Lecture Overview Web 2.0, Tagging, Multimedia, • Introduction to Web 2.0 Folksonomies, Lecture, • Overview of Tagging Systems – Overview of tagging Important, Must Attend, … – Design and attributes Martin Halvey – Case Studies • Other Web 2.0 Technologies • Conclusions Web 2.0 Definition Web 2.0 Definition “Web 2.0 is the business revolution in “An idea in people's heads rather than a the computer industry caused by the reality. It’s actually an idea that the move to the Internet as platform, and an reciprocity between the user and the attempt to understand the rules for provider is what's emphasised. In other success on that new platform” words, genuine interactivity if you like, simply because people can upload as Tim O'Reilly (2006-12-10). Web 2.0 well as download” Compact Definition: Trying Again Stephen Fry: Web 2.0 1 Web 2.0 Definition Web 2.0 “piece of jargon” “Participatory Web” “nobody really knows what it means” “if Web 2.0 for you is blogs and wikis, Bart Decrem (2006-06-13). Introducing then that is people to people. But that Flock Beta 1. Flock official blog was what the Web was supposed to be all along” -Tim Berners-Lee Web 2.0 Web 2.0 Feature/Techniques • Allow users to do more than retrieve • Rich Internet Application techniques, AJAX information • Semantically valid XHTML and HTML • "Network as platform" computing, allowing • REST and/or XML- and/or JSON-based APIs users to run software applications entirely through a browser • Cascading Style Sheets • In contrast to systems which categorise users • RSS or Atom feeds into roles with varying degrees of functionality • Mash Ups • Re-use of existing technologies, some new • Weblog publishing • Wiki or Forum • Social Networking 2 Tagging Folksonomies • A tag is a (relevant) keyword or term • A folksonomy is a user generated taxonomy associated with or assigned to a piece used to categorize and retrieve web content of information (e.g. a picture, article, or such as Web pages, photographs and Web video clip), thus describing the item and links, using open-ended labels called tags enabling keyword-based classification • Intended to make a body of information increasingly easy to search, discover, and of information. navigate over time • Usually chosen informally and • Normally used online but they can arise in a personally by item author/creator or by number of other contexts its consumer/viewers/community Folksonomies and Semantic Folksonomies Web • Well-developed folksonomy is ideally • The Semantic Web is an evolving extension accessible as a shared vocabulary that of the WWW in which content is expressed is both created by, and familiar to, its not only in a format that can be read and used by automated tools, as well as natural primary users language • Folksonomy tools are not part of the • Folksonomies can be used in conjunction with WWW protocols semantic web technologies to provide rich • Arise where special provisions are descriptions, but not quite yet. made • However metadata from folksonomies is not • Particularly useful when no other text is consistent or reliable available 3 Bridging the Semantic Gap Example Tagging Systems • The difference between low-level data • Del.ioco.us (http://del.ioco.us) representation of multimedia and the • CiteULike (http://www.citelike.org) higher level concepts users associate • Flickr (http://www.flickr.com) with the same multimedia • YouTube (http://www.youtube.com) • Providing annotations can alleviate this problem • Last.fm (http://www.last.fm) • Need to improve annotations to • Technorati (http://www.technorati.com) overcome this problem • ESP Game (http://www.espgame.org) Types of Tags System Design and Attributes • Identifying What (or Who) it is About • Tagging Rights • Identifying What it Is • Tagging Support • Identifying Who Owns It • Aggregation • Refining Categories • Type of Object • Identifying Qualities or Characteristics • Source of Material • Self Reference • Resource Connectivity • Task Organising • Social Connectivity 4 User Incentives Tagging/Folksonomies Pros • Future Retrieval • Easy to use • Contribution and Sharing • Intuitive • Attract Attention • Cheap way of getting annotations • Play and Competition • Gives new users quick and simple • Self Presentation impression of content • Opinion Expression • Can aid browsing and search Tagging/Folksonomies Cons Tagging Conclusions • Inaccurate and irrelevant tags • Provide keywords to describe objects • Lack of stemming • Collaborative tagging provides a • Freely chosen tags can result in community view of object – Synonyms • Number of pros and cons to using – Homonyms tagging – Polysemy • Number of design approaches can be taken • Number of incentives for users to tag 5 Tagging Case Studies Tagging Case Studies • A number of large studies of tagging YouTube Flickr Del.ico.us have taken place • Focus on different aspects of tagging Rights Self Tagging Self Tagging Free for all • We will focus on three analyses of Support Blind tagging Blind tagging Blind tagging different systems, the systems analysed Type of Videos Photographs Links are: Object – Del.icio.us Source Participants Participants Web resource – Flickr – YouTube Del.icio.us – Golder and Del.icio.us – Golder and Huberman Huberman • Provide an analysis of collaborative • Analyse two del.icio.us datasets tagging systems • Dataset 1 • They study the del.icio.us system for – 212 URL’s organising bookmarks – 19,422 bookmarks • Analyse the structure of collaborative • Dataset 2 tagging systems as well as their – 229 users dynamical aspects – 68,668 bookmarks 6 Del.icio.us – Golder and Del.icio.us – Golder and Huberman Huberman • Users have variety in use of tags, some • Appears that most tags are added for have many, some have few personal use • Tags vary in frequency of use • Never the less they are still useful to the • However, stable patterns emerge in public tags • They believe that consensus choices • Adding numbers of infrequently used that emerge may be used on a large tags/opinions, does not disrupt the scale to describe and organise how web general consensus documents interact with one another, and also can be used to make recommendations Flickr – Marlow et al Flickr – Marlow et al • Present a study of the photo sharing • Present a model for tagging systems and tagging system Flickr • Compare tagging on Flickr with that of • Looked at the dynamics of the Flickr del.icio.us and hope to expose interesting trends • Dynamics of interaction and and topics in the Flickr participation are different to Del.icio.us • Set of 25,000 users for individual • To be expected as they are different analysis models of tagging, and different user • Set of 2,500 users for network analysis incentives 7 YouTube – Halvey and Keane YouTube – Halvey and Keane • More interested in user goals when • Views of pages match distributions using and searching in a tag based found in web search system • Views from navigation match • Examine why some videos are viewed distributions found in web navigation more often than others • Found that in general the more tags that • Investigation of user interactions to see users provide the more likely that a if they vary in Web 2.0 video will be watched, up to a certain • Analysed104,465 video pages from point 57,639 users YouTube – Halvey and Keane Tagging Case Studies • In general users are consumers rather than • Briefly seen a number of analyses of creators of resources for the service tagging systems • Users of YouTube do not use the social tools • A number of different approaches were unless they gain a benefit investigated, from a number of different • Videos are recommended because they are aspects popular, not popular because they are recommended • Although tagging and tag based • Videos receive the majority of their views in systems seem random, there are a the first couple of days number of regularities 8 Tag Clouds Tag Clouds • Visual depiction of user generated tags • There are a number of uses for tag • Normally there is a weighting clouds, these include associated with the tags – Browsing • Can be alphabetised – Search • Importance of a tag can be represented – Impression Formation/Gisting by font size or colour – Recognition/Matching • Tags are usually hyperlinks that lead to further information Tag Clouds – Rivadeneira et Tag Clouds – Rivadeneira et al al • Investigated the use of tag clouds for • The use of different font sizes had an impression formation effect • Experiment 1 • Layout has an influence on the – Influences of tag cloud attributes on low- effectiveness of the users in performing level cognitive processes tasks • Experiment 2 • Some of the results can be attributed to – Effect of font size and word layout on westernised reading impression formation and memory 9 Tag Clouds – Halvey and Tag Clouds – Halvey and Keane Keane • Investigated browsing and search • Alphabetisation can aid users to find • Participants carried out search tasks on information more easily and quickly tags presented 6 different formats • Font size is very important for how • Font size, alphabetisation and layout quickly and easily users find information were varied for each of the layouts • Position of tags is also very important where appropriate. • It appears that users scan lists and • The effect of each scenario on task clouds rather than read them completion time was investigated Other Web 2.0 Technologies Rich Internet Applications • Rich Internet Applications • Web applications that have the features and • Really Simple Syndication (RSS) functionality of desktop applications • typically transfer the processing necessary for • Cascading Style Sheets (CSS) the user interface to the web client • Rest, XML, JSON • Keep the bulk of the data back on the • Mash Up application server • Wiki • Run in a web browser, and/or do not require software installation • Weblogs • Run locally in a secure environment called a • Social Networking sandbox 10 Really Simple Syndication Rich Internet Applications (RSS) • Can provide additional support for • Web feed formats used to publish frequently multimedia, which allow more updated content, e.g.