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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.

Parliaments could seem to be highly issue-agnostic places. All sorts of problems move in and out. But issues make cuts. Some parliamentarians become attached to specific issues. What if the parliament was approached not as a representation device for the national population, but as an assembly of multiple and constantly transforming issue-oriented publics? What kinds of issues come to the fore, how long does this last, and who associate themselves with them? The aim of the Twitter-thing is to trace the cuts issues make in a parliament. Each time a parliamentarian use a hashtag in a tweet, a link is created between that hashtag and the parliamentarian. The tool then generates a network visualization showing how parliamentarians group around topics and issues. The resulting 'issue publics' – or things in the sense of a collective aroused by an issue – are also 'data publics' because they are not necessarily aware of themselves as publics. At the same time, it is possible to self-select membership of these publics by using a specific hashtag. This raises the question of what feedback loops are at work between visualizations and those being visualized. How might a tool like the Twitter-thing change (parliamentary) politics? More generally, the tool prompts us to think about the fate of issues in institutionalized democracy. The Twitter-thing invites users to explore these questions by making the network available in an interactive format that makes it possible to zoom, search for particular politicians, parties or hashtags, narrow down the network, and follow it over time.

This study shows how liking politicians' public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems.