Analytics: Social Computing Trends Real Use

Social computing refers to an area of computer science that is the intersection of social behavior and computational systems.

So, social computing implies two components: a social behavior component and a computational system or technical component.

The technical component provides the environment in which people interact.

Another definition of social computing is, “Social computing is the use of technology in networked communication systems by communities of people for one or more goals.

Resources to Use in Social Computing

Social computing takes many forms including social networks, RSS, blogs, search engines, podcasts, wikis, and social bookmarking (or tagging).

A number of other terms are used loosely to refer to social computing. They are online communities, Web 2.0, virtual communities and social networking. These terms have similar definitions, and these definitions sometimes overlap (Parameswaran and Whinston 2007).

Online communities are defined as groups of people who meet and interact with others, are connected by specific interests, are brought together by means of a technical platform, and can establish social relationships or a sense of belonging to the group (Leimeister et al 2008).

According to Preece (2000), a virtual community refers to people with a common or shared purpose, whose interactions are governed by policies in the form of tacit assumptions, rituals, protocols, rules and laws and whose use of computer systems to support and mediate social interaction and to facilitate a sense of togetherness.

We see that the definitions of online communities, virtual communities and social computing are overlapped.

We use social computing as the overarching concept that includes online and virtual communities, RSS, blogs, wikis, and other systems as stated earlier.

But, I do use the terms of social computing and social networking interchangeably.

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Author: Srini

Experienced software developer. Skills in Development, Coding, Testing and Debugging. Good Data analytic skills (Data Warehousing and BI). Also skills in Mainframe.