understand the social perception of violence in Mexico.…”