Do News Actually “Find Me”? Using Digital Behavioral Data to Study the News-Finds-Me Phenomenon

Research on news exposure has shown that while political knowledge and interest largely determine the degree of active engagement with online news, some people are generally less willing to invest into actively staying informed. Instead, these people report to pursue a passive mode of relying on specific sources, such as social media, based on the belief that “news finds me” (NFM). Notably, the three dimensions of NFM—feeling informed, relying on peers, and not actively seeking news—combine intentions and perceptions related to news use. Understanding NFM perceptions, hence, requires an analytical distinction between active and passive modes of news use as well as reliable measures of (different types of) news exposure. We contribute to this field by combining a survey, tracked web-browsing data, and tracked Facebook data to investigate the relationship between NFM perceptions and exposure to online news, also taking into account political knowledge and interest as traditional predictors of active news use. Our results show that both political knowledge and interest are associated with more news exposure via web browsers and that political knowledge—but not political interest—is also associated with more news in people’s Facebook feeds. Compared with the NFM dimensions, political knowledge and interest are stronger predictors of online news exposure in our study. Taken together, the novel combination of Facebook and web tracking data provides evidence that online news exposure is shaped by a confluence of traditional factors and more diffuse interpersonal processes

Hardware-aware block size tailoring on adaptive spacetree grids for shallow water waves.

Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Though they directly yield an adaptive spatial discretisation, i.e. a mesh, it is often more efficient to augment them by regular Cartesian blocks embedded into the spacetree leaves. This facilitates stencil kernels working efficiently on homogeneous data chunks. The choice of a proper block size, however, is delicate. While large block sizes foster simple loop parallelism, vectorisation, and lead to branch-free compute kernels, they bring along disadvantages. Large blocks restrict the granularity of adaptivity and hence increase the memory footprint and lower the numerical-accuracy-per-byte efficiency. Large block sizes also reduce the block-level concurrency that can be used for dynamic load balancing. In the present paper, we therefore propose a spacetree-block coupling that can dynamically tailor the block size to the compute characteristics. For that purpose, we allow different block sizes per spacetree node. Groups of blocks of the same size are identied automatically throughout the simulation iterations, and a predictor function triggers the replacement of these blocks by one huge, regularly rened block. This predictor can pick up hardware characteristics while the dynamic adaptivity of the fine grid mesh is not constrained. We study such characteristics with a state-of-the-art shallow water solver and examine proper block size choices on AMD Bulldozer and Intel Sandy Bridge processors

The overstated generational gap in online news use? A consolidated infrastructural perspective

Recent research by Taneja et al. suggested that digital infrastructures diminish the generational gap in news use by counteracting preference structures. We expand on this seminal work by arguing that an infrastructural perspective requires overcoming limitations of highly aggregated web tracking data used in prior research. We analyze the individual browsing histories of two representative samples of German Internet users collected in 2012 (N = 2970) and 2018 (N = 2045) and find robust evidence for a smaller generational gap in online news use than commonly assumed. While short news website visits mostly demonstrated infrastructural factors, longer news use episodes were shaped more by preferences. The infrastructural role of social media corresponded with reduced news avoidance and more varied news repertoires. Overall, the results suggest that research needs to reconsider commonly held premises regarding the uses of digital media in modern high-choice settings

How social network sites and other online intermediaries increase exposure to news

Research has prominently assumed that social media and web portals that aggregate news restrict the diversity of content that users are exposed to by tailoring news diets toward the users' preferences. In our empirical test of this argument, we apply a random-effects within-between model to two large representative datasets of individual web browsing histories. This approach allows us to better encapsulate the effects of social media and other intermediaries on news exposure. We find strong evidence that intermediaries foster more varied online news diets. The results call into question fears about the vanishing potential for incidental news exposure in digital media environments

User-centric approaches for collecting Facebook data in the 'post-API age': experiences from two studies and recommendations for future research

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

Nonequilibrium entropy production for open quantum systems

We consider open quantum systems weakly coupled to a heat reservoir and driven by arbitrary time-dependent parameters. We derive exact microscopic expressions for the nonequilibrium entropy production and entropy production rate, valid arbitrarily far from equilibrium. By using the two-point energy measurement statistics for system and reservoir, we further obtain a quantum generalization of the integrated fluctuation theorem put forward by Seifert [PRL 95, 040602 (2005)].Comment: 4 pages, 1 figur

Virtual Screening for HIV Protease Inhibitors: A Comparison of AutoDock 4 and Vina

BACKGROUND: The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease. METHODOLOGY/PRINCIPAL FINDINGS: Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and iota = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random. CONCLUSIONS/SIGNIFICANCE: In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen

Integration of the 3D Environment for UAV Onboard Visual Object Tracking

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic monitoring, video surveillance, and navigation.Comment: Accepted in MDPI Journal of Applied Science

The role of the information environment during the first COVID-19 wave in Germany

The COVID-19 pandemic has been accompanied by intense debates about the role of the information environment. On the one hand, citizens learn from public information campaigns and news coverage and supposedly adjust their behaviours accordingly; on the other, there are fears of widespread misinformation and its detrimental effects. Analyzing the posts of the most important German information providers published via Facebook, this paper first identifies a uniform salience of subtopics related to COVID-19 across different types of information sources that generally emphasized the threats to public health. Next, using a large survey conducted with German residents during the first COVID-19 wave in March 2020 we investigate how information exposure relates to perceptions, attitudes and behaviours concerning the pandemic. Regression analyses show that getting COVID-19-related information from a multitude of sources has a statistically significant and positive relationship with public health outcomes. These findings are consistent even across the ideological left/right spectrum and party preferences. These consistent correlational results demonstrate that during the first wave of COVID-19, a uniform information environment went hand in hand with a cautious public and widely accepted mitigation measures. Nonetheless, we discuss these findings against the backdrop of an increased politicization of public-health measures during later COVID-19 waves