Definition of Social Networking & AWeb 2.0 Types of Web 2.0 Tools and Use Explanation of Most Popular Tools Consumer Options Business/Profit Models Participant Pros and Cons Strategies for Social Networking Participation Making Money with Social Networking (Haven't decided if I want to offer this.)
Not Decentralization Danah Boyd Social Networking Tools are applications that allow multimedia communication between a group who collectively subscribe to each other. Social Networks allow the audience to respond to the authors. to an audience of people who subscribe to History: Dating Services - Match.com Friendster - 2003: youth phenomena MySpace - picking up those Friendster was kicking out. Built for bands. youth phenomena Facebook - Fully open 2006 MySpace vs. Facebook - division brought on by sexual predator scare. (MySpace: Lower Eco / Facebook: Upper Eco) 2007 Collapse the network graph Tribe.net Orkut Facebook MySpace, Twitter and LinkedIn being the most widely used in North America; Nexopia (mostly in Canada); Bebo, Hi5, StudiVZ (mostly in Germany), Decayenne, Tagged, XING;, Badoo and Skyrock in parts of Europe; Orkut and Hi5 in South America and Central America; and Friendster, Mixi, Multiply, Orkut, Wretch, Xiaonei and Cyworld
PreWeb2.0 Tools: Chat Rooms, Instant Messaging, Bulletin Boards GroupWare - Social Software - User Generated Content - Computer-mediated Communication Blogs, wikis, media-sharing sites, social network sites, social bookmarking, virtual worlds, microblogging sites
Contextual Collaboration Walled Garden Network Effects Stickiness CrowdSourcing Retweet Degrees of Separation Network Effects – Social Circles doing the same thing at once – (Microfads) – SmartMobs – result of newtork effects. Tag Clouds – delicious.com/tags
Tribe.net Orkut Facebook MySpace, Twitter and LinkedIn being the most widely used in North America; Nexopia (mostly in Canada); Bebo, Hi5, StudiVZ (mostly in Germany), Decayenne, Tagged, XING;, Badoo and Skyrock in parts of Europe; Orkut and Hi5 in South America and Central America; and Friendster, Mixi, Multiply, Orkut, Wretch, Xiaonei and Cyworld
This slide is a summation of years of thought I have personally placed into the work I did at IBM addressing the business concerns of how to identify and measure the value of intellectual capital produced by the knowledge workers of an organization. The term is also used by engineers to decide the cost/value of uncertainty within a study. The avoid confusion, the full name of the concept is ‘Unstructured Information Value Analysis.’ Historically, businesses have spent a tremendous amount of energy in trying to determine the value of individual employees and their work. In the 1990s there were traditionally two ways to go about this process. The first was to create a profiling system. The profiling system would combine human resource information with a set of proclaimed skills that the employee would share about him/herself. The employee and the managers would author the profile record. Then the information would be shared with the rest of the organization. This approach led to a variety of problems. The profile tended to include biased and inaccurate information. Employees who wanted to appear as high achievers would pad their profiles to make them look like a stronger employee. Counter-intuitively, employees would do the opposite and purposely obscure their abilities for fear of being bothered and overwhelmed by others in the organization looking for their assistance. Another difficult problem was encouraging employees to continually maintain the data. Since employees are generally mobile within an organization and new challenges require different skills, many employee’s profiles with expire almost as soon as they were written. This was the nail in the coffin for manually fed profiling systems. They are still around but they are generally an expensive exercise in folly. The second way to attempt to derive this information was through automated systems and artificial intelligence. Instead of having employees profile themselves, information about their abilities could be derived from searching through the documents they author and handle. By employing a search engine to ‘spider’ through documents written, edited, or handled by an employee, the system could make several determinations about the subject. Enough to make a much more accurate picture of the expertise within the organization. IBM attempted this around 2000 with its Lotus Discovery Server product. The Discovery Server would produce a corporate taxonomy which included key concepts and terms that the business deemed valuable. The system would then search through all available documents to map the relevance of those documents to the taxonomy which they called a k-map. The system would then assign a value to the document based on it’s perceived usefulness by the organization. Usefulness was determined through employee interaction with the document. The number of links to and from the document increased it’s usefulness. So did the amount of times the document was opened, forwarded, and cited. Furthermore, to avoid the expiration of the document’s relevance, the system tracked updates and revisions. Tracking popularity and interaction with a document in the context of the organization’s taxonomy was a brilliant leap forward from the arbitrary self-promotion profiling systems that came before. However, the system was terribly flawed. The system needed to be massively scalable and would require an overwhelming amount of processing power for the end result. It proved to be economically unfeasible. Privacy was another issue. The system was only as good as the data that you fed it. IBM designers wanted to spider through employee email because they rightly decided that email applications contained the most relevant data. Although the system was only looking for key phrases and concepts that aligned to the K-Map taxonomy, there were very few customers that felt comfortable having the system riffle through their email. So, with both manual and automated profiling systems failing to deliver the promise of measuring and identifying the intellectual capital of unstructured data, companies surrendered to the realization that extracting that information was too complicated, flawed, and expensive. Then something brilliant yet simple happened in the commercial market space. Applications engaged in crowd sourcing started to appear. It is my observation that many of these application designers were not aware of crowd sourcing when they started building their applications and they stumbled into it with the help of the new design philosophy of Web 2.0. Wikis and Social Book-marking sites provide the best examples of crowd sourcing but it is the book-marking sites that broke the ceiling on Information Value Analysis. The concept of social (on-line) book-marking was very simple in it’s inception. It solved the problem accessing favorite websites without the need for a specific computer or web browser. By placing the bookmarks on the World Wide Web, users could access them anywhere. This was enough of an incentive to get a large consumer audience for the product. Yet it was the analysis that you could derive from the behavior of thousands of users that became extremely useful. By observing the book-marking choices of a community, you could extrapolate the value of the target website to that community and reveal it’s usefulness. Better yet, the cost of gathering that data was almost zero because the work is distributed across the community. Digg.com
Danah Boyd Persistence - PROS and CONS -what you say sticks around. Awareness - your availability, status, and geography are broadcasted. Scalability - What you say has the potential to permeate through the whole network fast! Invisible Audiences: Example - Facebook birthday Collapsed Contexts - Are you the professional or the high school buddy? All are together. Public vs. Private
Be Consistent - Make sure that once you decide to use an application, you visit it and contribute at regular intervals. Dummy Account - Always learn a technology with a dummy account before you commit to creating an on-line identity.
Social Media Overview
WEB 2.0 TYPES OF TOOLS AND THEIR USE CONCEPTS AND DEFINITIONS CONSUMER OPTIONS BUSINESS VALUE PARTICIPANT PROS AND CONS STRATEGIES FOR PARTICIPATION SOCIAL NETWORK WARS DEFINITION OF SOCIAL NETWORKING
Definition of Social Networking <ul><li> </li></ul> Social Networking: The use of applications that allow multimedia communication between a group who collectively subscribe to each other. Social Networks allow the audience to respond to the authors. A Brief History of Commercial Networking Software: AOL and AIM Match.com - Online Dating Service Friendster - 2003: no fake persona’s allowed LinkedIn - 2003: business only MySpace - All are welcome. Big for the Band set. Facebook - Extension of Existing Networks in Common. Highbrow.
Types of Tools <ul><li> </li></ul> Blogs (Blogger, Wordpress, Live Journal) MicroBlogs (Twitter) Social Networks (LinkedIn, Facebook) Social Bookmarks (Del.icio.us) Wikis (Wikipedia) Virtual Worlds (Second Life) Massive Multiplayer Gaming (World Of Warcraft) Media Sharing (Flickr, YouTube) Social News (Digg, Reddit, SlashDot)
Concepts & Phenomena Walled Gardens Social Bookmarking MicroBlogging Crowd Sourcing Syndication Network Effects Stickiness MashUps Dunbar’s Law Tag Clouds SmartMobs
Consumer Options <ul><li> </li></ul> LinkedIn Focused on working professionals, this networking site relies on it’s ability to create referrals through degrees of separation. Facebook Connections through established social circles. Popularity and front runner due to API and 3rd party business opportunity. MySpace Built for Bands and Fans. Orkut Bebo Nexopia
Business Value of Social Networks <ul><li> </li></ul> Internal Social Networking Solutions Corporate Intellectual Capital Knowledge Discovery Information Value Analysis (selfsource.wordpress.com) Community Building (inc. Geographic Flattening) Commercial Social Networking Solutions Relevant temporal and spatial information delivery. Target Marketing Peer driven customer support. (precarious) Consumer Data Collection
Participant Pros & Cons <ul><li> </li></ul> Persistence Awareness Scalability Invisible Audiences Collapsed Contexts Public vs. Private
Strategies for Participation Know the application purpose before you commit to it. Create dummy accounts and play with new technologies. Know what you want before you pick the application. Niche applications may be more appropriate than popular ones. The more you contribute to a SN application, the more relevant it becomes…and the more invested you become. Be consistent interacting through the applications you choose.
Social Network Wars <ul><li> </li></ul> Consumer Data Collection Open Search vs. Closed (Referral) Community Targeted information Delivery and Advertising Dollars