Dining table 2 presents the partnership ranging from gender and if a person brought a great geotagged tweet inside the data period

Though there is some work you to questions whether or not the step 1% API was random with regards to tweet perspective for example hashtags and you will LDA data , Twitter keeps that the sampling formula is actually “completely agnostic to the substantive metadata” in fact it is thus “a good and you may proportional sign all over most of the cross-sections” . As we could possibly not expect one scientific prejudice to get present throughout the data due to the nature of your own 1% API load we consider this data to get an arbitrary try of your Facebook populace. I supply zero a beneficial priori reason for convinced that pages tweeting inside are not user of your own population therefore we is for this reason implement inferential statistics and relevance testing to evaluate hypotheses concerning if or not people differences between individuals with geoservices and you may geotagging let disagree to those who don’t. There may well be profiles who have made geotagged tweets just who commonly picked up from the step 1% API stream and it will continually be a restriction of every search that will not have fun with one hundred% of investigation that’s an important certification in every search using this type of databases.

Facebook terms and conditions end united states regarding openly sharing this new metadata given by the brand new API, thus ‘Dataset1′ and you can ‘Dataset2′ include precisely the member ID (that’s acceptable) while cupid the demographics we have derived: tweet words, sex, ages and you will NS-SEC. Replication of studies will be used owing to individual experts using associate IDs to get this new Twitter-produced metadata that people try not to share.

Location Characteristics against. Geotagging Private Tweets

Considering all the users (‘Dataset1′), full 58.4% (n = 17,539,891) of pages lack location features allowed although the 41.6% would (letter = 12,480,555), hence exhibiting that profiles do not prefer it function. Having said that, the fresh new ratio of these on the function let is actually large given one pages need to opt for the. When leaving out retweets (‘Dataset2′) we come across you to definitely 96.9% (letter = 23,058166) do not have geotagged tweets about dataset even though the 3.1% (n = 731,098) do. This is certainly much higher than prior estimates of geotagged blogs regarding to 0.85% as the notice of the studies is found on the latest proportion of pages with this particular trait instead of the proportion away from tweets. Although not, it’s prominent that in the event a hefty proportion off users permitted the global form, few then move to indeed geotag its tweets–therefore showing certainly one to enabling towns and cities properties are a necessary but perhaps not sufficient standing away from geotagging.


Table 1 is a crosstabulation of whether location services are enabled and gender (identified using the method proposed by Sloan et al. 2013 ). Gender could be identified for 11,537,140 individuals (38.4%) and there is a slight preference for males to be less likely to enable the setting than females or users with names classified as unisex. There is a clear discrepancy in the unknown group with a disproportionate number of users opting for ‘not enabled’ and as the gender detection algorithm looks for an identifiable first name using a database of over 40,000 names, we may observe that there is an association between users who do not give their first name and do not opt in to location services (such as organisational and business accounts or those conscious of maintaining a level of privacy). When removing the unknowns the relationship between gender and enabling location services is statistically significant (x 2 = 11, 3 df, p<0.001) as is the effect size despite being very small (Cramer's V = 0.008, p<0.001).

Male users are more likely to geotag their tweets then female users, but only by an increase of 0.1%. Users for which the gender is unknown show a lower geotagging rate, but most interesting is the gap between unisex geotaggers and male/female users, which is notably larger for geotagging than for enabling location services. This means that although similar proportions of users with unisex names enabled location services as those with male or female names, they are notably less likely to geotag their tweets than male or female users. When removing unknowns the difference is statistically significant (x 2 = , 2 df, p<0.001) with a small effect size (Cramer's V = 0.011, p<0.001).