Results of the 2nd Semester of DigicomLab Thesis Grants 2019-2020

Three exciting research projects have been conducted as part of (research) master theses written at the University of Amsterdam’s Graduate School of Communication in semester 2 of the 2019-2020 academic year that received funding through the third edition of the Thesis Funding Grants. Alicia Gilbert, Nicolas Mattis, and Vladislav Petkevic have conducted digitally innovative studies, covering the important topics of smartphone-imputed stress, political news quality, and negative campaign effects. You can read about the results of their studies here.

The next round of the Thesis Funding Grants is going online at the end of August 2020, stay tuned for updates!

Three proposals selected for the Digicomlab thesis grants this semester

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

Alicia Gilbert

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

Nicolas Mattis

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

Vladislav Petkevic

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.

Selected proposals for Thesis Funding Grants announced!

The Digital Communication Methods Lab is happy to announce the selected proposals for the first edition of the Thesis Funding Grants. These grants provide provide (financial) support for theoretically-relevant and digitally innovative (research) master theses for semester 2 of the 2018-2019 academic year.

Cultivating perceptions of credibility in the context of online conversational agents: the role of similarity in personality and expertise claims

by Eirine Ntaligkari

The adoption of conversational agents (CAs) as social actors that can substitute other humans (Zhao, 2003) is a new form of corporate communication between stakeholders and organizations, as technological advances continue to drive CAs’ capabilities significantly. An interesting scope capacity of CAs is personalization of services, that allows organizations to raise their business intelligence and provide customized products or services, and users to enjoy tailored services that have been found to have multiple benefits (Wang & Li, 2012; Godey et al., 2016; Perna, Runfola, Temperini, & Gregoni, 2018). To achieve this, CAs need to acquire personal information, which users are rather sceptical to disclose due to uncertainty related to security and privacy risks (Aguirre, Roggeveen, Grewal & Wetzels, 2016).

In order for users to feel more comfortable to disclose such information to CAs, trust and credibility must be present. Nonetheless, credibility and its antecedents (namely, trustworthiness and expertise) are values that form gradually and are difficult to acquire in an online environment, especially through micro interactions such as the ones mentioned above. The assessment of credibility and the effects of its antecedents in such environments remains unresearched. By utilizing an CA created with the aid of the Conversational Agent Research Toolkit (CART) of UvA’s Digital Communication Methods Lab, this thesis aims to answer the following question: How can trustworthiness and expertise be embedded in CAs in a meaningful way, in order for an organization to influence not only the source, but also their corporate credibility, and therefore privacy and security risk perceptions?

How can we characterize disinformation in online news? Developing machine learning classifiers for examining structural differences in U.S. and Russian state-backed news in Serbia

by Ognjan Denkovski

Democratic nations globally are experiencing increasing levels of false and misleading information circulating through social media and political websites, often propagating alternative socio-political realities. One of the main actors in this process has been the Russian state, whose organized disinformation campaigns have influenced elections throughout the Western world. A key element of these campaigns has been the dissemination and spread of content produced by outlets like RT and Sputnik – content thereafter spread by underfunded local media and organized online networks which attempt to shape mainstream political narratives. In response to a lack of comprehensive research examining the characteristics of such content, this paper examines whether, and if so how, content produced by Russian websites like Sputnik is structurally distinct from that of mainstream Western outlets. Through text-as-data methods we examine: a) the stylistic and thematic differences in content produced by U.S. and Russian backed outlets in Serbia, a key geopolitical interest for both states and b) which features, if any, of Russian news can be used to characterize content as part of disinformation campaigns. These findings are used for the development and evaluation of supervised machine learning classifiers. The paper contributes towards an understanding of the structural characteristics of disinformation and online political polarization in a novel context – Balkan online news – while also forwarding the application of text-as-data methods in Serbian. Ideally, the project will allow for the development of an online suspicious news identifier for the Balkan languages.

Is it a big problem or not? An analysis of fake news diffusion on social media during the Brazilian 2018 presidential elections

by Pieter Attema Zalis

The goal is to provide new evidences on how ‘fake news’ spreads in social media during electoral campaigns. This study will analyse the subject in the context of the 2018’s Brazilian presidential election. As it occurred in the US with Trump, Brazil elected an unlikely candidate, Jair Bolsonaro. Due to Bolsonaro’s anti-establishment populist style and social media presence, international media outlets nicknamed him as the “Tropical Trump”.

This study has three main goals. First, following previous studies in the US, I’ll analyse if ideology (left vs right) might moderate the differences on fake news diffusion in social media. In the second and third steps, I’ll compare fake news to traditional news. I’ll first look if fake news stories present a more emotional content than traditional news stories and latter investigate if this leads to higher levels of fake news sharing compared to traditional ones. Second, i’ll test, in the Brazilian context, evidences found in Europe and in US that the general audience of fake news is significantly smaller than traditional news. To sum up, I have two research questions about the subject: (1) to what extent are interactions of fake news bigger or smaller than of traditional news in social media? (2) to what extent is the total amount of fake news bigger or smaller than traditional news in social media? I’ll answer these questions with a content analysis of 5,120,892 tweets streamed during the second turn of the presidential election.

Mapping CSR-crisis in issue arenas: applying the Network Agenda-Setting Model in big-data research

by Louelle Jasmin Pesurnaij

In the past decades, more and more plastic is floating in the oceans and seas as a result of disposed plastic products, such as food packaging, fishnets, synthetic clothing, toothbrushes and plastic furniture. In 2011, the Plastic Soup Foundation was founded, whose mission is to tackle the plastic pollution of oceans. Campaigning activities of NGOs such as the Plastic Soup Foundation have proven to increase public awareness and concern regarding social, ethical and environmental issues and have proven to be powerful at setting the public agenda by urging the public towards a pro-environmental stance. However, it remains unclear what the most effective way is to design campaign and/or news messages and how to find the right issues and issue arenas.

The aim of this study is to gain a more complete picture of the agenda-setting role of the media and NGOs in virtual environments. This study seeks to apply a relatively new concept in the field of communications: the Network Agenda-Setting (NAS) model, to research the capability of both news media and NGOs to influence how the public links different messages regarding #PlasticSoup. The NAS model extends traditional agenda-setting research, by asserting that issues and attributes are not just transferred as individual elements but issues and/or attributes are interconnected and transferred in bundles to the public agenda. The research question of this thesis study is as follows: “To what extent are the issue attribute networks regarding #PlasticSoup transferred from the agenda of media and NGOs onto the public agenda?”.

The present study uses innovative, digital research methods, such as automated content analyses of big datasets (including semantic network analysis), to identify issues and issue attributes in tweets about #PlasticSoup and to examine the agenda-setting power of both news media and NGOs.

Paid partnership, #ad or ambiguous hints to brands on Instagram? The impact of sponsorship disclosures, alternative cues and different influencer types on users’ persuasion knowledge

by Céline Marie Müller

Instagram, today’s most relevant platform for influencer marketing has received little scholarly consideration regarding this form of native advertising . Visual attention is a crucial indicator for whether and when people recognize Instagram ads. To date, it is unclear what elements help consumers to identify sponsored content. The few studies that explored users’ responses to sponsorship disclosures focused on different hashtags and Instagram’s platform-based disclosure (Evans, Phua, Lim, & Jun, 2017; Coursaris, Van Osch, & Kourganoff, 2018). But are these disclosures actual key elements that help users recognize sponsored content? Previously, the potential effectiveness of other cues (i.e. brand tags in picture or caption) has been neglected. With an eye-tracking experiment, this study clarifies the cues or combinations that successfully help consumers identify sponsored posts. Furthermore, it detects possible boundary conditions of the different disclosures’ value by distinguishing between posts of macro-, micro- and nano-influencers. A recent study found that users are less likely to identify sponsored posts by micro- compared to macro-influencers (Coursaris et al., 2018). Heretofore, consumers’ ad recognition for sponsored posts by nano-influencers remains underesearched. Based on the eye-tracking findings, a second online experiment elucidates the impact of influencer types and particular disclosures or elements on users’ persuasion knowledge and brand responses. In order to fill the above-mentioned gaps in academia, the thesis seeks to answer the following question:
To what extent do sponsorship disclosures, alternative cues and different influencer types affect users’ visual attention to Instagram posts, their persuasion knowledge and resulting brand responses?