Smells like clean data

A new approach to sniffing out user behaviour on social networks could improve how service providers understand and respond to their users’ needs. The work discussed in the International Journal of Computational Systems Engineering offers a more accurate way to track “browsing trajectories”, the path a user takes through a sequence of pages and content. Critically, the approach cleans up this data by filtering out the digital noise that has always been the bane of such analyses.

Social networks generate huge amounts of data, much of it ambiguous, inconsistent or irrelevant. For researchers and developers trying to spot patterns in this activity, for example, those running content-recommendation systems or monitoring user wellbeing, this is a significant problem. Current systems struggle to distinguish between signal and noise, which means any analysis leads to less effective personalisation and an increased risk of information overload for users.

The new approach integrates several computational tools, including “fuzzy logic”, which can handle uncertainty in a way that binary logic cannot. Fuzzy logic can thus model ambiguity in human behaviour online. A second tool is the use of a “random forest learning algorithm”, which can handle large and complex datasets and offer several decision trees by combining outputs to improve how well the system predicts behaviour. The third tool is “matched filtering”, a technique borrowed from signal processing, that detects patterns within noisy data.

When combined, these tools allowed the researchers to isolate meaningful user behaviour among irrelevant or redundant data and so boost the signal-to-noise ratio. In subsequent simulations, the team was able to achieve up to 93 percent behaviour-prediction accuracy. Such precise analysis of browsing trajectories might help social platforms serve content that more closely matches what users are actually looking for, increasing satisfaction while reducing exposure to irrelevant or disruptive material.

Cai, Q. (2025) ‘Social network user browsing trajectory detection based on soft computing to promote a healthy environment’, Int. J. Computational Systems Engineering, Vol. 9, No. 12, pp.12–21.