Social media data drives countless business decisions, but up to 40% of social media engagement may be artificial or manipulated by bots. For data scientists accustomed to cleaning messy data, deliberately manipulated data presents an entirely different challenge that requires specialized detection techniques.

In this Value Boost episode, Tim O'Hearn joins Dr. Genevieve Hayes to reveal practical strategies for identifying and filtering out bot activity from social media datasets to extract trustworthy business insights.

This episode uncovers:

1. The telltale patterns in social media data that reveal bot activity [03:10]
2. How machine learning classifiers can identify bot accounts [05:20]
3. Why removing bot activity can increase marketing ROI by 10-20% [06:41]
4. The broader application of these techniques beyond social media for identifying "dodgy" data records in any dataset [07:25]

Guest Bio

Tim O’Hearn is a software engineer who spent years gaining millions of followers for clients by circumventing anti-botting measures on social networks. He is also the author of the new book, Framed: A Villain’s Perspective on Social Media.

Links

• Tim's Website
• Connect with Tim on LinkedIn
• Subscribe to Tim's newsletter
• Connect with Genevieve on LinkedIn
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