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Researchers open source tools to identify Twitter bots at scale

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Security

Duo Security published technical research and methodology detailing how to identify automated Twitter accounts, known as bots, at a mass scale. Using machine learning algorithms to identify bot accounts across their dataset, Duo Labs researchers also unraveled a sophisticated cryptocurrency scam botnet consisting of at least 15,000 bots, and identified tactics used by malicious bots to appear legitimate and avoid detection, among other findings.

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Also: Duo Security researchers’ Twitter ‘bot or not’ study unearths crypto botnet

Spot the Bot: Researchers Open-Source Tools to Hunt Twitter Bots

  • Spot the Bot: Researchers Open-Source Tools to Hunt Twitter Bots

    Their goal? To create a means of differentiating legitimate from automated accounts and detail the process so other researchers can replicate it.

    What makes Twitter bots tick? Two researchers from Duo Security wanted to find out, so they designed bot-chasing tools and techniques to separate automated accounts from real ones.

    Automated Twitter profiles have made headlines for spreading malware and influencing online opinion. Earlier research has dug into the process of creating Twitter datasets and finding potential bots, but none has discussed how researchers can find automated accounts on their own.

    Duo's Olabode Anise, data scientist, and Jordan Wright, principal R&D engineer, began their project to learn about how they could pinpoint characteristics of Twitter bots regardless of whether they were harmful. Hackers of all intentions can build bots and use them on Twitter.

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