Posts Tagged ‘social networks’
Humans have more in common with bees than we like to admit: We’re social creatures first and foremost. Our most important habits of action—and most basic notions of common sense—are wired into us through our coordination in social groups. Social physics is about idea flow, the way human social networks spread ideas and transform those ideas into behaviors.
Thanks to the millions of digital bread crumbs people leave behind via smartphones, GPS devices, and the Internet, the amount of new information we have about human activity is truly profound. Until now, sociologists have depended on limited data sets and surveys that tell us how people say they think and behave, rather than what they actually do. As a result, we’ve been stuck with the same stale social structures—classes, markets—and a focus on individual actors, data snapshots, and steady states. Pentland shows that, in fact, humans respond much more powerfully to social incentives that involve rewarding others and strengthening the ties that bind than incentives that involve only their own economic self-interest.
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.
Read also: Facebook ‘likes’ predict personality
The Internet and Social Media change our way of decision-making. We are no longer the independent decision makers we used to be. Instead, we have become networked minds, social decision-makers, more than ever before. This has several fundamental implications. First of all, our economic theories must change, and second, our economic institutions must be adapted to support the social decision-maker, the “homo socialis“, rather than tailored to the perfect egoist, known as “homo economicus“.
The financial, economic and public debt crisis has seriously damaged our trust in mainstream economic theory. Can it really offer an adequate description of economic reality? Laboratory experiments keep questioning one of the main pillars of economic theory, the “homo economicus“. They show that the perfectly self-regarding decision-maker is not the rule, but rather the exception. And they show that markets, as they are organized today, are undermining ethical behavior.
In an excerpt from his book Why It’s Still Kicking Off Everywhere, Paul Mason argues that a global protest movement, based on social networks, is here to stay.
Two years on from the fall of Hosni Mubarak, the new Egyptian president is from the Muslim Brotherhood; on the streets of Cairo, the same kind of people who died in droves in 2011 are still getting killed. On the streets of Athens, the neo-Nazi party Golden Dawn is staging anti-migrant pogroms. In Russia, Pussy Riot are in jail and the leaders of the democracy movement facing criminal indictments. The war in Syria is killing 200 people a day. It’s an easy step from all this to the conclusion that 2011, the year it all kicked off, was a flash in the pan. But wrong. Something real and important was unleashed in 2011, and it has not yet gone away. I am confident enough now to call it a revolution. Some of its processes conform to the templates laid down in the revolutionary wave that swept Europe in 1848, but many do not: above all, the relationship between the physical and the mental, the political and the cultural, seem inverted.
Along with the origins of agriculture, the appearance of complex societies – often called ‘chiefdoms’ and ‘states’ – is one of the most widely discussed social processes in the archaeological literature. Explanations for the beginnings of complex societies commonly involve ideas of progressive social evolution.
An alternative approach for understanding the evolution of social complexity is based on concepts derived from the study of complex systems. As we illustrate below, complex systems give us new conceptual tools for studying the social processes that drove the evolution of small agricultural communities into political states. Complex systems are composed of many interacting components organized into nested groups that can be represented as organizational hierarchies or hierarchically structured networks; the more complex the system, the deeper the nesting of the groups of components. In human terms, such nested groups could be nuclear families within forager bands, and bands within regional metapopulations. They also could be households within clans within chiefdoms, or individuals within craft guilds, within a city within a state.
Renowned scientists Christakis and Fowler present compelling evidence for our profound influence on one another’s tastes, health, wealth, happiness, beliefs, even weight, as they explain how social networks form and how they operate. “Connected” uses science and research to explain how we are all socially tied together in one way shape or form. And how our actions affect other people’s lives and vice versa. We all create a ripple affect that is felt and heard to the ends of the world. After reading this book you suddenly become more sensitive about your actions, how your negative or positive energy can pass onto a complete stranger and how they will pass it along and so on and so forth. Life is a cycle and everything comes back to us. Reap what you sow.
Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.
This paper will examine the June-December 2004 popular uprising in Manipur, through the lens of reports in the Manipuri, Indian and global media, with a view to showing how the characteristics of an affinity-network arise in a marginal setting. It will show that social relations based on the affinity-network form provide an alternative to statist and hierarchical imaginaries which create antagonistic, fixed identities, turning difference into a positive force of empowerment instead of a matter of incompatible claims. It will also seek to understand how, in contrast to other local political forces, the mobilisation was able to turn difference into a source of strength.
Understanding social dynamics that govern human phenomena, such as communications and social relationships is a major problem in current computational social sciences. In particular, given the unprecedented success of online social networks (OSNs), in this paper we are concerned with the analysis of aggregation patterns and social dynamics occurring among users of the largest OSN as the date: Facebook. In detail, we discuss the mesoscopic features of the community structure of this network, considering the perspective of the communities, which has not yet been studied on such a large scale. To this purpose, we acquired a sample of this network containing millions of users and their social relationships; then, we unveiled the communities representing the aggregation units among which users gather and interact; finally, we analyzed the statistical features of such a network of communities, discovering and characterizing some specific organization patterns followed by individuals interacting in online social networks, that emerge considering different sampling techniques and clustering methodologies. This study provides some clues of the tendency of individuals to establish social interactions in online social networks that eventually contribute to building a well-connected social structure, and opens space for further social studies.
In complex social systems such as those of many mammals, including humans, groups (and hence ego-centric social networks) are commonly structured in discrete layers. We describe a computational model for the development of social relationships based on agents’ strategies for social interaction that favour more less-intense, or fewer more-intense partners. A trust-related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents’ strategies. A good fit of the observed layers of human social networks was found across a range of model parameter settings. Social interaction strategies which favour interacting with existing strong ties or a time-variant strategy produced more observation-conformant results than strategies favouring more weak relationships. Strong-tie strategies spread in populations under a range of fitness conditions favouring wellbeing, whereas weak-tie strategies spread when fitness favours foraging for food. The implications for modelling the emergence of social relationships in complex structured social networks are discussed.