What is people’s general outlook on the future? People often state that “things aren’t what they used to be”, or even “things are going to the dogs”, especially when referring to important contexts such as politics, culture, or social cohesion. Does that mean that people, by nature, in general or on average, are pessimistic?

It is well-established that individuals tend to prefer well-known things over unknown things. Through mere exposure (Zajonc, 1968), people form positive attitudes toward objects, symbols, and people that surround them daily – familiarity breeds liking. Because past events are more familiar than future events, it would make sense to grow fond of the past, and be wary of the future.

However, other mechanisms suggest that people might be positive about the future. Weinstein (1980) found that individuals generally believe to have less risk of experiencing negative events than others. Recently, Thorbjornson et al. (2015) showed that evaluations of fictitious future products were more positive than of current products. Also, construal level theory implies that people’s assessments of things that are further away in space, social distance, or time are more affect-laden and therefore possibly more positive (Trope & Liberman, 2010).

In line with the latter, we observed that sentiment scores (as provided by monitoring service Coosto) of social media messages referring to a future time (“tomorrow”) are more positive than those referring to the past (“yesterday”). This observation seems to be reoccurring over different time lags (“next/last week/month”) and time periods.

Because we believe determining whether people actually have a generic “optimistic” bias is an important stepping stone in explaining people’s behavior (e.g. stock trading, risk taking, voting, or planning) and their narratives, we need to establish whether we can validate the tentative Coosto results by (1) systematic and reliable computational linguistic analysis and (2) experimental studies.

The project addresses the longstanding problem of linguistic dating of the Psalms. We will investigate the novel idea of analyzing the text by elaborating on techniques and concepts from the analysis of social networks. Specifically, a probabilistic model (Markov chain) will be developed that captures the linguistic structure of the ‘textual network’.

Phase 1 An abstraction of the text (e.g., should conjugations be represented by the same node, singular- plural distinction etc.?) is developed. The project will benefit from the richly annotated linguistic ETCBC database.
We will interpret the text elements obtained in Phase 1 as ‘agents’ in a social network and will seek to identify communities/cliques in the text-network. The rationale is that text parts that originate from different time periods show distinguishable patterns (e.g., homopholy in the text-network).

Phase 2 A Markov chain analysis of the text-network is carried out elaborating on recent findings in Markov chain theory for cluster identification, where we make use of community measures such as expected distance between text elements. An important issue is whether Early Biblical Hebrew books (EBH; e.g., Judges, Samuel, according to most scholars composed before the Babylonian Exile) and Late Biblical Hebrew books (LBH; e.g., the books of Chronicles, unmistakably post-exilic) have enough inner consistency and are separated enough from each other to be able to classify a text of unknown date (e.g., the ‘timeless’ poetry of the Psalms) to one of these language phases.

We believe that this research can have groundbreaking implications for the identification of text types in library search (e.g., find all poems with ‘hope’ and ‘Monday’ as joint topics). While Markov chains have been successfully applied to automatic translation, this project will explore the novel application to understanding the linguistic structure of a text.

About 5 to 7% of all children have a language impairment1. Their language acquisition is severely hampered, mostly at the level of sound articulation, word formation and/or sentence building. Diagnosticians (speech language therapists and clinical linguists) face the challenge of diagnosing children as young as possible (2-4 years old), also when their home language (HL) is not Dutch. Their achievements on standard (Dutch) language tests will not be reliable indicators for a language impairment (LI). The earlier a LI is recognized, the better, so there is no time to wait until the children master Dutch well enough to be tested by means of Dutch tests. Since a language impairment is a neurological condition and not a language-specific problem, bilingual children with a language impairment encounter problems in both languages. If diagnosticians had access to information on the language development in the HL of these children, this would be tremendously helpful in the diagnostic process.
This project aims to develop a knowledge base (KB) -in a co-creation design process involving an applied linguistics and computer science student- This KB will collect relevant information on the specificities of 60 different home languages (normal and atypical language development), and on contrastive analyses of any of these languages with Dutch. To this end, we leverage an existing wiki, with content provided by students of the Applied Linguistics MA-programme.

As humans, we assess Web documents on two dimensions. On the one hand, we evaluate their quality by judging how precise, accurate or neutral the information contained in documents is, or how reliable their sources are based on their reputation. On the other hand, we consider which perspectives are represented in documents. Do the authors present information from their own or someone else’s perspective? How (un)certain are sources about the truth of statements? In turn, perspectivization of information may affect our quality assessments.

In this project, two students will collaborate to improve and enrich the output of an existing tool (Quality Assessment Service) which, at the moment, assesses the quality of Web documents at a very coarse level: documents are rated on a 1-5 likert scale learnt from a set of assessments collected from experts.

Interdisciplinary research is seen as a promising avenue towards new scientific insights. It is therefore heavily advocated for by research institutions and academic funding organizations alike. However, in order to craft policies that effectively promote interdisciplinary research we need to know more about the knowledge flows that happen in academia. The proposed research project aims to study exactly that: knowledge flows in interdisciplinary academic research.

Imagine a research project between a medical researcher and a researcher from computer science. We know that such a collaboration can potentially yield impactful findings. However, we do not know where such a collaboration publishes their outputs. Is it a 50/50 split, is it primarily in computer science, or is it primarily in medical outlets? If one of the latter two cases is true different motivational policies are needed for each of the participating disciplines. And it is very likely, that interdisciplinary collaborations publish heavily towards one of the involved disciplines. In a pilot study at the University of Wuerzburg Sascha Friesike found that collaborations between chemists and biologists are very common but results are almost always published in chemistry journals.

In our proposed study, we want to investigate interdisciplinary research projects on a large scale and figure out in which direction knowledge flows.

One of the major challenges of the 21st century is food waste. We currently waste about 30% of all produced food. In the public debate about food waste, consumers are often targeted as the group that wastes most food. Also, producers waste food during production, which receives media attention. Finally, supermarkets play a key role in the food (waste) chain. While consumers, producers and supermarkets waste food, the important role of signaling to media the problem is often taken up by non-governmental organizations (NGOs) and social movements (SMs). As governments are becoming more hesitant in implementing laws in market sectors, NGOs and SOs rise to take responsibility in signaling problems such as food waste.

The aim of this study is to map the public debate on food waste between 2012 and 2017. We focus on the framing of food waste in newspaper articles and show how consumers, producers, supermarkets and NGOs/SMs are portrayed in the media. This will reveal how the different stakeholders are framed in the media; where this framing overlaps and/or differs; and how the framing changed over time. Our project will contribute to the literature on food waste and corporate social responsibility (discussing NGOs and SMs), because we will show how different stakeholders in the food (waste) chain are framed in the public debate. As opposed to prior studies in this area, we will leverage automated content analysis techniques to produce longitudinal social and semantic network maps. These maps will show how the framing of food waste in the public debate changed over time, tracing the relative positioning of the different stakeholders. Besides the scientific contribution, our project will also deliver valuable insights for the relevant stakeholders. These insights can aid in reducing food waste in supermarkets, during production and in households.