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Combining surveys and digital trace data can enhance the analytic potential of both data types. We present two studies that examine factors influencing data sharing behaviour of survey respondents for different types of digital trace data: Facebook, Twitter, Spotify and health app data. Across those data types, we compared the relative impact of four factors on data sharing: data sharing method, respondent characteristics, sample composition and incentives. The results show that data sharing rates differ substantially across data types. Two particularly important factors predicting data sharing behaviour are the incentive size and data sharing method, which are both directly related to task difficulty and respondent burden. In sum, the paper reveals systematic variation in the willingness to share additional data which need to be considered in research designs linking surveys and digital traces.

Although other social media platforms have seen a steeper increase in users recently, Facebook is still the social networking site with the largest number of users worldwide. A large number of studies from the social and behavioral sciences have investigated the antecedents, types, and consequences of its use. In addition or as an alternative to self-reports from users, many studies have used data from the platform itself, usually collected via its Application Programming Interfaces (APIs). However, with the drastic reduction of data access via the Facebook APIs following the Cambridge Analytica scandal, this data source has essentially become unavailable to academic researchers. Hence, there is a need for different modes of data access for what Freelon (2018) has called the 'post-API age'. One promising approach is to directly collaborate with platform users to ask them to share (parts of) their personal Facebook data with researchers. This paper presents experiences from two studies employing such approaches. The first used a browser plugin to unobtrusively observe Facebook use while users are active. The second asked participants to export and share parts of their personal Facebook data archive. While both approaches yield promising insights suitable to extend or replace self-reports, both also entail specific limitations. We discuss and compare the unique advantages and limitations of both approaches and provide a list of recommendations for future research.

Given that Facebook is still the most widely used social networking site in the world, its influence on democratic processes is under constant scrutiny. Academics have put a special focus on Facebook's role in inhibiting or enhancing citizens' news exposure. Recent studies using digital behavioral data have analyzed the prevalence and effects of "Facebook news referrals". Using a web tracking tool that captures general browsing behavior as well as public posts seen on Facebook, this paper lays the groundwork for the field by assessing the validity of previously proposed operationalizations. We validate news referrals by investigating whether different measures actually reflect exposure to a news URL a user saw on Facebook. We furthermore assess the effects of news referrals on central outcomes in extant literature, contingent on different operationalizations. The results show that the most precise measure of news referrals are referral IDs attached as parameters to news URLs by Facebook. Still, the substantive findings are broadly comparable across approaches, lending further credibility to published research, despite measurement errors. The paper demonstrates the need for academics to constantly innovate in order to measure citizens' online behavior in an ecologically valid manner.

How is political news shared online? This fundamental question for political communication research in today’s news ecology is still poorly understood. In particular, very little is known about whether and how news sharing differs from news viewing. Based on a unique dataset of ≈ 870,000 URLs shared ≈ 100 million times on Facebook, grouped by countries, age brackets, and months, we study the correlates of viewing versus sharing of political versus non-political news. We first identify websites that at least occasionally contain news items, and then analyze metrics of the news items published on these websites. We enrich the dataset with natural language processing and super- vised machine learning. We find that political news items are viewed less than non-political news items, but are shared more than one would expect based on their views. Furthermore, the source of a news item and textual features, which are often studied in clickbait research and in commercial A/B testing, matter. Our findings are conditional on age, but are very similar across four different countries (Italy, Germany, Netherlands, Poland). While our research design does not allow for causal claims, our findings suggest that future work is well-advised to both theoretically and methodologically differentiate between factors that may explain (a) viewing versus sharing of news, and (b) political versus non-political news.

How is political news shared online? This fundamental question for political communication research in today’s news ecology is still poorly understood. In particular, very little is known about whether and how news sharing differs from news viewing. Based on a unique dataset of ≈ 870,000 URLs shared ≈ 100 million times on Facebook, grouped by countries, age brackets, and months, we study the correlates of viewing versus sharing of political versus non-political news. We first identify websites that at least occasionally contain news items, and then analyze metrics of the news items published on these websites. We enrich the dataset with natural language processing and super- vised machine learning. We find that political news items are viewed less than non-political news items, but are shared more than one would expect based on their views. Furthermore, the source of a news item and textual features, which are often studied in clickbait research and in commercial A/B testing, matter. Our findings are conditional on age, but are very similar across four different countries (Italy, Germany, Netherlands, Poland). While our research design does not allow for causal claims, our findings suggest that future work is well-advised to both theoretically and methodologically differentiate between factors that may explain (a) viewing versus sharing of news, and (b) political versus non-political news.

How transnational are European Parliament (EP) campaigns? Building on research on the Euro-pean public sphere and the politicisation of the EU, this study investigates to what extent the 2019 EP campaign was transnational and which factors were associated with ‘going transna-tional’. It conceptualises Twitter linkages of EP candidates as constitutive elements of a transna-tional campaign arena distinguishing interactions with EP candidates from other countries (hori-zontal transnationalisation) and interactions with the supranational European party families and lead candidates (vertical transnationalisation). The analysis of tweets sent by EP candidates from all 28 member states reveals that most linkages remain national. Despite this evidence for the second-order logic, there are still relevant variations contingent on EU positions of parties, the adoption of the Spitzenkandidaten system and socialisation in the EP. The findings have impli-cations for debates on the European public sphere and institutional reform proposals such as transnational party lists that might mitigate the EU’s democratic deficit.

The Spitzenkandidaten were meant to personalize European Parliament elections. This paper asks whether and through which channels the lead candidates were actually able to make themselves known among voters – a necessary precondition for any electoral effect. Combining panel surveys and online tracking data, the study explores candidate learning during the German 2019 European Parliament election campaign and relates learning to different types of news exposure, with a special focus on online news. The results show that learning was limited and unevenly distributed across candidates. However exposure to candidate-specific online news and most types of offline news helped to acquire knowledge. The findings imply that Spitzenkandidaten stick to voters’ minds when they get exposed to them, but that exposure is infrequent in high-choice media environments.

This article tests the assumption that candidates’ attack behavior is a result of their rational consideration of potential benefits and likely risks. Based on candidate surveys from three German state elections, we demonstrate that (i) attacks are an important strategy; (ii) on balance, candidates regard attacking opponents as a costly instead of a beneficial strategy; (iii) the differential between benefits and costs is positively associated with attack behavior; nevertheless, most candidates attack at least sometimes even when costs exceed benefits; (iv) candidate characteristics and the electoral context are rarely reflected in benefit-cost calculations; and (v) the theoretically assumed mediating role of the benefit-cost differential on attack behavior applies only to some explanatory factors. While the findings provide some evidence for rational choice explanations of negative campaigning, they also challenge some central assumptions. As such, they demonstrate the need for more comprehensive theoretical explanations and measurements of negative campaigning.