Present day anti-refugee and anti-immigrant rhetoric both in European countries and in the USA makes reference both to shared tropes and to culture-specific rhetoric devices. The paper analyzes four instances of Polish rabid anti-refugee rhetoric that is eminently country specific: they invoke Holocaust scenario as the means of dealing with the refugee question, should they appear on Polish soil, and specifically suggest exterminating them in former Nazi death camps. The analysis is carried out within the Conceptual Integration Theory, amended by (...) the Author with the notion of parasitic blends: these are said to occur when audiences recruit into the blend some elements of the two input spaces that were not intended to be recruited and come up with an emergent meaning that differ from the intended one. It is claimed that such possibility is actually built into CIT and explains why some of the criticism of CIT claims blends’ non-predictability and generally ex-post character of most analyses found in relevant literature. (shrink)

Recent works have suggested an analytical complementarity in mixing big and thick data sources. These works have, however, remained as programmatic suggestions, leaving us with limited methodological inputs on how to archive such complementary integration. This article responds to this limitation by proposing a method for ‘blending’ big and thick analytical insights. The paper first develops a methodological framework based on the cognitivist linguistics terminology of ‘blending’. Two cases are then explored in which blended spaces are crafted from engaging big (...) and thick analytical insights with each other. Through these examples, we learn how blending processes should be conducted as a rapid, iterative and collaborative effort with respect for individual expertise. Further, we demonstrate how the unique, but often overlooked, granularity of big data plays a key role in affording the blending with thick data. We conclude by suggesting four commonly appearing blending strategies that can be applied when relying upon big and thick data sources. (shrink)