SlideShare une entreprise Scribd logo
1  sur  31
Social Browsing & Information Filtering in Social Media 010011011 01100001 11110 011 0101110011010 0111011010  001001 01111  00001001111010 011011010101 01001 01111 01100001001111010 100101111  00110  1001 10110101110100100 1010100100  1101  0010  00010111010011
 
Elements of Social Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Elements of Social Web ,[object Object],[object Object],[object Object],[object Object],[object Object]
What are we going to see ? ,[object Object],[object Object],[object Object],[object Object]
Social News Aggregation on Digg ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
So … ,[object Object],[object Object],[object Object]
Data  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dynamics of votes received by select stories on Digg over a period of four days. Dashes indicate story’s transition to the front page.
Cumulative number of times images in the Explore set (solid lines) and Random set (dashed lines) were viewed over the time of the tracking period
Social Network - Digg Scatter plot of the number of friends (contacts) vs reverse friends (contacts) for (a) the top 1020 Digg users
Social Networks -  Flicker Scatter plot of the number of friends (contacts) vs reverse friends (contacts) for 1100 Flickr users from the Apex, Explore and Random datasets story.
Social browsing on Digg Strength of the linear correlation coefficient between user’s success rate and the number of friends and reverse friends he has.
Number of voters who are also among the reverse friends of the user who submitted the story.
Claim 1: ,[object Object],[object Object],[object Object],[object Object],[object Object],By enabling users to quickly digg stories submitted by friends, social networks play an important role in promoting stories to the front page.
Claim 2:  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The data indicates that users do use the “see the stories my friends have dugg” portion of the Friends interface to find new interesting stories.
Social browsing in Flicker ,[object Object],[object Object],[object Object]
Histogram of the number of pools to which images from each set were submitted
Histogram of the number of tags assigned to the images
It represents: ,[object Object]
Social Networks & Comments ,[object Object]
Proportion of comments that came from the submitting user’s reverse contacts, mutual contacts and strangers vs the number of pools to which the image was submitted for the three datasets.
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Future of Social Web ,[object Object]
The Future of Social Web 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reference: ,[object Object]
[object Object]
Top User ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How friends Interface Works  Submitter “see” stories my friends submitted ,[object Object],See stories my friends dugg

Contenu connexe

Tendances

Predicting Discussions on the Social Semantic Web
Predicting Discussions on the Social Semantic WebPredicting Discussions on the Social Semantic Web
Predicting Discussions on the Social Semantic Web
Matthew Rowe
 
You're Hired: Examining Acceptance of Social Media Screening of Job Applicants
You're Hired: Examining Acceptance of Social Media Screening of Job ApplicantsYou're Hired: Examining Acceptance of Social Media Screening of Job Applicants
You're Hired: Examining Acceptance of Social Media Screening of Job Applicants
Toronto Metropolitan University
 
Altmetrics: Listening & Giving Voice to Ideas with Social Media Data
Altmetrics: Listening & Giving Voice to Ideas with Social Media DataAltmetrics: Listening & Giving Voice to Ideas with Social Media Data
Altmetrics: Listening & Giving Voice to Ideas with Social Media Data
Toronto Metropolitan University
 
Data Cleaning for social media knowledge extraction
Data Cleaning for social media knowledge extractionData Cleaning for social media knowledge extraction
Data Cleaning for social media knowledge extraction
Marco Brambilla
 

Tendances (20)

2015 pdf-marc smith-node xl-social media sna
2015 pdf-marc smith-node xl-social media sna2015 pdf-marc smith-node xl-social media sna
2015 pdf-marc smith-node xl-social media sna
 
Social Media Mining and Analytics
Social Media Mining and AnalyticsSocial Media Mining and Analytics
Social Media Mining and Analytics
 
Social Media Analysis: Present and Future
Social Media Analysis: Present and FutureSocial Media Analysis: Present and Future
Social Media Analysis: Present and Future
 
Predicting Discussions on the Social Semantic Web
Predicting Discussions on the Social Semantic WebPredicting Discussions on the Social Semantic Web
Predicting Discussions on the Social Semantic Web
 
2009-JCMC-Discussion catalysts-Himelboim and Smith
2009-JCMC-Discussion catalysts-Himelboim and Smith2009-JCMC-Discussion catalysts-Himelboim and Smith
2009-JCMC-Discussion catalysts-Himelboim and Smith
 
How information spreads on social networks when unexpected events occur
How information spreads on social networks when unexpected events occurHow information spreads on social networks when unexpected events occur
How information spreads on social networks when unexpected events occur
 
Data mining in social network
Data mining in social networkData mining in social network
Data mining in social network
 
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
 
You're Hired: Examining Acceptance of Social Media Screening of Job Applicants
You're Hired: Examining Acceptance of Social Media Screening of Job ApplicantsYou're Hired: Examining Acceptance of Social Media Screening of Job Applicants
You're Hired: Examining Acceptance of Social Media Screening of Job Applicants
 
About the Social Semantic Web
About the Social Semantic WebAbout the Social Semantic Web
About the Social Semantic Web
 
Citizen Sensing: Opportunities and Challenges in Mining Social Signals and Pe...
Citizen Sensing: Opportunities and Challenges in Mining Social Signals and Pe...Citizen Sensing: Opportunities and Challenges in Mining Social Signals and Pe...
Citizen Sensing: Opportunities and Challenges in Mining Social Signals and Pe...
 
2013 NodeXL Social Media Network Analysis
2013 NodeXL Social Media Network Analysis2013 NodeXL Social Media Network Analysis
2013 NodeXL Social Media Network Analysis
 
Altmetrics: Listening & Giving Voice to Ideas with Social Media Data
Altmetrics: Listening & Giving Voice to Ideas with Social Media DataAltmetrics: Listening & Giving Voice to Ideas with Social Media Data
Altmetrics: Listening & Giving Voice to Ideas with Social Media Data
 
Interlinking semantics, web2.0, and the real-world
Interlinking semantics, web2.0, and the real-worldInterlinking semantics, web2.0, and the real-world
Interlinking semantics, web2.0, and the real-world
 
2014 TheNextWeb-Mapping connections with NodeXL
2014 TheNextWeb-Mapping connections with NodeXL2014 TheNextWeb-Mapping connections with NodeXL
2014 TheNextWeb-Mapping connections with NodeXL
 
Data Cleaning for social media knowledge extraction
Data Cleaning for social media knowledge extractionData Cleaning for social media knowledge extraction
Data Cleaning for social media knowledge extraction
 
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
 
Predicting what gets ‘Likes’ on Facebook: case study of BlogTO
Predicting what gets ‘Likes’ on Facebook:  case study of BlogTOPredicting what gets ‘Likes’ on Facebook:  case study of BlogTO
Predicting what gets ‘Likes’ on Facebook: case study of BlogTO
 
Recommendation System Using Social Networking
Recommendation System Using Social Networking Recommendation System Using Social Networking
Recommendation System Using Social Networking
 
Jf2516311637
Jf2516311637Jf2516311637
Jf2516311637
 

En vedette (7)

Web I - 04 - Forms
Web I - 04 - FormsWeb I - 04 - Forms
Web I - 04 - Forms
 
ACM Communication March 2009
ACM Communication March 2009ACM Communication March 2009
ACM Communication March 2009
 
How To Create Personal Web Pages On My Web
How To Create Personal Web Pages On My WebHow To Create Personal Web Pages On My Web
How To Create Personal Web Pages On My Web
 
WEB I - 09 - Usability
WEB I - 09 - UsabilityWEB I - 09 - Usability
WEB I - 09 - Usability
 
Talent attraction & entrepreneurship. Global Metro Conference
Talent attraction & entrepreneurship. Global Metro ConferenceTalent attraction & entrepreneurship. Global Metro Conference
Talent attraction & entrepreneurship. Global Metro Conference
 
41 Ways to Boost Your Year-End Appeal with Social Media
41 Ways to Boost Your Year-End Appeal with Social Media41 Ways to Boost Your Year-End Appeal with Social Media
41 Ways to Boost Your Year-End Appeal with Social Media
 
Conscious Commandments 2.6.2009
Conscious Commandments 2.6.2009Conscious Commandments 2.6.2009
Conscious Commandments 2.6.2009
 

Similaire à Social Information & Browsing March 6

Similaire à Social Information & Browsing March 6 (20)

Data mining for social media
Data mining for social mediaData mining for social media
Data mining for social media
 
SocialCom09-tutorial.pdf
SocialCom09-tutorial.pdfSocialCom09-tutorial.pdf
SocialCom09-tutorial.pdf
 
Social networks
Social networksSocial networks
Social networks
 
Social Web .20 Class Week 6: Lightweight Authoring, Blogs, Wikis
Social Web .20 Class Week 6: Lightweight Authoring, Blogs, WikisSocial Web .20 Class Week 6: Lightweight Authoring, Blogs, Wikis
Social Web .20 Class Week 6: Lightweight Authoring, Blogs, Wikis
 
Social Search Arnaud Fischer
Social Search Arnaud FischerSocial Search Arnaud Fischer
Social Search Arnaud Fischer
 
Frontiers of Computational Journalism week 3 - Information Filter Design
Frontiers of Computational Journalism week 3 - Information Filter DesignFrontiers of Computational Journalism week 3 - Information Filter Design
Frontiers of Computational Journalism week 3 - Information Filter Design
 
Understanding Public Sentiment: Conducting a Related-Tags Content Network Ext...
Understanding Public Sentiment: Conducting a Related-Tags Content Network Ext...Understanding Public Sentiment: Conducting a Related-Tags Content Network Ext...
Understanding Public Sentiment: Conducting a Related-Tags Content Network Ext...
 
Understanding Public Sentiment: Conducting a Related-Tags Content Network E...
Understanding Public Sentiment: Conducting a Related-Tags Content Network E...Understanding Public Sentiment: Conducting a Related-Tags Content Network E...
Understanding Public Sentiment: Conducting a Related-Tags Content Network E...
 
UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ an...
UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ an...UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ an...
UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ an...
 
Information Retrieval and Social Media
Information Retrieval and Social MediaInformation Retrieval and Social Media
Information Retrieval and Social Media
 
Picturing the Social: Talk for Transforming Digital Methods Winter School
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolPicturing the Social: Talk for Transforming Digital Methods Winter School
Picturing the Social: Talk for Transforming Digital Methods Winter School
 
Profiling User Interests on the Social Semantic Web
Profiling User Interests on the Social Semantic WebProfiling User Interests on the Social Semantic Web
Profiling User Interests on the Social Semantic Web
 
National Geographic - Omniture Cafe 6/11/09
National Geographic - Omniture Cafe 6/11/09National Geographic - Omniture Cafe 6/11/09
National Geographic - Omniture Cafe 6/11/09
 
Think Link: Network Insights with No Programming Skills
Think Link: Network Insights with No Programming SkillsThink Link: Network Insights with No Programming Skills
Think Link: Network Insights with No Programming Skills
 
DMI Summer 2010 - Final Presentations
DMI Summer 2010 - Final PresentationsDMI Summer 2010 - Final Presentations
DMI Summer 2010 - Final Presentations
 
Jx2517481755
Jx2517481755Jx2517481755
Jx2517481755
 
Jx2517481755
Jx2517481755Jx2517481755
Jx2517481755
 
Mining and analyzing social media part 1 - hicss47 tutorial - dave king
Mining and analyzing social media   part 1 - hicss47 tutorial - dave kingMining and analyzing social media   part 1 - hicss47 tutorial - dave king
Mining and analyzing social media part 1 - hicss47 tutorial - dave king
 
Detecting Spam Tags Against Collaborative Unfair Through Trust Modelling
Detecting Spam Tags Against Collaborative Unfair Through Trust ModellingDetecting Spam Tags Against Collaborative Unfair Through Trust Modelling
Detecting Spam Tags Against Collaborative Unfair Through Trust Modelling
 
User Behaviour Pattern Recognition On Twitter Social Network
User Behaviour Pattern Recognition On Twitter Social NetworkUser Behaviour Pattern Recognition On Twitter Social Network
User Behaviour Pattern Recognition On Twitter Social Network
 

Dernier

Salient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsSalient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functions
KarakKing
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 

Dernier (20)

UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfUGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
 
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701
 
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptxSKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
 
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17  How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17  How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
Salient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsSalient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functions
 
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning PresentationSOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
Single or Multiple melodic lines structure
Single or Multiple melodic lines structureSingle or Multiple melodic lines structure
Single or Multiple melodic lines structure
 
Dyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxDyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptx
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
Food safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdfFood safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdf
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024
 

Social Information & Browsing March 6

  • 1. Social Browsing & Information Filtering in Social Media 010011011 01100001 11110 011 0101110011010 0111011010 001001 01111 00001001111010 011011010101 01001 01111 01100001001111010 100101111 00110 1001 10110101110100100 1010100100 1101 0010 00010111010011
  • 2.  
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. Dynamics of votes received by select stories on Digg over a period of four days. Dashes indicate story’s transition to the front page.
  • 11. Cumulative number of times images in the Explore set (solid lines) and Random set (dashed lines) were viewed over the time of the tracking period
  • 12. Social Network - Digg Scatter plot of the number of friends (contacts) vs reverse friends (contacts) for (a) the top 1020 Digg users
  • 13. Social Networks - Flicker Scatter plot of the number of friends (contacts) vs reverse friends (contacts) for 1100 Flickr users from the Apex, Explore and Random datasets story.
  • 14. Social browsing on Digg Strength of the linear correlation coefficient between user’s success rate and the number of friends and reverse friends he has.
  • 15. Number of voters who are also among the reverse friends of the user who submitted the story.
  • 16.
  • 17.
  • 18.
  • 19. Histogram of the number of pools to which images from each set were submitted
  • 20. Histogram of the number of tags assigned to the images
  • 21.
  • 22.
  • 23. Proportion of comments that came from the submitting user’s reverse contacts, mutual contacts and strangers vs the number of pools to which the image was submitted for the three datasets.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.