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New app Who’s in Town provides a shocking insight into Instagram data collection

The data collection makes it really easy to get a detailed look at data profiling.

The new app, Who’s In Town is designed to interact with Instagram to tell you who is in town, presents its users with an eerie map of every location the “followed” people have visited and shared since they created their account. The interactive map updates in real-time and feeds on the data Instagram’s users intentionally share on the platform each time they use the geotag features in posts and stories.

The information is already public: users willingly choose what to share with their followers. But collecting it in one place over time means that Who’s in Town can aggregate seemingly unimportant data points into an extensive database containing habits and routines of Instagram’s public accounts.

It shows the visited places, such as restaurants and bars; when users last shared information about their location; and draws a detailed picture underlining habits that wouldn’t be noticeable just by looking at a profile.

Eric Barto, the app’s creator, said to Wired that the amount of data contained is “insane”.

“It’s the equivalent of you going through every single story and writing down every single location, just consistently all the time.”

He conducted a pre-release study and tracked the posting habits of over 15,000 active users on Instagram over multiple weeks, finding that 30 percent of people who post Instagram stories during the weekends geotag a minimum of one location.

“This capability is problematic… from a privacy perspective as long-term aggregate data can potentially be misused in various ways,” said Jason Polakis, security researcher and assistant professor at the University of Illinois.

Polakis pointed out that aggregate location data reveals sensitive information about daily routines, such as when a person habitually goes to work or is not at home, enabling stalking. It could also reveal social connections such as friendships and relationships, based on similarities in the posts and their locations.

Moreover, the information could be accessed by companies to discover a person’s secret habits or traits. For example, a health insurance company could scan geotag history to check how often a potential customer frequents bars or the gym.

According to Polakis, the app streamlines and facilitates “potentially invasive behavior at a large scale, as anyone installing the app would have access to this functionality.”

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