The umbrella legitimization of many such Fourier transforms is as tempered distributions (where the sense of "distribution" is not the probability sense, but in the sense of Laurent Schwartz). The various "regularization" tricks amount to approaching the given distribution in the "weak *-topology" on distributions, by more tractable functions. Fourier transform on tempered distributions is (provably) continuous, so we conclude that all these trick must yield the same outcome.
[Edit in response to comment:] The "how to compute" (once we know that any device succeeds) is non-trivial, insofar as it is not clear a-priori how explicit an outcome could be expected. The first volume of Gelfand-Graev-et-alia's "Generalized Functions" does many illuminating examples, mostly computed via meromorphic continuation.
The simplest family of examples is probably $|x|^s$. Here, the homogeneity and rotational symmetry, and the fact that Fourier transform respects these (in suitable senses), promise that the Fourier transform of $|x|^s$ on $\mathbb R^n$ is a constant multiple of $|x|^{n-s}$, for $\Re(s)>-n$ to assure local integrability. The constant multiple is determined (for example) by integrating against Gaussians.
Then use the fact that the derivative of $|x|^s$ in $s$ multiplies it by $\log|x|$, and set $s=0$. This is the nice way logarithms can arise. The implicit claim that we can do complex analysis with distribution-valued functions was legitimized by Schwartz, and is pervasive in Gelfand-et-alia.
Products of $|x|^s$ by harmonic polynomials can be treated almost identically, using the repn theory of the orthogonal group on harmonic polynomials.
That is, very often, some sort of unique_characterization of the tempered distribution, and of its image under Fourier Transform, reduce the computation to determination of the relevant constant!