Archive for the ‘Social network’ Category
Previous research has focused heavily on community communications as they occur in e.g.
communities of practice. Still, as indicated by the concept of networked individualism, contacts are becoming more networked in nature and group membership is transient. The research presented here yields to the call of Garton et al to move away from the study of communication taking place only in groups and to also investigate the potential of computer-mediated communication to support interaction in unbound and sparsely-knit social networks. As a consequence, in chapters 5 and 6, I’ve adopted a research method which takes the relationship between people as the basic unit of analysis. In conclusion, as work practice in Western economies is evolving towards knowledge work, and knowledge work rests heavily on knowledge sharing, the combination of networked individualism and knowledge sharing seems a relevant subject of study.
Innovation – the process of obtaining, understanding, applying, transforming, managing and transferring knowledge – is a result of human collaboration, but it has become an increasingly complex process, with a growing number of interacting parties involved. Lack of innovation is not necessarily caused by lack of technology or lack of will to innovate, but often by social and cultural forces that jeopardize the cognitive processes and prevent potential innovation. This book focuses on the rule of social capital in the process of innovation: the social networks and the norms; values and attitudes (such as trust) of the actors; social capital as both bonding and bridging links between actors; and social capital as a feature at all spatial levels, from the single inventor to the transnational corporation. Contributors from a wide variety of countries and disciplines explore the cultural framework of innovation through empirics, case studies and examination of conceptual and methodological dilemmas.
Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks, although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.
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