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Project

"Trust me, I defend your interests". A Multidisciplinary Study of Politicians' Representative Claims and their Effects on Citizens.

Representative claims—i.e. statements in which politicians claim to represent somebody or to know what is in the interests of the represented—are a key element of political communication. Yet, empirical research on these claims is scarce and many central questions about the incidence, type, and effects of representative claims making remain unanswered. For example, at present, we do not know what kinds of claims politicians actually make, how parties differ in their claims making, and how different communication channels are used to make different types of claims. Moreover, we are in the dark about the effects that these claims have on citizens, and in particular on how well they feel represented (or not) by their representatives. This project tackles the above questions by drawing on two different but complementary approaches: a political science and a computational linguistics approach. From a political science point of view, we aim (1) to examine the use (frequency and type) of representative claims by politicians, on three communication channels (parliament, mass media and Facebook), in a longitudinal fashion (1999-2020), and (2) to test the effects that these claims have on citizens' feeling of (not) being well-represented. To reach these aims we will rely on a large-scale content analysis of political speech by politicians (who make representative claims) and by citizens (who react to these claims), complemented with survey-experiments on citizens. The use of advanced techniques in computational linguistics will make such a big data collection possible. Computational linguists will not only have an assisting or methodological role in the project, however (i.e. enabling the data collection); there is a substantive contribution to make to this field too. More specifically, to date, a linguistic challenge is to extract concepts that involve implicit language, such as memes, irony, or other non-literal language, in an automated fashion. To be able to make progress on this problem, there is a need for relevant examples from different scientific domains. Representative claims are an ideal case because often parts of these claims are implicit. The second goal is hence to use the detection and classification of representative claims as a case to advance models of automated extraction of implicit concepts. Substantive insights from political science will help to set the requirements that the models need to meet. The collaboration between the two fields is essential to make gains in each domain separately. Taken together, the project will allow us to examine the incidence and effects of representative claims on an unprecedented scale, and will thus result in better knowledge of a phenomenon that is crucial to understand (the crisis of) representative democracy itself.
Date:1 Jan 2022 →  Today
Keywords:INFORMATION EXTRACTION, POLITICAL REPRESENTATION, REPRESENTATIVE CLAIMS, POLITICAL COMMUNICATION
Disciplines:Political communication, Public opinion, Party politics, Computational linguistics