Karsten Donnay

Karsten Donnay
University of Zurich | UZH · Department of Political Science

Dr. sc.

About

48
Publications
13,420
Reads
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916
Citations
Introduction
In my research, I combine a substantive interest in political science with the development and refinement of quantitative methodologies for social science research. In the past years, the main focus of my research has been on understanding the influence of new digital (online) media for political behavior. This includes research on biased media coverage but also on the role of individuals’ perceptions for how such content ultimately affects behavior.
Additional affiliations
April 2020 - present
University of Zurich
Position
  • Professor (Assistant)
November 2016 - March 2020
Universität Konstanz
Position
  • Professor (Assistant)
October 2015 - October 2016
University of Maryland, College Park
Position
  • PostDoc Position
Education
April 2010 - September 2014
ETH Zurich
Field of study
  • Computational Social Science, Political Science
October 2008 - September 2009
August 2007 - June 2008
McGill University
Field of study
  • Physics

Publications

Publications (48)
Preprint
Full-text available
Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality, annotated datasets with only a few manual annotations, thus strongly reducing annotation cost and effort. For t...
Preprint
Full-text available
Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines st...
Preprint
Full-text available
Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models...
Preprint
Full-text available
Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference...
Conference Paper
Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines st...
Preprint
Full-text available
Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution....
Article
Full-text available
The exploration of large‐scale conflicts, as well as their causes and effects, is an important aspect of socio‐political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi‐automatic matching algorithm to integrate event data of different origins into one comprehensive data...
Preprint
Full-text available
Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Many researchers focus on automatically detecting and identifying media bias in the news, but only very few studies exist that systematically analyze how theses biases ca...
Preprint
Full-text available
News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, na...
Preprint
Full-text available
Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news...
Article
Full-text available
Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high conte...
Chapter
Full-text available
Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly. In this paper, we investigate TSC in news articles, a much less researched TSC domain despite the importance of news as an essential information source in individual...
Chapter
Full-text available
Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution....
Chapter
Full-text available
Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news...
Conference Paper
Full-text available
Many people consider news articles to be a reliable source of information on current events. However, due to the range of factors influencing news agencies, such coverage may not always be impartial. Media bias, or slanted news coverage, can have a substantial impact on public perception of events, and, accordingly, can potentially alter the belief...
Preprint
Full-text available
Abstract. Many people consider news articles to be a reliable source of information on current events. However, due to the range of factors influencing news agencies, such coverage may not always be impartial. Media bias, or slanted news coverage, can have a substantial impact on public perception of events, and, accordingly, can potentially alter...
Article
Full-text available
Media outlets strategically frame news about violent events using sensationalist labels such as "terrorist" or "Islamist" but also more subtle wording choices that affect the overall article tone. We argue theoretically and show empirically using a conjoint experiment that, contrary to existing studies, the effect of these two framing devices on re...
Article
Full-text available
Abstract In this paper, we take a novel approach to study the empirical relationship between public debate in the media and asylum acceptance rates in Europe from 2002–2016. In theory, an asylum seeker should experience the same likelihood of being granted refugee status from each of the 20 European countries we study. Yet, in practice, acceptance...
Conference Paper
Full-text available
News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, na...
Conference Paper
Full-text available
Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Many researchers focus on automatically detecting and identifying media bias in the news, but only very few studies exist that systematically analyze how theses biases ca...
Article
Full-text available
Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and s...
Article
Full-text available
Even though human behavior is largely driven by real-time feedback from others, this social complexity is underrepresented in psychological theory, largely because it is so difficult to isolate. In this work, we performed a quasi-experimental analysis of hundreds of millions of chat room messages between young people. This allowed us to reconstruct...
Article
The growing multitude of sophisticated event-level data collection enables novel analyses of conflict. Even when multiple event data sets are available, researchers tend to rely on only one. We instead advocate integrating information from multiple event data sets. The advantages include facilitating analysis of relationships between different type...
Article
Full-text available
The recent data revolution, and with it the emergence of big data on social processes, holds enormous promise as a new paradigm for policy monitoring and evaluation. This is of particular relevance in the context of measuring and monitoring the implementation of the Sustainable Development Goals (SDGs). While there remain many practical and methodo...
Article
Full-text available
In 2011-2012 Russia experienced a wave of mass protests surrounding the Duma and presidential elections. The protests, however, faded shortly after the second election. We study the Russian political discourse on Twitter during this period and the main actors involved: the pro-government camp, the opposition and the general public. We analyse aroun...
Chapter
Full-text available
This chapter provides an overview of cutting-edge research about peace and conflict, focusing on studies from the last five years and especially those published in 2014 and thus far in 2015.1 Our review pays particular attention to recent developments in data and methods that are driven by and also influence theoretical innovation. We consider vibr...
Article
Full-text available
The quantitative study of violent conflict and its mechanisms has in recent years greatly benefited from the availability of detailed event data. With a number of highly visible studies both in the natural sciences and in political science using such data to shed light on the complex mechanisms underlying violent conflict, researchers have recently...
Article
Full-text available
This paper introduces a new method for finding causal relationships in spatiotemporal event data with potential applications in conflict research, criminology, and epidemiology. The method analyzes how different types of interventions affect subsequent levels of reactive events. Sliding spatiotemporal windows and statistical matching are used for r...
Article
Full-text available
We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities an...
Chapter
Full-text available
This chapter takes stock of the emerging research track by providing an overview of notable recent work that disaggregates conflict by its constitutive actors and the timing and location of events. We discuss select insights from these examples, why they challenge results from prior research or the conventional wisdom, and the associated implicatio...
Article
We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities an...
Article
Full-text available
Containing the spreading of crime is a major challenge for society. Yet, since thousands of years, no effective strategy has been found to overcome crime. To the contrary, empirical evidence shows that crime is recurrent, a fact that is not captured well by rational choice theories of crime. According to these, strong enough punishment should preve...
Article
Full-text available
How does segregation shape intergroup violence in contested urban spaces? Should nominal rivals be kept separate or instead more closely integrated? We develop an empirically grounded agent-based model to understand the sources and patterns of violence in urban areas, employing Jerusalem as a demonstration case and seeding our model with microlevel...
Article
Full-text available
Containing the spreading of crime is a major challenge for society. Yet, since thousands of years, no effective strategy has been found to overcome crime. To the contrary, empirical evidence shows that crime is recurrent, a fact that is not captured well by rational choice theories of crime. According to these, strong enough punishment should preve...

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