Combination of computer techniques scans sites like Instagram

Researchers at the University of Colorado Boulder have developed a new technique to debunk personal attacks on social networks. The new approach combines several different computer tools to scan large amounts of social media data and send alerts that abuse has occurred to parents or network administrators.

Automated scanning

“The approach uses five times fewer computer resources than existing tools. “This is efficient enough to monitor a network the size of Instagram with a modest investment in server power,”

explains study co-author Richard Han.

“The reaction of social networks to fake news has recently started to increase. However, in the case of cyberbullying, this increase must look the same.”

To create their tool, the researchers first used real people to teach a computer program how to separate benign online comments from abuse.

The system works reliably: When a user uploads a new post, it is automatically scanned. If the post appears questionable, it will be marked as a high priority for further review.

Unique performance achieved

The researchers tested their approach with real data from the former video-sharing platform Vine and Instagram. Han chose these networks because they make their data publicly available.

In their own study, the researchers then calculated that their toolset could monitor activity on Vine and Instagram in real time and detect cyberbullying behavior with 70 percent accuracy.

In addition, the approach could send warning signals two hours after the start of abuse - a performance not yet achieved by currently available software.

The researchers also released a free app, BullyAlert, for Android phones that allows parents to receive alerts when their children are being bullied on Instagram.


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