  The last decade has witnessed an explosion on the computational power and a
parallel increase of the access to large sets of data (the so called Big Data
paradigm) which is enabling to develop brand new quantitative strategies
underpinning description, understanding and control of complex scenarios. One
interesting area of application concerns fraud detection from online data, and
more particularly extracting meaningful information from massive digital
fingerprints of electoral activity to detect, a posteriori, evidence of
fraudulent behavior. In this short article we discuss a few quantitative
methodologies that have emerged in recent years on this respect, which
altogether form the nascent interdisciplinary field of election forensics.
