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Fig 1 illustrates the two distributions of age for those who do enable location services and those who do not. There is a long tale on both, but notably the tail has a less steep decline on the right-hand side for those without the setting enabled. An independent samples Mann-Whitney U confirms that the difference is statistically significant (p<0.001) and descriptive measures show that the mean age for ‘not enabled' is lower than for ‘enabled' at and respectively and higher medians ( and respectively) with a slightly higher standard deviation for ‘not enabled' (8.44) than ‘enabled' (8.171). This indicates an association between older users and opting in to location services. One explanation for this might be a naivety on the part of older users over enabling location based services, but this does assume that younger users who are more ‘tech savvy' are more reticent towards allowing location based data.

Fig 2 shows the distribution of age for users who produced or did not produce geotagged content (‘Dataset2′). Of the 23,789,264 cases in the dataset, age could be identified for 46,843 (0.2%) users. Because the proportion of users with geotagged content is so small the y-axis has been logged. There is a statistically significant difference in the age profile of the two groups according to an independent samples Mann-Whitney U test (p<0.001) with a mean age of for non-geotaggers and for geotaggers (medians of and respectively), indicating that there is a tendency for geotaggers to be slightly older than non-geotaggers.

Class (NS-SEC)

Adopting the into out of recent manage classifying the fresh new social group of tweeters from character meta-study (operationalised inside framework because the NS-SEC–pick Sloan et al. for the full methodology ), i incorporate a course recognition formula to the studies to analyze whether or not specific NS-SEC organizations be a little more otherwise less likely to allow place attributes. Although the group detection equipment is not best, early in the day research shows it to be right when you look at the classifying particular organizations, somewhat positives . General misclassifications is actually regarding the work-related terms and conditions with other significance (such as for instance ‘page’ or ‘medium’) and you will jobs that may additionally be termed passions (such ‘photographer’ or ‘painter’). The potential for misclassification is a vital limitation to consider when interpreting the outcome, nevertheless very important section would be the fact i’ve zero an effective priori cause for believing that misclassifications wouldn’t be randomly marketed round the people who have and as opposed to place characteristics allowed. With this thought, we are really not plenty seeking the overall image off NS-SEC teams throughout the analysis because proportional differences when considering place enabled and you will low-permitted tweeters.

NS-SEC should be harmonised with other Western european steps, nevertheless the job identification equipment was designed to get a hold of-right up Uk work merely also it really should not be applied exterior for the perspective. Past research has identified British pages using geotagged tweets and you can bounding packages , however, since function of that it papers is always to evaluate it class together with other non-geotagging pages we decided to play with time area since the a proxy to have venue. The brand new Facebook API will bring a period zone job each affiliate as well as the following the analysis is bound in order to profiles of that of these two GMT zones in britain: Edinburgh (n = twenty eight,046) and you will London (n = 597,197).

There is a statistically significant association between the two variables (x 2 = , 6 df, p<0.001) but the effect is weak (Cramer's V = 0.028, p<0.001). 6% between the lowest and highest rates of enabling geoservices across NS-SEC groups with the tweeters from semi-routine occupations the most likely to allow the setting. Why those in routine occupations should have the lowest proportion of enabled users is unclear, but the size of the difference is enough to demonstrate that the categorisation tool is measuring a demographic characteristic that does seem to be associated with differing patterns of behaviour.