Detecting scraper bots through scroll behaviour
A developer explores using 'burstiness' and 'memory' coefficients to distinguish human scrolling behavior from automated scraper bots. The author argues that human scrolling patterns are non-linear and bursty, making them difficult for current bots to replicate.
Why it matters
As bot detection becomes more sophisticated, identifying human-like behavioral patterns is essential for maintaining web security and preventing unauthorized data scraping.
August 20, 2026 Reading time: 4 minutes key "series-homelab"), ordered oldest-first. --> Ever since I first read "Burstiness and Memory in Complex Systems" by Kwang-Il Goh, I have been obsessed with the two formulas showcased in the paper. Burstiness (B), and its just-as-important counterpart Memory (M), let us understand the dynamics of event-based systems.
We can use them to analyse the behaviour of sent emails, texts or even heartbeats when only the time at which those events happened is known. This allows us to clearly establish which patterns are human-like and which aren't based on a dataset of already classified data. We know humans reply to texts in a bursty manner (the time they take to answer is not uniform) while simple bots respond as fast as possible, thus they have different B and M coefficients.
Goh, K.-I., & Barabási, A.-L. (2008), Figure 4
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