Table 2 merchandise the partnership anywhere between sex and if or not a person delivered a beneficial geotagged tweet within the studies several months

However, there is a few works that issues whether the 1% API are haphazard regarding tweet framework such as for example hashtags and LDA study , Myspace maintains that testing algorithm is “totally agnostic to your substantive metadata” and is thus “a good and proportional expression across the all of the cross-sections” . Because the we might not be expectant of one systematic bias becoming expose regarding the analysis as a result of the character of your step one% API load i think about this study getting a haphazard decide to try of your Facebook populace dil mil online. I also have no good priori factor in believing that profiles tweeting for the aren’t affiliate of your people and in addition we can be for this reason implement inferential statistics and you may significance tests to check on hypotheses regarding if one differences between people with geoservices and you will geotagging permitted differ to those that simply don’t. There is going to well be users who have produced geotagged tweets whom commonly acquired on step one% API load and it will be a restriction of any research that will not explore 100% of the research in fact it is a significant qualification in every look with this particular data source.

Myspace small print prevent all of us from publicly sharing the new metadata provided by the fresh new API, therefore ‘Dataset1′ and you will ‘Dataset2′ consist of precisely the affiliate ID (that’s acceptable) additionally the demographics you will find derived: tweet language, intercourse, ages and you may NS-SEC. Duplication on the study is going to be presented as a result of individual boffins playing with associate IDs to gather the fresh new Fb-produced metadata we usually do not share.

Venue Characteristics compared to. Geotagging Individual Tweets

Considering most of the profiles (‘Dataset1′), total 58.4% (n = 17,539,891) from pages don’t have place features let whilst the 41.6% perform (n = 12,480,555), for this reason exhibiting that most pages do not like it setting. However, the newest proportion of these toward mode enabled is actually higher given you to definitely profiles have to opt inside the. When leaving out retweets (‘Dataset2′) we come across that 96.9% (letter = 23,058166) do not have geotagged tweets about dataset although the step three.1% (n = 731,098) manage. This is exactly much higher than simply past rates out-of geotagged stuff from up to 0.85% given that attract on the data is on the fresh new proportion from users with this specific feature rather than the ratio off tweets. Yet not, it is known one to whether or not a hefty ratio away from profiles permitted the global function, hardly any after that relocate to indeed geotag their tweets–therefore indicating obviously one to enabling towns features was a required but maybe not adequate condition regarding 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).