Maybe. It can feel like this, but applying mathematical models to social phenomena is tricky. You have to map formal parameters to fuzzy, poorly understood factors. How do you define, precisely, what is a "channel" on a social network? What is "signal", and what is "noise"? People can and do prove anything by using slightly different (or inconsistent) definitions here, getting the numbers to line up just like they want them to.
My understanding is that a better way to approach such mapping would be to shove in probability distributions in place of hard-to-map exact parameters - abstracting away choices and measurements lets you see the wider context here. This pushes the problem to defining the appropriate distributions, but I think that's more tamper-proof. Unfortunately, the results may come out next to useless - e.g. probability distributions so wide you could sail a carrier strike group through them.
I'd love to know what's considered the correct, robust approach to such problems.