Ever wonder what your Twitter followers look like in a report? Know your followers?

How they do it

To prepare this analysis, we look at data that your followers have chosen to make public on Twitter (their names, locations, bios etc.), and process this iteratively, using a series of algorithms developed in-house at schmap.

These algorithms are based on the tenets of fuzzy set theory (as pioneered by the mathematician and computer scientist Lotfi A. Zadeh), a probabilistic logic that we use to reassess the likelihood of given classifications, based on often conflicting interpretations of multiple data signals. It’s the kind of approach that’s fundamental to the promised Web 3.0, a semantic web, where fuzzy logic will power genuinely intelligent search, customer interaction and much more.

Put simply, we make statistically sensible deductions based on multiple bits of data. The multi-signal statistical stuff is necessary, because computers, by and large, still suck at interpreting natural language, particularly the kind of culturally diverse and informal language your followers use on Twitter.

Here we provide some insight into this process and the challenges involved, with reference to the primary data signals for our analysis…


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