Understanding how citizens keep themselves informed about current affairs is crucial for a functioning democracy. Extant research suggests that in an increasingly fragmented digital news environment, search engines and social media platforms promote more incidental, but potentially more shallow modes of engagement with news compared to the act of routinely accessing a news organization’s website. In this study, we examine classic predictors of news consumption to explain the preference for three modes of news engagement in online tracking data: routine news use, news use triggered by social media, and news use as part of a general search for information. In pursuit of this aim, we make use of a unique data set that combines tracking data with survey data. Our findings show differences in predictors between preference for regular (direct) engagement, general search-driven, and social media–driven modes of news engagement. In describing behavioral differences in news consumption patterns, we demonstrate a clear need for further analysis of behavioral tracking data in relation to self-reported measures in order to further qualify differences in modes of news engagement.

This paper presents results from three studies on science blogging, the use of blogs for science communication. A survey addresses the views and motives of science bloggers, a first content analysis examines material published in science blogging platforms, while a second content analysis looks at reader responses to controversial issues covered in science blogs. Bloggers determine to a considerable degree which communicative function their blog can realize and how accessible it will be to non-experts Frequently readers are interested in adding their views to a post, a form of involvement which is in turn welcomed by the majority of bloggers.

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Sentiment analysis is an increasingly popular instrument for the analysis of social media discourse. Sentiment scores seemingly represent an objective means of assessing the mood of social media users, consumers, and the public at large. Similar to other computational tools, sentiment analysis promises to reduce complexity and mitigate information overload, and to inform the decisions of marketers, pollsters, and scholars with reliable data. This article argues that the assumptions encoded into sentiment analysis as a method are accompanied by a number of constraints, both regarding its technical limitations (in terms of what sentiment analysis can and cannot accomplish) and conceptually (in terms of what the notion of sentiment implicitly represents), constraints which are often de-emphasized in public discourse. After providing an overview of its history and development in computer science as well as psychology and the social sciences, we turn to the role of sentiment as a currency in the attention economy. We then present a brief study of common framing of sentiment analysis in the news media, highlighting the expectations that exist regarding its analytical capabilities. We close by discussing the kind of conceptual work that takes place around computational methods such as sentiment analysis in specific cultural environments, highlighting their influence on the public imaginary.

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