NTU Natural Language Processing Lab. 1 An Analysis of Effectiveness of Tagging in Blogs Christopher H. Brooks and Nancy Montanez University of San Francisco.

Slides:



Advertisements
Similar presentations
HT06, Position Paper, Tagging, Taxonomy, Flickr, Academic Article, ToRead, Presentation Cameron Marlow, Mor Naaman, danah boyd, Marc Davis Yahoo! Research.
Advertisements

1 What feeds are there and how to find them Patti Biggs & Anne Welsh.
Engineering Village ™ Basic Searching.
Bring Order to Your Photos: Event-Driven Classification of Flickr Images Based on Social Knowledge Date: 2011/11/21 Source: Claudiu S. Firan (CIKM’10)
GENERATING AUTOMATIC SEMANTIC ANNOTATIONS FOR RESEARCH DATASETS AYUSH SINGHAL AND JAIDEEP SRIVASTAVA CS DEPT., UNIVERSITY OF MINNESOTA, MN, USA.
Stephan Gammeter, Lukas Bossard, Till Quack, Luc Van Gool.
College for Professional Studies Topic : Social Bookmarking Name: Santosh Devbhandari Course and Sem: B.Sc. IT 4 th SEM.
Explorations in Tag Suggestion and Query Expansion Jian Wang and Brian D. Davison Lehigh University, USA SSM 2008 (Workshop on Search in Social Media)
OntoBlog: Linking Ontology and Blogs Aman Shakya 1, Vilas Wuwongse 2, Hideaki Takeda 1, Ikki Ohmukai 1 1 National Institute of Informatics, Japan 2 Asian.
Enhancing Research Projects with Environmental Informatics and Web Technologies.
Flickr Tags Network Mustafa Kilavuz. Tags A tag is a keyword Search, spam detection, reputation systems, personal organization and metadata.
Existing tools to analyze Blogosphere. IceRocket Ice Spy – Spy on what others are searching. Blog Trends – Identifies the trend of particular terms in.
Tagging Systems Austin Wester. Tags A keywords linked to a resource (image, video, web page, blog, etc) by users without using a controlled vocabulary.
Tagging Systems Mustafa Kilavuz. Tags A tag is a keyword added to an internet resource (web page, image, video) by users without relying on a controlled.
Del.icio.us Bill G. Kelm IDS 150: Research in the Information Age April 3, 2007.
The Social Web: A laboratory for studying s ocial networks, tagging and beyond Kristina Lerman USC Information Sciences Institute.
Overview of Search Engines
What’s The Difference??  Subject Directory  Search Engine  Deep Web Search.
UKOLN is supported by: Introduction To Blogs And Social Networks For Heritage Organisations: Introduction To The Workshop Brian Kelly UKOLN University.
Introduction to social software in the enterprise “There’s something happening here, what it is ain’t exactly clear.” - Quoted from John Hagel on Web2.0.
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation SEASR Overview Loretta Auvil and Bernie Acs National.
Tag-based Social Interest Discovery
Tag-based Social Interest Discovery 2009/2/9 Presenter: Lin, Sin-Yan 1 Xin Li, Lei Guo, Yihong Zhao Yahoo! Inc WWW 2008 Social Networks & Web 2.0.
Web 2.0: Concepts and Applications 4 Organizing Information.
Tag Clouds Revisited Date : 2011/12/12 Source : CIKM’11 Speaker : I- Chih Chiu Advisor : Dr. Koh. Jia-ling 1.
By : Garima Indurkhya Jay Parikh Shraddha Herlekar Vikrant Naik.
1 Web Search Personalization via Social Bookmarking and Tagging Michael G. Noll & Christoph Meinel Hasso-Plattner-Institut an der Universit¨at Potsdam,
Andriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher, and Robin Burke
No Title, yet Hyunwoo Kim SNU IDB Lab. September 11, 2008.
Integrating Technology for Instruction and Learning Jennifer Verschoor & Evelyn Izquierdo April 3, 2009.
Tag Data and Personalized Information Retrieval 1.
Thanks to Bill Arms, Marti Hearst Documents. Last time Size of information –Continues to grow IR an old field, goes back to the ‘40s IR iterative process.
School Library 2.0: An Introduction Carrie Gits Assistant Director of Reference Alvin Sherman Library Nova Southeastern University February 1, 2008.
29-30 October, 2006, Estonia 1 IST4Balt Information analysis using social bookmarking and other tools IST4Balt Information analysis using social bookmarking.
ON THE SELECTION OF TAGS FOR TAG CLOUDS (WSDM11) Advisor: Dr. Koh. Jia-Ling Speaker: Chiang, Guang-ting Date:2011/06/20 1.
From Social Bookmarking to Social Summarization: An Experiment in Community-Based Summary Generation Oisin Boydell, Barry Smyth Adaptive Information Cluster,
Puget Sound Information Challenge Experiences and Lessons Learned.
NTU Natural Language Processing Lab. 1 Investment and Attention in the Weblog Community Advisor: Hsin-Hsi Chen Speaker: Sheng-Chung Yen.
Let's play “tag”. what is a tag? A tag is a keyword or descriptive term associated with an item as means of classification by means of a folksonomy...
Technorati CI by Anissa Malady LIBR 282-Social Media for Competitive & Company Research S.Brown C. Confetti-Higgins.
Contextual Ranking of Keywords Using Click Data Utku Irmak, Vadim von Brzeski, Reiner Kraft Yahoo! Inc ICDE 09’ Datamining session Summarized.
Information Retrieval Effectiveness of Folksonomies on the World Wide Web P. Jason Morrison.
Enhancing Cluster Labeling Using Wikipedia David Carmel, Haggai Roitman, Naama Zwerdling IBM Research Lab (SIGIR’09) Date: 11/09/2009 Speaker: Cho, Chin.
Center for E-Business Technology Seoul National University Seoul, Korea Social Ranking: Uncovering Relevant Content Using Tag-based Recommender Systems.
Date : 2013/03/18 Author : Jeffrey Pound, Alexander K. Hudek, Ihab F. Ilyas, Grant Weddell Source : CIKM’12 Speaker : Er-Gang Liu Advisor : Prof. Jia-Ling.
A Method for Classification of Data with Tags based on Support Vector Machine (Working Title) March 22, 2007 SNU iDB Lab. Byunggul Koh.
Social Bookmarking with del.icio.us. What is del.icio.us? Social Software Store your bookmarks online Tag your bookmarks Share your bookmarks with others.
2009/05/04 Y.H.Chang 1Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Advisor: Hsin-Hsi Chen Reporter: Y.H Chang
Automatic Video Tagging using Content Redundancy Stefan Siersdorfer 1, Jose San Pedro 2, Mark Sanderson 2 1 L3S Research Center, Germany 2 University of.
What is Web 2.0? And why should you care?. Web 2.0 is…  Web 2.0 is the era when people have come to realize that it's not the software that enables the.
Web Review The Web Web 1.0 Web 2.0 Future of the Web Internet Programming - Chapter 01:XHTML1.
From Text to Image: Generating Visual Query for Image Retrieval Wen-Cheng Lin, Yih-Chen Chang and Hsin-Hsi Chen Department of Computer Science and Information.
Google Custom Search Engine Presented by David Bickford Director, Arizona Health Sciences Library at the Phoenix Biomedical Campus October 21, 2015.
+ User-induced Links in Collaborative Tagging Systems Ching-man Au Yeung, Nicholas Gibbins, Nigel Shadbolt CIKM’09 Speaker: Nonhlanhla Shongwe 18 January.
© 2006 Jan-Paul Nachtwey & Enrico Schnepel, FHTW Berlin Social Software Social Software and its influences.
Web Information Retrieval Prof. Alessandro Agostini 1 Context in Web Search Steve Lawrence Speaker: Antonella Delmestri IEEE Data Engineering Bulletin.
1 One Table Stores All: Enabling Painless Free-and-Easy Data Publishing and Sharing Bei Yu 1, Guoliang Li 2, Beng Chin Ooi 1, Li-zhu Zhou 2 1 National.
Controlled Vocabulary & Thesaurus Design Types of Controlled Vocabularies.
1 The EigenRumor Algorithm for Ranking Blogs Advisor: Hsin-Hsi Chen Speaker: Sheng-Chung Yen ( 嚴聖筌 )
NTU Natural Language Processing Lab. 1 Blog Track Open Task: Spam Blog Classification Advisor: Hsin-Hsi Chen Speaker: Sheng-Chung Yen Date: 2007/01/08.
Tag File System in Cloud 林敬棋 NTU CSIE D Research Statement This project aims at adding tags to the files in the cloud storage. ◦ A tag is a keyword.
ENHANCING CLUSTER LABELING USING WIKIPEDIA David Carmel, Haggai Roitman, Naama Zwerdling IBM Research Lab SIGIR’09.
Bringing Order to the Web : Automatically Categorizing Search Results Advisor : Dr. Hsu Graduate : Keng-Wei Chang Author : Hao Chen Susan Dumais.
Finding similar items by leveraging social tag clouds Speaker: Po-Hsien Shih Advisor: Jia-Ling Koh Source: SAC 2012’ Date: October 4, 2012.
What part of the URL tell the computer to find the server?
UKOLN is supported by: Using Blogs Effectively Within Your Library: Introduction A Half-Day Workshop Brian Kelly UKOLN University of Bath Bath, UK
Web2.0 Services and the Management of Academic Libraries Dr. Christian Hänger Christine Krätzsch.
Improving Search Relevance for Short Queries in Community Question Answering Date: 2014/09/25 Author : Haocheng Wu, Wei Wu, Ming Zhou, Enhong Chen, Lei.
Artificial Intelligence Techniques
Presentation transcript:

NTU Natural Language Processing Lab. 1 An Analysis of Effectiveness of Tagging in Blogs Christopher H. Brooks and Nancy Montanez University of San Francisco Advisor: Hsin-Hsi Chen Speaker: Sheng-Chung Yen ( 嚴聖筌 )

2 NTU Natural Language Processing Lab. Agenda Introduction Technorati Uses of Tags Experiments Conclusion & Future Work References

3 NTU Natural Language Processing Lab. Introduction Tagging Tag are collections of keywords that are attached to blog entries, obviously to help describe the entry. Folksonomy A labeling system that enables Internet users to categorize contents. del.icio.us flickr

4 NTU Natural Language Processing Lab. Technorati Technorati is a search engine and aggregation site that focuses on indexing and collecting all of the information in blogosphere.

5 NTU Natural Language Processing Lab. Uses of Tags (1/2) The 250 most popular tags on Technorati, as of October 6, 2005.

6 NTU Natural Language Processing Lab. Uses of Tags (2/2) The greatest potential uses of tags is as a means for annotating particular articles and indicating their content. It may be the case that less popular tags are better at describing the subject of specific articles.

7 NTU Natural Language Processing Lab. Experiments Hypothesis A cluster of documents that shared a tag should be more similar than a randomly constructed set of documents. 350 most popular tags from Technorati. For each tag, 250 most resent articles.

8 NTU Natural Language Processing Lab. Experiments

9 NTU Natural Language Processing Lab. Experiments

10 NTU Natural Language Processing Lab. Experiments

11 NTU Natural Language Processing Lab. Experiments

12 NTU Natural Language Processing Lab. Experiments Automated tagging Extracting the three words with the top TFIDF score. These words were then treated as the article’s “autotags.”

13 NTU Natural Language Processing Lab. Experiments

14 NTU Natural Language Processing Lab. Conclusion & Future Work Tags help users group their blog entries into broad categories.

15 NTU Natural Language Processing Lab. References [1] Christopher H Brooks and Nancy Montanez, An Analysis of the Effectiveness of Tagging in Blogs, AAAI [2] Wikipedia,