Attention on Weak Ties in Social and Communication Networks

Granovetter's weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter's work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter's hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties --- possibly reflecting the postulated informational purposes of such ties --- but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform's typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content

Maintaining intellectual diversity in data science

Data science is a young and rapidly expanding field, but one which has already experienced several waves of temporarily-ubiquitous methodological fashions. In this paper we argue that a diversity of ideas and methodologies is crucial for the long term success of the data science community. Towards the goal of a healthy, diverse ecosystem of different statistical models and approaches, we review how ideas spread in the scientific community and the role of incentives in influencing which research ideas scientists pursue. We conclude with suggestions for how universities, research funders and other actors in the data science community can help to maintain a rich, eclectic statistical environment

Changing User Behavior in Decisions to Share COVID-19 Misinformation: An Implicit Association Test Study

Making medical decisions while distracted when receiving COVID-19 misinformation can majorly impact a person's life and even lead to death. Blatantly sharing COVID-19 misinformation is a significant problem of human behavior that triggers a speed-up and acceleration in the propagation and diffusion of misinformation in social media. While the latest research has focused on understanding the psychological dimensions of this phenomenon, few studies have explored the role of selective exposure and technological prevention when a person considers sharing COVID-19 misinformation, primarily through an Implicit Association Test (IAT). Our study identified and intervened in the association of user exposure between misinformation and implicit truth evaluations by using the Implicit Association Test (IAT) with "Misinformation vs. Fact Information or Positive vs. Negative Words”, 38 from 150 participants were either exposed to misinformation headlines or actual new headline posts on stimulants, in the form of images. We then measured participants' implicit truth evaluations and self-reported perceived accuracies of actual and of misinformation headlines using the Visual Selective Attention System (VSAS). After intervening, participants exposed to fake news headlines had lower implicit truth evaluations and increased perceived accuracy. This implies that exposure to fake news headlines after the intervention with the VSAS system may have directly affected implicit evaluations and changed user behavior in sharing COVID-19 misinformation

Analysing engagement towards the 2014 Earth Hour Campaign in Twitter

Earth Hour (EH) is a large-scale campaign launched by the World Wide Fund For Nature (WWF) every year to raise awareness about environmental issues. Although the EH campaign is active on social media, there is currently no systematic way of assessing its impact on public engagement and the topics they post about. In this paper we study engagement towards the 2014 EH campaign on Twitter. By analysing more than 35K tweets around the campaign we observed that longer posts, easier to read and with positive sentiment generated higher attention levels. Conversations were driven by the main themes of the campaign (super hero, the panda, etc.), but engagement towards these themes did not always translate in engagement towards environmental issues. Users decreased their engagement towards the topics of the campaign after it finished, but these topics still remained in their conversations one month later

The Behavior of Epidemics under Bounded Susceptibility

We investigate the sensitivity of epidemic behavior to a bounded susceptibility constraint -- susceptible nodes are infected by their neighbors via the regular SI/SIS dynamics, but subject to a cap on the infection rate. Such a constraint is motivated by modern social networks, wherein messages are broadcast to all neighbors, but attention spans are limited. Bounded susceptibility also arises in distributed computing applications with download bandwidth constraints, and in human epidemics under quarantine policies. Network epidemics have been extensively studied in literature; prior work characterizes the graph structures required to ensure fast spreading under the SI dynamics, and long lifetime under the SIS dynamics. In particular, these conditions turn out to be meaningful for two classes of networks of practical relevance -- dense, uniform (i.e., clique-like) graphs, and sparse, structured (i.e., star-like) graphs. We show that bounded susceptibility has a surprising impact on epidemic behavior in these graph families. For the SI dynamics, bounded susceptibility has no effect on star-like networks, but dramatically alters the spreading time in clique-like networks. In contrast, for the SIS dynamics, clique-like networks are unaffected, but star-like networks exhibit a sharp change in extinction times under bounded susceptibility. Our findings are useful for the design of disease-resistant networks and infrastructure networks. More generally, they show that results for existing epidemic models are sensitive to modeling assumptions in non-intuitive ways, and suggest caution in directly using these as guidelines for real systems