The Research Priority Area Communication and its Digital Communication Methods Lab are happy to announce that three proposals have been selected for the third edition of the Digicomlab Thesis Funding Grants. These grants provide financial support for theoretically-relevant and digitally innovative (research) master theses written at the University of Amsterdam’s Graduate School of Communication for semester 2 of the 2019-2020 academic year.
When the smartphone takes over: The roles of goal conflict and autonomy appraisals in eliciting digital stress
In today’s media-saturated environments, the use of mobile digital devices like smartphones is ubiquitous, leading users to be permanently online and connected to others. Both cognitive (i.e. online vigilance) and behavioural components (i.e. communication load, media multitasking) of permanent connectedness can elicit stress (i.e. digital stress), which is detrimental to individuals’ well-being and health. The present research deepens the understanding of underlying mechanisms of digital stress by considering the boundary conditions of goal conflict and autonomy appraisals which have been linked to indicators of permanent connectedness (i.e. availability demands, instant messenger use) in the past. It thus integrates two streams of research: Media stressors identified in the digital stress literature are put in context of intervening factors stemming from self-determination theory and research on media use for basic need satisfaction. With an experience-sampling design spanning seven days, indicators of permanent connectedness and stress are observed in a situational context. Concepts can thus be measured in an application- and context-specific manner with only short time lag between a behaviour and its measurement, reducing biases that have previously skewed measurements of e.g., digital media use. Furthermore, temporal fluctuations of effects can be analysed, which is so far scarce in digital stress research and can guide the creation of digital stress interventions.
Is quality journalism going bankrupt? An automated comparison of news quality indicators among political news in German print and online as well as national and regional newspapers
News outlets’ adherence to normative standards for news quality, such as diversity, impartiality, and comprehensibility (Urban & Schweiger, 2014), is crucial for society, as it determines how well news outlets can contribute to a healthy and deliberative democracy (Strömbäck, 2005). In light of worries about the impact that increased online readership (Burggraaff & Trilling, 2017) and the ongoing commercialisation of news production has on news quality (Jacobi, Kleinen-von Königslöw, & Ruigrok, 2016), it is both timely and relevant to examine if and how adherence to these standards differs depending on modality (online vs. offline) and reach (national vs. regional). To move the field methodologically forward, this thesis will develop a comprehensive framework for automatically assessing news quality in German newspapers and examine potential differences between different types of outlets. This will be done by a) combining existing automated news quality indicators, and b) advancing the automated measurement of impartiality by means of supervised machine learning (SML). In a field that still largely relies on manual content analyses, doing so offers new insights into how and to what extent news quality can be measured in an automated manner. The results will not only provide important insights into the current state of news quality in Germany, but also contribute to the field by addressing important questions about both the opportunities and limitations of automated research methods (Boyd & Crawford, 2012) and by developing an overall framework, as well as classifiers and a training dataset for impartiality that future studies can build on.
Emotions as the Impetus of Negative Campaigning Effects. Investigating Voters’ Perception of Campaign Negativity, Voter Turnout, and Vote Intention
So far, research on negative campaigning has produced inconsistent findings on the effects of negativity on voter turnout and voter intention. Emerging evidence suggests that these inconsistencies stem, at least in part, from the fact that the effects of campaign negativity are a function not of some absolute value of negativity of a political ad – the usual subject of analysis, but rather of a person’s perception of it. The present study addresses this possibility by analyzing the emotions expressed in the UK public’s online discourse around the 2019 UK general election. Moving beyond the customary (sentiment) analysis of the candidates’ rhetoric, natural language processing and supervised machine learning will be employed to estimate the public’s emotional reactions to the candidates’ political campaigns. The estimates wil utilized in three ways. On the most aggregate level, a time series analysis will be conducted to examine the relationship between daily average values of emotions expressed towards a given party and that party’s position in the polls. Constituency-level data will be used to model a relationship between a given constituency’s electorate’s emotional state on the election date and voter turnout in that constituency. Lastly, sentiment expressed in reaction to individual candidates’ campaigns will be compared to the levels of negativity expressed by the candidates themselves. These analyses will elucidate how the conventional measures of negativity in campaigns compare to the public’s perception of it, and how discrete emotions experienced by the electorate affect the electorate’s voting choices.