u/LuisCosta_

Hey everyone!

We partnered with master’s students from Malmö University who built a bot-detection game where 710 players had to spot AI-generated comments in a simulated social media feed. The game tested players across four topics, two neutral (data centers, pineapple on pizza) and two emotionally charged (immigration, women's rights).

Some highlights:

• Only 53% of participants won the game. The average player caught just 58% of bots;
• Reddit and X users tied for the best bot-detection rate at 68%. Facebook users scored low at 47% and were the most likely to falsely accuse real people of being bots;
• This was the big one: topic matters. On data centers, players caught 71% of bots. On immigration, that dropped to 54%. On women's rights, it fell to 49%. The more emotional the topic, the more bots slipped through unnoticed;
• You'd think being online all the time would sharpen your instincts. It doesn't. Moderate users who check social media a few times a day actually outperformed people who are online almost constantly;
• And all of this is happening while fake accounts cost as little as $0.08 to create. Platforms deleted 6.3 billion fake accounts and 11.1 billion spam content pieces last year.

The game is still live if you want to test yourself: https://botornot.one/

Full study with methodology: https://surfshark.com/blog/bot-detection-experiment

How often do you engage with a comment before even considering it might not be a real person?

Deepfakes have been making headlines for years, but most of the conversation is about the technology itself, not the financial damage it's doing.

So we pulled 7 years of fraud data from the AI Incident Database, Resemble.AI, and the OECD to find out who's losing money, how much, and what people should actually watch out for. Here's what stood out:

• Deepfake fraud has caused $2.19 billion in global losses, with $1.65 billion of that reported in 2025 alone;
• The most effective scam? Using deepfakes of celebrities and government officials to push fake investment opportunities, accounting for 52% of all losses. Think of all those deepfaked Elon Musk crypto ads you've probably scrolled past, or the Brad Pitt scam that made the news not long ago;
• The US leads globally at $712 million, with 43% from corporate attacks. Remember the Hong Kong case where a finance worker joined a video call with deepfaked versions of his colleagues and transferred $25 million? That's the kind of thing driving these numbers;
• The US also accounts for 99.9% of all deepfake family impersonation losses worldwide. Imagine getting a call from your mom in a panic asking for money, except it's not actually her voice, it's a clone. That's already a $124 million problem.

Full research → https://surfshark.com/research/chart/deepfake-fraud-countries

How do you see deepfake scams evolving from here? Do you have a plan to protect yourself, or does it feel like one of those things that's hard to prepare for?

Hey everyone,

So every January, like clockwork, fitness searches spike globally. This year, interest in personal training hit its highest point since 2022. A lot of that growth is being driven by AI — apps promising personalized coaching at a fraction of what a human trainer costs.

That made us curious. If these apps are using AI to personalize your experience, what are they actually collecting to make that happen?
We went through Apple App Store privacy disclosures and privacy policies for five of the most popular workout apps — Strava, Nike Training Club, Peloton, LADDER, and Fitness+. The gap between them was... pretty wide:

• Strava collects 20 out of 35 data types linked to your identity — location, purchase history, photos, search history, the works. Nike Training Club sits at 19;
• Peloton? Just 2;
• 4 out of 5 track you across other apps and websites. Fitness+ was the only one that doesn't;
• All 5 have AI features. Some, like Strava, openly state they use your data to train their AI models.

The methodology of the study: we used Google Trends to track global search interest in "fitness" and "personal training" from January 2022 onward. For the data collection analysis, we selected apps from a CNET list based on the largest number of monthly active users in 2025 (via Similarweb), and reviewed their Apple App Store privacy disclosures and privacy policies for AI-related practices.

You can check the full research → https://surfshark.com/research/chart/ai-fitness

Do you use any of these apps? Did any of these findings surprise you?