BackgroundTwitter 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.ObjectiveThe 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.MethodsLocation 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.ResultsThe 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).ConclusionsHealth 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.

Current mobile devices do not leverage the rich haptic channel of information that our hands can sense, and instead focus primarily on touch based graphical interfaces. Our goal is to enrich the user experience of these devices through bidirectional haptic and tactile interactions (display and control) around the edge of hand-held devices. We propose a novel type of haptic interface, a Haptic Edge Display, consisting of actuated pins on the side of a display, to form a linear array of tactile pixels (taxels). These taxels are implemented using small piezoelectric actuators, which can be made cheaply and have ideal characteristics for mobile devices. We developed two prototype Haptic Edge Displays, one with 24 actuated pins (3.75mm in pitch) and a second with 40 pins (2.5mm in pitch). This paper describes several novel haptic interactions for the Haptic Edge Display including dynamic physical affordances, shape display, non-dominant hand interactions, and also in-pocket "pull" style haptic notifications. In a laboratory experiment we investigated the limits of human perception for Haptic Edge Displays, measuring the just-noticeable difference for pin width and height changes for both in-hand and simulated in-pocket conditions.

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The health landscape is shifting to one in which common individuals are no longer merely consumers, but also producers, of health information. We demonstrate that social media platforms provide the means to seek and receive personalized, credible health advice from peers at any place and time, by tracking dental health advice sought and received in Twitter. We show that for genuine dental advice-seeking questions, answers are received 32 % of the time, with the first reply coming less than 6 min after the question is posed, in the median. We compare our results to studies focusing on generic questions and find stronger relationships between users that answer health questions. Additionally, we find that users with more social capital, in the form of more reciprocal follower/following relationships, are more likely to receive responses and receive them faster, and are thus better able to leverage their social networks in receiving advice.