Damian Trilling

Two Different Debates? Investigating the Relationship Between a Political Debate on TV and Simultaneous Comments on Twitter

While watching television, more and more citizens comment the program live on social media. This is especially interesting in the case of political debates, as viewers’ comments might not only allow us to tap into public opinion, but they can also be an influential factor of their own and contribute to public discourse. This article analyzes how the TV debate between the candidates for chancellor during the German election campaign 2013 was discussed on Twitter. To do so, the transcript of the debate is linked to a set of N = 120,557 tweets containing the hashtag #tvduell. The results indicate that the candidates were only to a minor degree successful in getting their topics to the Twitter debate. An optimistic reading of the results suggests that Twitter serves as a complement to draw attention to topics neglected in the official debate. A more pessimistic reading would point to the fact that the discourse on Twitter seems to be dominated by sarcastic or funny rather than by substantial content.

Taking stock of the toolkit: an overview of relevant automated content analysis approaches and techniques for digital journalism scholars

When analyzing digital journalism content, journalism scholars are confronted with a number of substantial differences compared to traditional journalistic content. The sheer amount of data and the unique features of digital content call for the application of valuable new techniques. Various other scholarly fields are already applying computational methods to study digital journalism data. Often, their research interests are closely related to those of journalism scholars. Despite the advantages that computational methods have over traditional content analysis methods, they are not commonplace in digital journalism studies. To increase awareness of what computational methods have to offer, we take stock of the toolkit and show the ways in which computational methods can aid journalism studies. Distinguishing between dictionary-based approaches, supervised machine learning, and unsupervised machine learning, we present a systematic inventory of recent applications both inside as well as outside journalism studies. We conclude with suggestions for how the application of new techniques can be encouraged.

Skipping current affairs: The non-users of online and offline news

In an information-rich environment with ample choice, do citizens still get exposed to what is going on around them in society? Or do they become ‘information hermits’, only interested in their personal hobbies? In contrast to widespread fears, the results of a large-scale survey, representative for the population of the Netherlands, suggest that most citizens still get an overview of what is going on in the world, and that television news is still the most popular source for that information. In addition, news on the Internet reaches those who are unlikely to seek news offline and wish to be entertained instead of informed. In detail, the study examines (1) which factors influence total news-overview avoidance, but also (2) what determines the amount of news exposure for those who do not skip the news.

From newsworthiness to shareworthiness : How to predict news sharing based on article characteristics

People increasingly visit online news sites not directly, but by following links on social network sites. Drawing on news value theory and integrating theories about online identities and self-representation, we develop a concept of shareworthiness, with which we seek to understand how the number of shares an article receives on such sites can be predicted. Findings suggest that traditional criteria of newsworthiness indeed play a role in predicting the number of shares, and that further development of a theory of shareworthiness based on the foundations of newsworthiness can offer fruitful insights in news dissemination processes.

Should We Worry About Filter Bubbles

Some fear that personalised communication can lead to information cocoons or filter bubbles. For instance, a personalised news website could give more prominence to conservative or liberal media items, based on the (assumed) political interests of the user. As a result, users may encounter only a limited range of political ideas. We synthesise empirical research on the extent and effects of self-selected personalisation, where people actively choose which content they receive, and pre-selected personalisation, where algorithms personalise content for users without any deliberate user choice. We conclude that at present there is little empirical evidence that warrants any worries about filter bubbles.

Investigating people’s news diets: how online news users use offline news

Abstract The question how offline media use is related to online media use has been heavily debated in the last decades. If they are functionally equivalent, then advantages like low costs, rapid publication cycles, and easy access to online news could lead to them displacing offline news. Data from a large-scale survey with detailed questions about media use in the Netherlands show that, interestingly, the functions that online and offline media are used for are often the same: Those who use online media to gain a broad overview of the news, for fast updates, or for background information use offline media for the same purpose. There are some differences, though: For many citizens, the need of a broad overview of the news seems to be fulfilled by repertoires consisting of several outlets of different types, while they seem to have favorite specific outlets for news updates or background information, respectively. This suggests that outlets can especially focus on the latter two functions to distinguish themselves.

The Role of Media Coverage in Explaining Stock Market Fluctuations: Insights for Strategic Financial Communication

ABSTRACT This study investigates the reciprocal relationships between the fluctuation of the closing prices of three companies listed on the Amsterdam exchange index, namely ING, Philips and Shell and online media coverage related to these firms for a period of two years (2014–2015). Automated content analysis methods were employed to analyze sentiment and emotionality and to identify corporate topics related to the companies. A positive relation of the amount of coverage and emotionality with the fluctuation of stock prices was detected for Shell and Philips. In addition, corporate topics were found to positively Granger cause stock price fluctuation, particularly for Philips. The study advances past research in showing that the prediction of stock price fluctuation based on media coverage can be improved by including sentiment, emotionality, and corporate topics. The findings inform strategic communication, and particularly investor relations, in suggesting that media attention, sentiment, and certain corporate topics are crucial when managing media relations and with regard to securing a fair evaluation of listed companies. Furthermore, the innovative research methods are useful for researchers and practitioners alike in showcasing how media coverage related to firms and their stock fluctuations can be identified and analyzed in a reproducible, hands-on and efficient manner.

Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity

ABSTRACT In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.

Through a different gate: An automated content analysis of how online news and print news differ

We investigate how news values differ between online and print news articles. We hypothesize that print and online articles differ in terms of news values because of differences in the routines used to produce them. Based on a quantitative automated content analysis of N = 762,095 Dutch news items, we show that online news items are more likely to be follow-up items than print items, and that there are further differences regarding news values like references to persons, the power elite, negativity, and positivity. In order to conduct this large-scale analysis, we developed innovative methods to automatically code a wide range of news values. In particular, this article demonstrates how techniques such as sentiment analysis, named entity recognition, supervised machine learning, and automated queries of external databases can be combined and used to study journalistic content. Possible explanations for the difference found between online and offline news are discussed.

More or less diverse: An assessment of the effect of attention to media salient company types on media agenda diversity in Dutch newspaper coverage between 2007 and 2013

This study on news coverage of highly visible company types in a Dutch daily quality newspaper (NRC Handelsblad; N = 14,363), during the economic crisis (2007–2013), shows that attention to banks (and to a lesser extent also to the automobile and components industry) had a structural negative influence on media agenda diversity. The majority of the other salient company types had a significant positive impact on diversity. These results suggest that banks attracted attention at the expense of more varied, diverse coverage during the crisis. Our findings extend knowledge of agenda-building dynamics in relation to organizational news by considering characteristics of the broader media agenda. We discuss our findings in light of causes and consequences of media coverage of salient businesses.