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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.
After the to your away from latest work at classifying the newest public group of tweeters out-of profile meta-study (operationalised within perspective once the NS-SEC–pick Sloan ainsi que al. into the complete methods ), we pertain a class identification formula to our studies to research whether particular NS-SEC communities much more otherwise less inclined to enable location attributes. Whilst classification recognition device isn’t finest, earlier in the day research shows it to be specific for the classifying specific communities, somewhat gurus . Standard misclassifications are regarding the occupational terms with other definitions (like ‘page’ or ‘medium’) and you may perform which can additionally be termed appeal (including ‘photographer’ otherwise ‘painter’). The potential for misclassification is a vital restrict to take on when interpreting the outcome, nevertheless important area would be the fact we have zero a great priori reason behind convinced that misclassifications wouldn’t be at random distributed all over people with and you may instead of place attributes allowed. With this in mind, we’re not much looking all round representation from NS-SEC groups on investigation once the proportional differences when considering venue permitted and you can low-permitted tweeters.
NS-SEC are harmonised with other Western european strategies, but the community detection device was designed to find-right up British work simply plus it really should not be used external of this perspective. Past research has known United kingdom pages having fun with geotagged tweets and you can bounding packages , but because the aim of this papers is to evaluate that it class along with other non-geotagging profiles we decided to play with big date zone because a proxy to own area. The Facebook API will bring a period of time area job for every associate in addition to adopting the analysis is bound to profiles from the you to definitely of the two GMT areas in the uk: 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.