What do ongoing changes in the media environment, notably the perceived popularization of news and the shift towards individualized
online media, mean for political news quality, both in terms of what it is, as well as how we measure it? This dissertation
firstly argues, based on a literature review showing the benefits and risks of popular and online news for democracy, that
news quality standards should be more lenient towards popular news elements. These elements, such as relatively low complexity,
a focus on leaders and a more emotional style, may increase political interest and knowledge among specific audience groups.
In the online environment characterized by information overload and vulnerable to bias as a consequence of audience preference
tracking, drawing people’s attention and keeping it is arguably just as important as transmitting political information. Secondly,
a content analysis of political news in Austrian online and print newspapers during the 2013 election campaign shows that
in print, elite and popular newspapers perform rather similar across quality indicators, including content complexity. However,
online, popular newspapers perform worse, even if we emphasize involvement alongside information. Lastly, the dissertation
discusses automatic content analysis methods, and demonstrates the use of topic modelling, and inductive method for exploring
themes in the news. Although allowing the analysis of larger data sets, automatic content analysis is still limited in the
news quality indicators it can measure. Further research should refine existing methods for communication studies goals, and
find ways to effectively combine automatic and human coding.