Here are 89 Life Hacks That Will Make Your New Year So Much Better
As you go into the New Year, remember that things can be better than previous years. Maybe you'll get a raise, date someone new or even adopt an adorable pet to welcome into your family. But, if you fail at all of those things (sorry, if you do, but the chances are you may), here are life hacks that you can use to make your 2014 a lot better.
Measuring Large-Scale Social Networks with High Resolution
This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Figures Citation: Stopczynski A, Sekara V, Sapiezynski P, Cuttone A, Madsen MM, et al. (2014) Measuring Large-Scale Social Networks with High Resolution. Editor: Yamir Moreno, University of Zaragoza, Spain Received: February 15, 2014; Accepted: April 2, 2014; Published: April 25, 2014 Copyright: © 2014 Stopczynski et al. Introduction Related Work Data collection
Facebook feelings are contagious: Study examines how emotions spread online
You can’t catch a cold from a friend online. But can you catch a mood? It would seem so, according to new research from the University of California, San Diego. Published in PLOS ONE, the study analyzes over a billion anonymized status updates among more than 100 million users of Facebook in the United States. “Our study suggests that people are not just choosing other people like themselves to associate with but actually causing their friends’ emotional expressions to change,” said lead author James Fowler, professor of political science in the Division of Social Sciences and of medical genetics in the School of Medicine at UC San Diego. There is abundant scientific literature on how emotion can spread among people – through direct contact, in person – not only among friends but also among strangers or near-strangers. Fowler worked on the study with Lorenzo Coviello – a PhD student in the electrical and computer engineering department of the UC San Diego Jacobs School of Engineering.
Social Network Analysis
Brief Description: "Social network analysis is the mapping and measuring of relationships and flows between people, groups, organisations, computers or other information/knowledge processing entities." (Valdis Krebs, 2002). Social Network Analysis (SNA) is a method for visualizing our people and connection power, leading us to identify how we can best interact to share knowledge. History: When to use: Visualize relationships within and outside of the organization.Facilitate identification of who knows who and who might know what - teams and individuals playing central roles - thought leaders, key knowledge brokers, experts, etc.Identify isolated teams or individuals and knowledge bottlenecks.Strategically work to improve knowledge flows.Accelerate the flow of knowledge and information across functional and organisational boundaries.Improve the effectiveness of formal and informal communication channels.Raise awareness of the importance of informal networks. How to use: Training on SNA Tags
Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters
Polarized Crowds: Political conversations on Twitter Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation. If a topic is political, it is common to see two separate, polarized crowds take shape. While these polarized crowds are common in political conversations on Twitter, it is important to remember that the people who take the time to post and talk about political issues on Twitter are a special group. Conversational archetypes on Twitter Why is it useful to map the social landscape this way? What this all means Figure 2
Applying algorithm to social networks can reveal hidden connections criminals use to commit fraud
Fraudsters beware: the more your social networks connect you and your accomplices to the crime, the easier it will be to shake you from the tree. The Steiner tree, that is. In an article recently published in the journal Computer Fraud and Security, University of Alberta researcher Ray Patterson and colleagues from the University of Connecticut and University of California – Merced outlined the connection linking fraud cases and the algorithm designed by Swiss mathematician Jakob Steiner. Fraud is a problem that costs Canadians billions of dollars annually and countless hours of police investigations. The criminal path of least resistance To quote a television gumshoe, everything's connected. "You're really trying to find the minimum set of connectors that connect these people to the various [network] resources," he said. Fraud and the Steiner tree, by the numbers "All of these things that we see in life, behind them is a mathematical representation," said Patterson.
A search engine for social networks based on the behavior of ants
Research at Carlos III University in Madrid is developing an algorithm, based on ants' behavior when they are searching for food, which accelerates the search for relationships among elements that are present in social networks. One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network. Multiple applications
Computational social science: Making the links
Jon Kleinberg's early work was not for the mathematically faint of heart. His first publication1, in 1992, was a computer-science paper with contents as dense as its title: 'On dynamic Voronoi diagrams and the minimum Hausdorff distance for point sets under Euclidean motion in the plane'. That was before the World-Wide Web exploded across the planet, driven by millions of individual users making independent decisions about who and what to link to. And it was before Kleinberg began to study the vast array of digital by-products generated by life in the modern world, from e-mails, mobile phone calls and credit-card purchases to Internet searches and social networks. Today, as a computer scientist at Cornell University in Ithaca, New York, Kleinberg uses these data to write papers such as 'How bad is forming your own opinion?' “I realized that computer science is not just about technology,” he explains. Kleinberg is not alone. Social calls Infectious ideas Message received
