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.

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.

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.

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 ¼ 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.

Deepfakes are perceived as a powerful form of disinformation. Although many studies have focused on detecting deepfakes, few have measured their effects on political attitudes, and none have studied microtargeting techniques as an amplifier. We argue that microtargeting techniques can amplify the effects of deepfakes, by enabling malicious political actors to tailor deepfakes to susceptibilities of the receiver. In this study, we have constructed a political deepfake (video and audio), and study its effects on political attitudes in an online experiment ( N = 278). We find that attitudes toward the depicted politician are significantly lower after seeing the deepfake, but the attitudes toward the politician’s party remain similar to the control condition. When we zoom in on the microtargeted group, we see that both the attitudes toward the politician and the attitudes toward his party score significantly lower than the control condition, suggesting that microtargeting techniques can indeed amplify the effects of a deepfake, but for a much smaller subgroup than expected.

Prompted by the ongoing development of content personalization by social networks and mainstream news brands, and recent debates about balancing algorithmic and editorial selection, this study explores what audiences think about news selection mechanisms and why. Analysing data from a 26-country survey (N ¼ 53,314), we report the extent to which audiences believe story selection by editors and story selection by algorithms are good ways to get news online and, using multi-level models, explore the relationships that exist between individuals' characteristics and those beliefs. The results show that, collectively, audiences believe algorithmic selection guided by a user's past consumption behaviour is a better way to get news than editorial curation. There are, however, significant variations in these beliefs at the individual level. Age, trust in news, concerns about privacy, mobile news access, paying for news, and six other variables had effects. Our results are partly in line with current general theory on algorithmic appreciation, but diverge in our findings on the relative appreciation of algorithms and experts, and in how the appreciation of algorithms can differ according to the data that drive them. We believe this divergence is partly due to our study's focus on news, showing algorithmic appreciation has context-specific characteristics.

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.

How to study media diversity has become a major concern in today's media landscape. Many expect that algorithmic filtering and a shift of audiences from legacy media to new intermediaries decrease the diversity of news diets, leading to fragmented societies, polarization and spread of misinformation. Different fields, from journalism research to law and computer science, are involved in the study of media diversity. They operate, however, with vastly different vocabularies, frameworks, and measurements. To overcome this fragmentation, this study provides an extensive overview of conceptualizations and operationalizations of media diversity in different fields using a systematic literature review (1999-2018). This showed a lack of theorizing and linking of conceptual with empirical work in media diversity research. Based on this, we develop a framework on how to move forward: Regarding conceptualization, we call for focusing on different places in the journalistic information chain instead of the classical exposure-supply distinction. Methodologically, automated approaches (e.g., analyzing digital traces) and qualitative approaches (e.g., capturing perceptions of diversity) should receive more attention. For analysis, matters of balance and disparity need to be stressed more, especially discussing possible limits to diversity. Overall, research into media diversity thus needs to be addressed in interdisciplinary collaboration.