Home location inference from sparse and noisy data: models and applications

Optimal Proxy Selection for Socioeconomic Status Inference on Twitter

“…Moreover, when comparing the weekday and weekend patterns of behavior, we notice that the average distance to the inferred home location seemed to be smaller when considering only geolocations posted during the weekend, most likely due to the absence of the home-to-work commuting during the weekend. We found that this circadian pattern was more consistent with earlier results [31] when we considered all geolocated tweets ("All" in Figure 1(a)) rather than only tweets including "home-related" expressions ("Night" in Figure 1(a)). To further verify the inferred home locations, for a subset of 29,389 users we looked for regular expressions in their tweets that were indicative of being at home [31], such as "chez moi", "bruit", "dormir" or "nuit".…”

“…We found that this circadian pattern was more consistent with earlier results [31] when we considered all geolocated tweets ("All" in Figure 1(a)) rather than only tweets including "home-related" expressions ("Night" in Figure 1(a)). To further verify the inferred home locations, for a subset of 29,389 users we looked for regular expressions in their tweets that were indicative of being at home [31], such as "chez moi", "bruit", "dormir" or "nuit". In Figure 1(c) we show the temporal distribution of the rate of the word "dormir" at the inferred home locations.…”

“…To verify our results, we computed the average weekday and weekend distance from each recorded location of a user to her inferred home location defined either as her most frequent location overall or among locations posted outside of work-hours from 9AM to 6PM (see Figures 1(a) and 1(b)). This circadian pattern displays great similarity to earlier results [31] with two maxima, roughly corresponding to times at the workplace, and a local minimum at 1PM due to people having lunch at home for locations posted in weekdays. Moreover, when comparing the weekday and weekend patterns of behavior, we notice that the average distance to the inferred home location seemed to be smaller when considering only geolocations posted during the weekend, most likely due to the absence of the home-to-work commuting during the weekend.…”

“…To find users with a representative home location we followed the method published in [30,31]. As a bottom line, we concentrated on 127,614 users who posted at least five geolocated tweets with valid GPS coordinates, with at least three of them within a valid census cell (for definition see later), and over a longer period than seven days.…”

“…Using geolocated users identified in Section 3.1.1, we further filtered them to obtain a smaller set of users with more precise inferred home locations. We screened all of their geotagged tweets and looked for regular expressions determining whether or not a tweet was sent from home [31]. As explained in Section 3.1.1, we exploited that "home-suspected" expressions appeared with a particular temporal distribution (see Figure 1(c)) since these expressions were used during the night when users are at home.…”