Gender Reporting in Sprout

Sprout provides information about the gender of your social audience, and this page explains the methodology used in each report. Sprout recognizes gender is not binary and we strive to reflect that in our gender-related reporting. However, it is not yet possible to represent non-binary gender in all cases due to limitations in network partner data.

Facebook Pages Report

Gender data in the Facebook Pages Report is sourced from Facebook user profiles and indicated by users themselves. Users are given the option to select from male, female, or custom gender designations (with freeform text entry)—or to decline providing gender information. When sharing gender data with Sprout, Facebook combines custom and declined entries into a single group.

In order to protect the gender information of individual Facebook users, your Sprout-connected Facebook Page must have at least 100 fans in order for gender data to be collected and displayed.

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Instagram Business Profiles Report

Gender data in the Instagram Business Profiles Report is sourced from Instagram user profiles and indicated by users themselves. Users are given the option to select from male, female, or custom gender designations.

In order to protect the gender information of individual Instagram followers, your Sprout-connected Instagram profile must have at least 100 followers in order for gender data to be collected and displayed.

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Twitter Profiles Report

Gender data in the Twitter Profiles Report is inferred by a Sprout internal tool. Sprout uses data from Twitter follower profiles like name, location, and bio to approximate the make-up of your audience by gender. Due to limitations of this approach, only inferences of “men” and “women” are displayed—there is no non-binary category. We hope to represent non-binary genders and improve the accuracy of this data, and we will do so when better network data becomes available.

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Advanced Listening

Gender data in Advanced Listening is inferred by a Sprout internal tool. Sprout uses public data like name, location, and bio from author profiles on Twitter, YouTube, Reddit, Tumblr, and web sources to create a best guess on audience gender. Due to limitations of this approach, only inferences of “men” and “women” are displayed—there is no non-binary category. We hope to represent non-binary genders and improve the accuracy of this data, and we will do so when better network data becomes available.

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