There is no graph here yet. This might have two different reasons:
1. You lack the data to generate this graph. For example you might never have tweeted with geo locations enabled, thus we can't show you your maps or times when you tweet. Or you never replied/retweeted anyone.
2. This graph isn't fully calculated yet. Depending on the size of the Twitter archive the initial graph generation can take a while. But no worries, once that is done they will be loaded instantaneously on subsequent visits. We will send you an email through Open Humans once your graphs have been calculated.
Replying to someone's tweets is the main way of interacting on Twitter. The people we interact with can stay the same over large spans of time, or they can change - reflecting different social networks and circles of friends we move in.
The graph here shows you the Top Five people you have replied to most, summed up for your total time on Twitter. To make the graph less prone to noise the replies for each user are not given per day but are summed up per quarter.
The x-axis thus lists individual
quarters since the sign-up to Twitter. The y-axis gives the
sum of replies for the individual users per quarter. The Top Five
people you replied to are color-coded and are also listed on the graph
when hovering over a given data point.
There is no graph here yet. This might have two different reasons:
1. You lack the data to generate this graph. For example you might never have tweeted with geo locations enabled, thus we can't show you your maps or times when you tweet. Or you never replied/retweeted anyone.
2. This graph isn't fully calculated yet. Depending on the size of the Twitter archive the initial graph generation can take a while. But no worries, once that is done they will be loaded instantaneously on subsequent visits. We will send you an email through Open Humans once your graphs have been calculated.
Twitter can be a highly gendered experience and there is plenty of research showing that such biases overwhelmingly favor men . Other tools, like Twitterlytic allow you to find out how the gender breakdown is amongst the people you follow and the people that follow you. By looking at a whole Twitter archive we can have a look into whether interactions - replies and retweets - are gender balanced as well.
The graph shows you the number of replies to Twitter users
that are classified as either male or female. The
classifications are predictions based on users' first names as
given in their Twitter accounts. The predictions itself are performed by the Python
package
gender_guesser
. It uses name/gender-frequencies from a larger text corpus.
mostly male, mostly female, andy
and unknown classifications are ignored.
To decrease the noise the daily values have been averaged by a daily
average over a 180 day window (dataframe.rolling('180d').mean()).
Ideally these graphs would include non-binary folks. Doing this is a bit trickier. It is thus a work in progress.
There is no graph here yet. This might have two different reasons:
1. You lack the data to generate this graph. For example you might never have tweeted with geo locations enabled, thus we can't show you your maps or times when you tweet. Or you never replied/retweeted anyone.
2. This graph isn't fully calculated yet. Depending on the size of the Twitter archive the initial graph generation can take a while. But no worries, once that is done they will be loaded instantaneously on subsequent visits. We will send you an email through Open Humans once your graphs have been calculated.
Even more interesting than whether replying to people might be gendered can be the question which voices are being amplified . On Twitter a good indicator of amplification are retweets. These can be gender balanced or show biases, similarly to the replies to other users.
The graph shows you the number of retweets to Twitter users
that are classified as either male or female. The
classifications are again predictions made by the Python
package
gender_guesser
.
To decrease the noise the daily values have again been averaged by a daily
average over a 180 day window (dataframe.rolling('180d').mean()).
Ideally these graphs would include non-binary folks. Doing this is a bit trickier. It is thus a work in progress.