J. Med. Internet Res. - Right time, right place health communication on Twitter: value and accuracy of location information.

Tópicos

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Resumo

CKGROUND: Twitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility.OBJECTIVE: The objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data.METHODS: Location data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state's population with the number of Twitter users who enable the GPS location option.RESULTS: The GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user's city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r = 0.97, P < .001).CONCLUSIONS: Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users' geographical location.

Resumo Limpo

ckground twitter provid various type locat data includ exact global posit system gps coordin use infoveil infodemiolog ie studi monitor onlin health inform health communic intervent despit potenti twitter locat inform well understood well document limit public health utilityobject object studi document describ various type locat inform avail twitter differ type locat data can ascertain twitter user describ inform key inform futur research avail usabl limit locat datamethod locat data gather direct twitter use applic program interfac api maximum tweet allow twitter gather total tweet separ week octob novemb final dataset consist million tweet million uniqu user frequenc locat option calcul determin preval various locat data option region world time zone state within unit state data us census bureau also compil determin popul proport state pearson correl coeffici use compar state popul number twitter user enabl gps locat optionresult gps locat data ascertain tweet uniqu user use simpl textmatch approach user profil continent us time zone abl use determin user citi state agreement gps data data textmatch approach high furthermor signific correl number twitter user per state us census state popul r p conclus health research explor way use twitter data diseas surveil awar major tweet current associ identifi geograph locat locat can identifi approxim time number tweet use straightforward textmatch process compar use gps locat inform avail twitter given strong correl data gather method futur research may consid use qualit approach higher yield text mine acquir inform twitter user geograph locat

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