Archive for the ‘Networks in the News’ Category

Population structured by witchcraft beliefs


Anthropologists have long argued that fear of victimization through witchcraft accusations promotes cooperation in small-scale societies. Others have argued that witchcraft beliefs undermine trust and therefore reduce social cohesion. However, there are very few, if any, quantified empirical examples demonstrating how witchcraft labels can structure cooperation in real human communities. Here we show a case from a farming community in China where people labelled zhu were thought capable of supernatural activity, particularly poisoning food. The label was usually applied to adult women heads of household and often inherited down the female line. We found that those in zhuhouseholds were less likely to give or receive gifts or farm help to or from non-zhu households; nor did they have sexual partnerships or children with those in non-zhu households. However, those in zhuhouseholds did preferentially help and reproduce with each other. Although the tag is common knowledge to other villagers and used in cooperative and reproductive partner choice, we found no evidence that this assortment was based on cooperativeness or quality. We favour the explanation that stigmatization originally arose as a mechanism to harm female competitors. Once established, fear that the trait is transmissible may help explain the persistence of this deep-rooted cultural belief. Source: Nature

Scale-free Networks Are Rare


Recently Aaron Clauset and his colleague share their new study: “Scale-free networks are rare”. In this study, they found scale-free network structure is not so prevalent based on their statistical analyses of almost 1000 network datasets across different domains. In particular, their results indicate only 4% of the datasets showing the strongest-possible evidence of scale-free structure and 52% demonstrating the weakest-possible evidence. Additionally, this study has invoked intense conversations over Twitter. For instance, Laszlo Barabasi retweeted Aaron Caluset’s tweet, saying “Every 5 years someone is shocked to re-discover that a pure power law does not fit many networks. True: Real networks have predictable deviations. Hence forcing a pure power law on these is like…fitting a sphere to the cow. Sooner or later the hoof will stick out.” Link to the paper: Link to Barabasi’s retweet:

A Mechanistic Model of Human Network Recall


Recently, Omodei, Brashears, and Arenas published a paper about describing a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior based on experimental data. They found that human recall is based on accurate recall of a hub of high degree actors and also uses compression heuristics (i.e., schemata simplifying the encoding and recall of social information) for both structural and affective information. The original paper is here:

A manifesto for reproducible science


Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research. Link:

The Social Bow Tie


A recent study investigated a new way to identify the strength of ties. Using two different large datasets, the researchers found that for each pair of individuals, a bow tie structure of the network itself is strongly associated with the strength of ties between them that the researchers measure in other ways. The abstract of the paper is as follows: Understanding tie strength in social networks, and the factors that influence it, have received much attention in a myriad of disciplines for decades. Several models incorporating indicators of tie strength have been proposed and used to quantify relationships in social networks, and a standard set of structural network metrics have been applied to predominantly online social media sites to predict tie strength. Here, we introduce the concept of the “social bow tie” framework, a small subgraph of the network that consists of a collection of nodes and ties that surround a tie of interest, forming a topological structure that resembles a bow tie. We also define several intuitive and interpretable metrics that quantify properties of the bow tie. We use random forests and regression models to predict categorical and continuous measures of tie strength from different properties of the bow tie, including ...

The Strength of Absent Ties: Social Integration via Online Dating


by Josué Ortega and Philipp Hergovich We used to marry people to which we were somehow connected to: friends of friends, schoolmates, neighbors. Since we were more connected to people similar to us, we were likely to marry someone from our own race. However, online dating has changed this pattern: people who meet online tend to be complete strangers. Given that one-third of modern marriages start online, the authors investigate theoretically, using random graphs and matching theory, the effects of those previously absent ties in the diversity of modern societies. The authors find that when a society benefits from previously absent ties, social integration occurs rapidly, even if the number of partners met online is small. Their findings are consistent with the sharp increase in interracial marriages in the U.S. in the last two decades. Read the full article here.

Decentralized Social Networks Sound Great: Too Bad They’ll Never Work


by Chelsea Barabas, Neha Narula, and Ethan Zuckerman Over the last 13 years, Facebook has evolved from a lifestyle site for college kids into a cornerstone of civic life. It is one of a handful of very large platforms that dominate our online world. As such platforms have gained traction, the web has transformed from an open space for free expression into a corporate-owned gated community of private platforms. The power of giant platforms like Facebook, Google, and Twitter leads to problems ranging from the threat of government-ordered censorship to more subtle, algorithmic biases in the curation of content users consume. Moreover, as these platforms expand their reach, the ripple effects of exclusion can have serious consequences for people’s personal and professional lives, and users have no clear path to recourse. The platforms that host and inform our networked public sphere are unelected, unaccountable, and often impossible to audit or oversee. In response, there is a growing movement among free speech advocates to create new technology to address these concerns. Early web pioneers like Brewster Kahle have called for ways we might “lock the web open” with code, enabling peer-to-peer interactions in place of mediated private platforms. The idea is to return to ...

How does network structure influence the wisdom of crowds?


Researchers at Annenberg School for Communication, University of Pennsylvania recently published a paper about “Network dynamics of social influence in the wisdom of crowds” in PNAS. They conducted an online network experiment where participants were asked to estimate numeric quantity (e.g., the caloric content) and tested how the accuracy of group estimates changes in different communication networks. They found that in decentralized networks, the group estimates were improved and in centralized networks, the accuracy of group estimates was undermined. Read the full article here.

Social networks may one day diagnose disease–but at a cost


by Sam Volchenboum The world is becoming one big clinical trial. Humanity is generating streams of data from different sources every second. And this information, continuously flowing from social media, mobile GPS and wifi locations, search history, drugstore rewards cards, wearable devices, and much more, can provide insights into a person’s health and well-being. It’s now entirely conceivable that Facebook or Google—two of the biggest data platforms and predictive engines of our behavior—could tell someone they might have cancer before they even suspect it. Someone complaining about night sweats and weight loss on social media might not know these can be signs of lymphoma, or that their morning joint stiffness and propensity to sunburn could herald lupus. But it’s entirely feasible that bots trolling social network posts could pick up on these clues. Sharing these insights and predictions could save lives and improve health, but there are good reasons why data platforms aren’t doing this today. The question is, then, do the risks outweigh the benefits? Read the full article here.

Emotion shapes the diffusion of moralized content in social networks


by William J. Brady, Julian A. Willis, John T. Tost, Joshua A. Tucker, and Jay J. Van Bavel Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.” Using a large sample of social media communications about three polarizing moral/political issues, we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly ...