Gordon Pennycook, Ziv Epstein, Mohsen Mosleh, Antonio Arechar, Dean Eckles, David Rand
The spread of false and misleading news on social media is of great societal concern. Why do people share such content, and what can be done about it? In a first survey experiment (N=1,015), we demonstrate a disconnect between accuracy judgments and sharing intentions: even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. We argue against a “post-truth” interpretation, whereby people deliberately share false content because it furthers their political agenda. Instead, we propose that the problem is simply distraction: most people do not want to spread misinformation, but are distracted from accuracy by other salient motives when choosing what to share. Indeed, when directly asked, most participants say it is important to only share accurate news. Accordingly, across three survey experiments (total N=2775) and an experiment on Twitter in which we messaged N=5,482 users who had previously shared news from misleading websites, we find that subtly inducing people to think about the concept of accuracy increases the quality of the news they share. Together, these results challenge the popular post-truth narrative. Instead, they suggest that many people are capable of detecting low-quality news content, but nonetheless share such content online because social media is not conducive to thinking analytically about truth and accuracy. Furthermore, our results translate directly into a scalable anti-misinformation intervention that is easily implementable by social media platforms.
Maximilian Voit and Hildegard Meyer-Ortmanns
Front. Appl. Math. Stat., 10 December 2019
Heteroclinic networks are structures in phase space that consist of multiple saddle fixed points as nodes, connected by heteroclinic orbits as edges. They provide a promising candidate attractor to generate reproducible sequential series of metastable states. While from an engineering point of view it is known how to construct heteroclinic networks to achieve certain dynamics, a data based approach for the inference of heteroclinic dynamics is still missing. Here, we present a method by which a template system dynamically learns to mimic an input sequence of metastable states. To this end, the template is unidirectionally, linearly coupled to the input in a master-slave fashion, so that it is forced to follow the same sequence. Simultaneously, its eigenvalues are adapted to minimize the difference of template dynamics and input sequence. Hence, after the learning procedure, the trained template constitutes a model with dynamics that are most similar to the training data. We demonstrate the performance of this method at various examples, including dynamics that differ from the template, as well as a regular and a random heteroclinic network. In all cases the topology of the heteroclinic network is recovered precisely, as are most eigenvalues. Our approach may thus be applied to infer the topology and the connection strength of a heteroclinic network from data in a dynamical fashion. Moreover, it may serve as a model for learning in systems of winnerless competition.
Demival Vasques Filho, Dion R. J. O’Neale
Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news", innovation, and others, are highly dependent on the structure of the social network on which they occur. However, questions on why most social networks present some particular structural features, namely high levels of
transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network — together with the presence of cycles of length four and six — explains the observed levels of transitivity and degree assortativity in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.
Mohsen Mosleh, Gordon Pennycook, Antonio Arechar, David Rand
Social media is playing an increasingly large role in everyday life. Thus, it is of both scientific and practical interest to understand behavior on social media platforms. Furthermore, social media provides a unique window for social scientists to deepen our understanding of the human mind. Here we investigate the relationship between individual differences in cognitive reflection and behavior on Twitter in a sample of large N = 1,953 users recruited via Prolific Academic. In doing so, we differentiate between two competing accounts of human information processing: an “intuitionist” account whereby reflection plays little role in daily life, and a “reflectionist” account whereby reflection (and, in particular, overriding intuitive responses) does play an important role. We found that people who score higher on the Cognitive Reflection Test (CRT) – a widely used measure of reflective thinking – were more discerning in their social media use: They followed more selectively, shared news content from more reliable sources, and tweeted about weightier subjects. Furthermore, a network analysis indicated that the phenomenon of echo chambers, in which discourse is more likely with like-minded others, is not limited to politics: we observe “cognitive echo chambers” in which people low on cognitive reflection tend to follow the same set of accounts. Our results help to illuminate the drivers of behavior on social media platforms, and challenge intuitionist notions that reflective thinking is unimportant for everyday judgment and decision-making.