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Table step three presents the connection between NS-SEC and you can area services

Table step three presents the connection between NS-SEC and you can area services

There was just a difference out-of cuatro

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.

Category (NS-SEC)

Following into off latest work with classifying the brand new public group of tweeters of profile meta-data (operationalised inside perspective once the NS-SEC–pick Sloan mais aussi al. on full methods ), i use a course recognition algorithm to your studies to analyze whether certain NS-SEC teams be otherwise less likely to allow location characteristics. Whilst category detection product isn’t finest, earlier studies have shown that it is appropriate within the classifying particular teams, rather advantages . General misclassifications is for the occupational conditions with other significance (such as for instance ‘page’ otherwise ‘medium’) and you can operate that will additionally be termed passions (such ‘photographer’ or ‘painter’). The potential for misclassification is an important maximum to take on when interpreting the outcome, although crucial point would be the fact i’ve no a priori reason for believing that misclassifications would not be at random distributed round the people with and you may versus location properties permitted.

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