Archive for the ‘Social network’ Category
The purpose of this chapter is to apply new theoretical constructions to the unique situation of online social networks by investigating the issue of personal and collective empowerment. To better illustrate the applicability of the new theoretical constructions, ideas of identity, false consciousness, and collective intelligence are addressed to demonstrate the tensions in and among the realities of online social networks and their ability to empower individuals and collectivities or to delude those same people into thinking that online social networks enable empowerment.
Social networking is no doubt an important component in empowering individuals, collectivities, and communities – but, while social networks constitute an interesting wedge by which to examine notions of personal or collective power, we need to see that this type of empowerment can only take place if supported by multiple agents in the process of change. Contemporary theories like web theory, network theory, and systems theory all help us understand the relationships between and among human users and technologies – particularly when examined from perspectives of architecture and control (including control by corporations, governments, and other institutions), but the empowerment of the individual is analogous to the power of digital communication, conveying a temporary or ephemeral way of knowing, unless the larger cybernetic system is constantly reinforced by multiple lenses of interpretation. Only then, over time, can we attempt to know or evaluate whether our consciousness has been changed by or through empowerment – or whether we fall into a false consciousness that eludes and deludes our true understanding of meaning.
Collective intelligence can be defined, very broadly, as groups of individuals that do things collectively, and that seem to be intelligent. Collective intelligence has existed for ages. Families, tribes, companies, countries, etc., are all groups of individuals doing things collectively, and that seem to be intelligent. However, over the past two decades, the rise of the Internet has given upturn to new types of collective intelligence. Companies can take advantage from the so-called Web enabled collective intelligence. Web-enabled collective intelligence is based on linking knowledge workers through social media. That means that companies can hire geographically dispersed knowledge workers and create so-called virtual teams of these knowledge workers (members of the virtual teams are connected only via the Internet and do not meet face to face). By providing an online social network, the companies can achieve significant growth of collective intelligence. But to create and use an online social network within a company in a really efficient way, the managers need to have a deep understanding of how such a system works.Thusthe purpose of this paper is to share the knowledge about effective use of social networks in organizations. The main objectives of this paper are as follows: to introduce some good practices of the use of social media in organizations, to analyze these practices and to generalize recommendations for a successful introduction and use of social media to increase collective intelligence of a company.
When making decisions, humans can observe many kinds of information about others’ activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others’ solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers’ solutions, popularity of solution elements among peers, similarity of peers’ solutions to their own, and relative payoffs from individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers’ payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers’ solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration.
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.