(…) the takeaway lesson is that the intuitive idea that objects influence each other because they are close in space is soon to become another of those easy beliefs that turn out to be wrong when we look deeper. The smoothness of space is soon to become an illusion that hides a tiny and complex world of causal interactions, which do not live in space—but which rather define and create space as they create the future from the present.
There are certain contexts, where we would like to analyze the behavior of small interacting systems, such as sports teams. While large interacting systems have drawn much attention in the past years, let it be physical systems of interacting particles or social networks, small systems are short of appropriate quantitative modeling and measurement tools. We propose a simple procedure for analyzing a small system through the degree in which its behavior at different granularity levels (e.g., dyads) non-linearly diverges from the simple additive behavior of its sub-units. For example, we may model the behavior of a soccer team by measuring the extent to which the behavior changes when we move from individual players to dyads, triads, and so on. In this paper, we address the challenge of modeling small systems in terms of measuring divergence from additivity at different granularity levels of the system. We present and develop a measure for quantifying divergence from additivity through what we term a Relative Entropy Lattice , and illustrate its benefits in modeling the behavior of a specific small system, a soccer team, using data from the English Premier League. Our method has practical implications too, such as allowing the coach to identify “hidden” weak spots in the team’s behavior.
Modeling Small Systems Through the Relative Entropy Lattice
Yair Neuman ; Dan Vilenchik
IEEE Access ( Volume: 7 )
Page(s): 43591 – 43597
- An earthquake in Mexico received the spotlight of the media for several weeks, allowed quantifying media coverage.
- A person from a large city receives more attention from the media, per person, than a person from a small city.
- The coverage that the media places on a specific event or topic has an exponential decay. The coverage given to an event drops by half every eight days.
Temporal and spatial analysis of the media spotlight
Rafael Prieto Curiel, Carmen Cabrera Arnau, MaraTorres Pinedo, Humberto González Ramírez, Steven R.Bishop
Computers, Environment and Urban Systems
Volume 75, May 2019, Pages 254-263
Measuring the impact and success of human performance is common in various disciplines, including art, science, and sports. Quantifying impact also plays a key role on social media, where impact is usually defined as the reach of a user’s content as captured by metrics such as the number of views, likes, retweets, or shares. In this paper, we study entire careers of Twitter users to
understand properties of impact. We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other. Second, users commonly have ‘hot streaks’ of impact, i.e., extended periods of high-impact tweets. Third, impact tends to gradually build up before, and fall off after, a user’s most impactful tweet. We attempt to explain these characteristics using various properties measured on social media, including the user’s network, content, activity, and experience, and find that changes in impact are associated with significant changes in these properties. Our findings open interesting avenues for future research on virality and influence on social media.
Hot Streaks on Social Media
Kiran Garimella, Robert West
Research using social network analyses has been booming since the start of the 2000s, with studies not only in humans but also many nonhuman species. Primates are no exception, with the number of retrievable items using the keywords “social networks primates” increasing tenfold from 2000 to 2017 (Fig. 1a). Studies are in various domains including psychology, behavioral sciences, and sociology, as well as neurosciences and infectious diseases (Fig. 1b). To our knowledge, several special issues and books have focused on animals (Croft et al. 2008; Whitehead 2008; Krause et al. 2009; Sheldon 2015; Sueur and Mery 2017) but with only one special issue devoted to primates (Sueur et al. 2011). In the last decade studies have evolved from describing structures (Manno 2008; Carter et al. 2013; Bret et al. 2013) and topologies of social networks or centrality of group members according to their sociodemographic characteristics (Lusseau and Newman 2004; Kanngiesser et al. 2011), to a more holistic approach where the function and evolution of networks are linked to ecological factors, behavioral mechanisms, network topologies, and vice versa (Brent et al. 2013; Fisher et al. 2016; Balasubramaniam et al. 2018). In this new special issue, our aim is to present this integrative and multilevel approach along with state-of-the-art methodologies and theoretical approaches for the study of primate social networks.
Editorial: Social networks analyses in primates, a multilevel perspective
Ivan Puga-Gonzalez, Sebastian Sosa, Cédric Sueur