The Members section of a Twitter report offers six different subsections to learn about how members self describe, their demographic profiles, and the content they publish.
1. Profiles
Example subset of users in this audience or cluster: a member mosaic assembled from images of the top 100 cluster members to give you an idea of how members visually represent themselves. Twitter members’ profile pictures are directly hyperlinked to their profiles.
In the case of Twitter audiences, Affinio clusters individuals based on the accounts they follow (i.e. their interest patterns). Once the algorithm detects a group of users with a pattern of following a handful of the same accounts, that group becomes the signal for a particular cluster. Individuals can only be members of one cluster. So once the patterns of multiple clusters have been observed, each individual within an audience is assigned to the closest matching cluster.
The mosaic of profile images represents the top 100 members who had the strongest signal for a particular cluster.
Gender Breakdown: this is identified by applying facial recognition technology to members' profile images. Compare cluster results to the line for the overall audience average.
Age Breakdown: this breakout is determined by applying facial recognition technology to members' profile images. Users <18 years are excluded. Compare cluster results to the line for the overall audience average.
Bio Keywords: these are the top terms that members of this audience (or cluster) use to describe themselves.
Tweeted From: these are the devices & apps that people within this audience (or cluster) use to access Twitter.
Top Locations: we analyze the profiles of every audience member and plot their locations using a heat map. Darker/highly saturated areas indicate zones where many cluster members live.
Location Tips: if you want to be able to see a global perspective and isolate a particular area (i.e. USA), you will need to run two reports. When creating a report, you can input a geographic location in the “people who are located in” field.
These are the most liked posts created by members of the audience or cluster.
4. Most Retweeted
These are the most retweeted posts by members of the audience or cluster.
This subsection provides graphs for what time by what day users post. You can leverage these metrics to find the best time to interact with your audience.
Engagement Timeline: this graph visualizes the engagement patterns across the month-long timeline of your report. As you hover or click boxes on the heat-map, you'll see where the data lands on the timeline.
Engagement Hour/Day Heatmap: green represents the most active times users post and red represents the least active times. As you hover or click boxes on the heat-map, to the right of the app, you'll see where they rank from the engagement peak and lowest point.
In the Engagement subsection found in the Members section, users can search for the total and average audience engagement by hour or day based on desired time zones. For example, if you wanted to understand when the best time to share content is with your audience located in New York, you could search New York in the Timezone Search bar.
Note: Time zones are based on the TZ database found here.
A sample Twitter stream for members of your audience or cluster. This stream is designed to help you experience Twitter through the eyes of a cluster member at the time the report was generated.