The early signs are that Belgium is heading for more political deadlock over who should form the next government

Belgium held federal elections in May, with negotiations currently on-going over the makeup of the next government. As Peter Van Aelst writes, a key concern is that the country could experience political deadlock of the kind which occurred after the 2010 elections, where it took 541 days of negotiations before a government could be formed. He notes that while there appears to be more urgency than there was in 2010, the linguistic cleavage between French and Dutch-speaking parties will still be exceptionally difficult to overcome

Who is leading the campaign charts? Comparing individual popularity on old and new media

Traditionally, election campaigns are covered in the mass media with a strong focus on a limited number of top candidates. The question of this paper is whether this knowledge still holds today, when social media outlets are becoming more popular. Do candidates who dominate the traditional media also dominate the social media? Or can candidates make up for a lack of mass media coverage by attracting attention on Twitter? This study addresses these question by paring Twitter data with traditional media data for the 2014 Belgian elections. Our findings show that the two platforms are indeed strongly related and that candidates with a prominent position in the media are generally also most successful on Twitter. This is not because more popularity on Twitter translates directly into more traditional media coverage, but mainly because largely the same political elite dominates both platforms

Who is leading the campaign charts? Comparing individual popularity on old and new media

Traditionally, election campaigns are covered in the mass media with a strong focus on a limited number of top candidates. The question of this paper is whether this knowledge still holds today, when social media outlets are becoming more popular. Do candidates who dominate the traditional media also dominate the social media? Or can candidates make up for a lack of mass media coverage by attracting attention on Twitter? This study addresses these question by paring Twitter data with traditional media data for the 2014 Belgian elections. Our findings show that the two platforms are indeed strongly related and that candidates with a prominent position in the media are generally also most successful on Twitter. This is not because more popularity on Twitter translates directly into more traditional media coverage, but mainly because largely the same political elite dominates both platforms

High-Breakdown Robust Multivariate Methods

When applying a statistical method in practice it often occurs that some observations deviate from the usual assumptions. However, many classical methods are sensitive to outliers. The goal of robust statistics is to develop methods that are robust against the possibility that one or several unannounced outliers may occur anywhere in the data. These methods then allow to detect outlying observations by their residuals from a robust fit. We focus on high-breakdown methods, which can deal with a substantial fraction of outliers in the data. We give an overview of recent high-breakdown robust methods for multivariate settings such as covariance estimation, multiple and multivariate regression, discriminant analysis, principal components and multivariate calibration.Comment: Published in at http://dx.doi.org/10.1214/088342307000000087 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

What Politicians Look for in the News and How That Affects Their Behavior : A Uses and Gratifications Approach to Political Agenda Setting

Peer reviewe

Identifying the Drivers Behind the Dissemination of Online Misinformation: A Study on Political Attitudes and Individual Characteristics in the Context of Engaging With Misinformation on Social Media

The increasing dissemination of online misinformation in recent years has raised the question which individuals interact with this kind of information and what role attitudinal congruence plays in this context. To answer these questions, we conduct surveys in six countries (BE, CH, DE, FR, UK, and US) and investigate the drivers of the dissemination of misinformation on three noncountry specific topics (immigration, climate change, and COVID-19). Our results show that besides issue attitudes and issue salience, political orientation, personality traits, and heavy social media use increase the willingness to disseminate misinformation online. We conclude that future research should not only consider individual’s beliefs but also focus on specific user groups that are particularly susceptible to misinformation and possibly caught in social media “fringe bubbles.

The contingency of voter learning: how election debates influence voters’ ability and accuracy to position parties in the 2010 Dutch election campaign

Election campaigns are expected to inform voters about parties’ issue positions, thereby increasing voters’ ability to influence future policy and thus enhancing the practice of democratic government. We argue that campaign learning is not only contingent on voters’ characteristics and different sources of information, but also on how parties communicate their issue positions in election debates. We combine a two-wave panel survey with content analysis data of three televised election debates. In cross-classified multilevel auto-regression models we examine the influence of these debates in the 2010 Dutch parliamentary election campaign on voters’ knowledge of the positions of eight parties on three issues. The Dutch multiparty system allows us to separate voters’ ability to position parties from their accuracy in ordering these parties. We reach three main conclusions. First, this study shows that voters become more able and accurate during the campaign. However, these campaign learning effects erode after the elections. Second, whereas voters’ attention to campaigns consistently contributes to their ability to position parties, its effect on accuracy is somewhat less consistent. Third, televised election debates contribute to what voters learn. Parties that advocate their issue positions in the debates stimulate debate viewers’ ability to position these parties on these issues. In the face of the complexity of campaigns and debates in multiparty systems, campaigns are more likely to boost voters’ subjective ability to position parties than their accuracy

Prediction of coronary artery disease using urinary proteomics

Aims: Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. Methods and results: Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78–0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66–0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47–0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80–0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26–1.89, P \u3c 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25–0.95, P = 0.001; 0.64, 95% CI: 0.28–0.98, P = 0.001, correspondingly). Conclusion: A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention