arXiv subjects

Andreas Roepstorff

Publications and source records attributed to Andreas Roepstorff.

4 recordsLinked to original sources

arXiv2021

When no news is bad news -- Detection of negative events from news media content

During the first wave of Covid-19 information decoupling could be observed in the flow of news media content. The corollary of the content alignment within and between news sources experienced by readers (i.e., all news transformed into Corona-news), was that the novelty of news content went down as media focused monotonically on the pandemic event. This all-important Covid-19 news theme turned out to be quite persistent as the pandemic continued, resulting in the, from a news media's perspective, paradoxical situation where the same news was repeated over and over. This information phenomenon, where novelty decreases and persistence increases, has previously been used to track change in news media, but in this study we specifically test the claim that new information decoupling behavior of media can be used to reliably detect change in news media content originating in a negative event, using a Bayesian approach to change point detection.

cs.CY↗

arXiv2021

News Information Decoupling: An Information Signature of Catastrophes in Legacy News Media

Content alignment in news media was an observable information effect of Covid-19's initial phase. During the first half of 2020, legacy news media became "corona news" following national outbreak and crises management patterns. While news media are neither unbiased nor infallible as sources of events, they do provide a window into socio-cultural responses to events. In this paper, we use legacy print media to empirically derive the principle News Information Decoupling (NID) that functions as an information signature of culturally significant catastrophic event. Formally, NID can provide input to change detection algorithms and points to several unsolved research problems in the intersection of information theory and media studies.

cs.CY↗

arXiv2016

Inferring causality from noisy time series data

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses.

nlin.CD↗

arXiv2015

A Heart for Interaction: Shared Physiological Dynamics and Behavioral Coordination in a Collective, Creative Construction Task

Interpersonally shared physiological dynamics are increasingly argued to underlie rapport, empathy and even team performance. Inspired by the model of interpersonal synergy, we critically investigate the presence, temporal development, possible mechanisms and impact of shared interpersonal heart rate dynamics during individual and collective creative LEGO construction tasks. In Study 1 we show how shared HR dynamics are driven by a plurality of sources including task constraints and behavioral coordination. Generally, shared HR dynamics are more prevalent in individual trials (involving participants doing the same things) than in collective ones (involving participants taking turns and performing complementary actions). However, when contrasted against virtual pairs, collective trials display more stable shared HR dynamics suggesting that online social interaction plays an important role. Furthermore, in contrast to individual trials, shared HR dynamics are found to increase across collective trials. Study 2 investigates which aspects of social interaction might drive these effects. We show that shared HR dynamics are statistically predicted by interpersonal speech and building coordination. In Study 3, we explore the relation between HR dynamics, behavioral coordination, and self-reported measures of rapport and group competence. While behavioral coordination predicts rapport and group competence, shared HR dynamics do not. Although shared physiological dynamics were reliably observed in our study, our results warrant not to consider HR dynamics a general driving mechanism of social coordination. Behavioral coordination - on the other hand - seems to be more informative of both shared physiological dynamics and collective experience.

physics.soc-ph↗