بایگانی برچسب برای: Social networks

Social media Get serious! Understanding the.[taliem.ir]

Social media? Get serious! Understanding the functional building blocks of social media

Traditionally, consumers used the Internet to simply expend content: they read it, they watched it, and they used it to buy products and services. Increasingly ,however, consumers are utilizing platforms–—such as content sharing sites, blogs, social networking, and wikis–—to create, modify, share, and discuss Internet content. This represents the social media phenomenon, which can now significantly impact a firm’s reputation, sales, and even survival. Yet, many executives eschew or ignore this form of media because they don’t understand what it is, the various forms it can take, and how to engage with it and learn. In response, we present a framework that defines social media by using seven functional building blocks: identity, conversations, sharing, presence, relationships, reputation, and groups. As different social media activities are defined by the extent to which they focus on some or all of these blocks ,we explain the implications that each block can have for how firms should engage with social media. To conclude, we present a number of recommendations regarding how firms should develop strategies for monitoring, understanding, and responding to different social media activities.
Management perception[taliem.ir]

Management perception of introducing social networking sites as a knowledge management tool in higher education A case study

Purpose – The purpose of this paper is to present a study of the understanding and usage of social networking sites (SNS) as a knowledge management (KM) tool in knowledge-intensive enterprises .Design/methodology/approach – In terms of research approach, the study has taken an interpretitivist framework, using a higher education (HE) institution as the case-study, which is characterised by the need to generate process, share and use knowledge on a daily basis in order to remain competitive. The case study was analysed using qualitative research methodology, composed of interviews and utilised narrative analysis as a means of data analysis, thus deriving a characterisation of understandings, perceptions and acceptance of SNS as a KM tool .Findings – The study provides evidence that even in HE, where it is generally acknowledged that there is a need to adequately capture, store, share and disseminate knowledge, as this can lead to greater innovation, creativity and productivity, participants were suspicious of the nature of the technology and the fact that it could intertwine their professional and social life. As a result, they were not prepared to invest the relatively high effort required in employing SNS as a KM tool as they also have difficulty in establishing the added value. Consequently, in order to employ SNS for KM purposes cultural ,behavioural and organisational issues need to be tackled before even considering technical issues. Originality/value – The paper provides an insight into KM and social networking in HE. This also highlights issue for international HE.
cialization, uses and influence of social networks in adolescents[taliem.ir]

cialization, uses and influence of social networks in adolescents: the role of broadcast scheduling

This research deals with the influence on adolescents that have the stereotypes from TV series, reality shows and social networks.
Social networking on the[taliem.ir]

Social networking on the semantic web

Purpose – Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach – The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the “Friend of a Friend” (FOAF) ontology. The datasets were analyzed to discover how FOAF is being used and investigate the kinds of social networks found on the web. Findings – The FOAF ontology is the most widely used domain ontology on the semantic web. People are using it in an open and extensible manner by defining new classes and properties to use with FOAF. Research limitations/implications – RDF data was only obtained from public RDF documents published on the web. Some RDF FOAF data may be unavailable because it is behind firewalls, on intranets or stored in private databases. The ways in which the semantic web languages RDF and OWL are being used (and abused) are dynamic and still evolving. A similar study done two years from now may show very different results. Originality/value – This paper describes how social networks are being encoded and used on the world wide web in the form of RDF documents and the FOAF ontology. It provides data on large social networks as well as insights on how the semantic web is being used in 2005 .
Computational Models for Social Network Analysis[taliem.ir]

Computational Models for Social Network Analysis: A Brief Survey

With the exponential growth of online social network services such as Facebook and Twitter, social networks and social medias become more and more important, directly influencing politics, economics, and our daily life. Mining big social networks aims to collect and analyze web-scale social data to reveal patterns of individual and group behaviors. It is an inherently interdisciplinary academic field which emerged from sociology, psychology, statistics, and graph theory. In this article, I briefly survey recent progress on social network mining with an emphasis on understanding the interactions among users in the large dynamic social networks. I will start with some basic knowledge for social network analysis, including methodologies and tools for macro-level, meso-level and microlevel social network analysis. Then I will give an overall roadmap of social network mining. After that, I will describe methodologies for modeling user behavior including state-of-the-art methods forlearning user profiles, and introduce recent progress on modeling dynamics of user behaviors using deep learning. Then I will present models and algorithms for quantitative analysis on social interactions including homophily and social influence.Finally, I will introduce network structure model including social group formation, and network topology generation. We will introduce recent developed network embedding algorithms for modeling social networks with the embedding techniques. Finally, I will use several concrete examples from Alibaba, the largest online shopping website in the world, and WeChat, the largest social messaging service in China, to explain how online social networks influence our offline world.