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Why We Twitter?

Why We Twitter?. Akshay Java Tim Finin University of Maryland, Baltimore County. Understanding Microblogging Usage and Communities. Xiaodan Song Belle Tseng NEC Laboratories America Inc. What is Twitter?. Micro-Blogging, Social Networking Service

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Why We Twitter?

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  1. Why We Twitter? Akshay Java Tim Finin University of Maryland, Baltimore County Understanding Microblogging Usage and Communities Xiaodan Song Belle Tseng NEC Laboratories America Inc. WebKDD and 1st SNA-KDD Workshop

  2. What is Twitter? • Micro-Blogging, Social Networking Service • Users send updates/tweets via SMS, IM or Web • Light-weight blogging, short posts (140 characters or less) Akshay Java

  3. What is Twitter? Current Status Twitter post Friends Easily share status messages Akshay Java

  4. MICRO-BLOGS Akshay Java

  5. “Ambient intimacy is about being able to keep in touch with people with a level ofregularity and intimacy that you wouldn’t usually have access to, because time and space conspire to make it impossible. “ - Leisa Reichelt Twitter, Flickr and other Social Media sites. Ambient Intimacy Akshay Java

  6. Twitter! Twitter! Twitter! Disclaimer: No association to Twitter Inc/ Obvious corp. Akshay Java

  7. Motivation • Goal: Study Micro-blogging usage, user-intentions and community structure. • Motivation: • What is the excitement about? • What is the user-intention in micro-blogging? • How is this form of communication different? Akshay Java

  8. Outline Why We Twitter? Micro-blogging Usage User Intentions Community-based Intentions Content-based Intentions Akshay Java

  9. Social Media Social media describes the online technologies and practices that people use to share opinions, insights, experiences, perspectives with each other. ~ Wikipedia 07 Akshay Java

  10. Social Media Social media describes the online technologies and practices that people use to share opinions, insights, experiences, perspectives and engage with each other. Examples: Blogs, Wikis, Flickr, YouTube, Micro-blogs… Akshay Java

  11. Outline Why We Twitter? Micro-blogging Usage User Intentions • USAGE • Popularity of Twitter? • Is it very different from blogs? • What is it’s adoption across the world? Community-based Intentions Content-based Intentions Akshay Java

  12. Dataset Description • Constructed by monitoring the Public timeline of posts • 2 month period (04/07-05/07) • 1,348,543 posts • 76,177 Users • 829,053 friend relation between them • Used the Twitter API for accessing user social network Akshay Java

  13. The Twitter Phenomenon • Twitter’s popularity increased after winning the Web award at SXSW conference, March 2007. Currently, Twitter is the most popular micro-blogging tool. Source: ComScore Akshay Java

  14. Growth Rate Dedicated user-base generating new updates/tweets. Slow down in growth of new users joining the network. Akshay Java

  15. Network Statistics Degree distributions similar to Blogs, Web… Akshay Java

  16. Network Statistics Network Statistics However, higher reciprocal linking and clustering coefficient indicates mutual acquaintance. Akshay Java

  17. User Retention User has at least one post in the week ACTIVE RETAINED Active user, who reposts the following week Shows a continued activity and retention on Twitter Akshay Java

  18. Geographical Spread Cross Continent Social Network Social network crosses geographical boundaries Continental Network Properties Top Cities: Tokyo, NY, SF, Seattle, LA, Chicago, Toronto, Austin, Singapore, Madrid Global popularity Higher reciprocity in Europe and Asia Akshay Java

  19. Outline Why We Twitter? Micro-blogging Usage User Intentions • USER INTENTION • What are we using Twitter for? • Are there communities? • If so, what are the community-level intentions? Community-based Intentions Content-based Intentions Akshay Java

  20. User Intentions Using Link Structure: • Information source Such users have a number of followers ( include bots like forecast, stock, CNN breaking news, etc.) • Information seeker Such users may post infrequently, but have a number of connections • Friendship relation Most user’s social network is within mutual acquaintances Using Content: • Daily chatter dinner, work, movie… • Conversations (@) Reply to a specific person @ev • Sharing URLs Sharing URLs through tinyURL • Commenting on News Number of automated RSS to Twitter bots posting news Akshay Java

  21. Communities in Twitter Hubs and Authorities • First find Hubs and Authorities using HITS • Consider only bidirectional links • Clique Percolation Method (CPM) to find overlapping communities A-list bloggers and personalities are on Twitter Scobleizer, SteveRubel, JasonCalacanis, SteveJobs JohnEdwards, BarakObama, et al. Akshay Java

  22. Clique Percolation Method (CPM) Example Gaming Community Basic Idea Two nodes belong to the same community if they can be connected through adjacent k-cliques. I. Derenyi, G. Palla, and T. Vicsek. Clique percolation in random networks. Physical Review Letters, 94:160202, 2005. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435:814, 2005. Akshay Java

  23. com:175 twitter:134 just:133 like:86 good:82 tinyurl:75 time:74 new:74 jasona:73 going:68 day:63 don:61 work:58 think:56 ll:54 scottw:54 today:52 hkarthik:50 nice:49 getting:47 got:47 really:46 yeah:44 need:43 watching:41 love:41 night:40 home:40 com:198 twitter:132 just:109 tinyurl:87 going:59 blog:56 like:55 good:51 new:50 url:50 day:49 people:46 time:45 today:45 google:42 don:41 think:40 night:38 ll:38 need:35 got:33 ireland:33 great:31 looking:29 work:29 thanks:28 video:26 INFORMATION HUB com:93 twitter:74 just:35 new:32 tinyurl:29 going:24 ll:22 blog:21 jaiku:21 don:21 leo:21 flickr:21 like:19 video:18 google:18 today:18 feeds:18 getting:16 yeah:16 good:15 people:15 com:93 twitter:76 tinyurl:34 just:32 new:28 video:26 going:24 ll:22 jaiku:22 blog:21 leo:21 like:19 don:19 gamerandy:19 yeah:18 google:17 live:16 people:16 got:16 know:15 time:15 com:121 twitter:76 just:50 ustream:43 tv:42 live:42 today:39 hawaii:36 day:33 new:33 time:33 good:33 video:32 leo:30 work:30 like:28 watching:28 tinyurl:28 Akshay Java Information Source: Communities connected via Robert Scoble, an A-list blogger

  24. Key Terms going:222 just:218 work:170 night:143 bed:140 time:139 good:137 com:130 lost:124 day:122 home:112 listening:111 today:100 new:98 got:97 gspn:92 watching:92 kids:88 morning:81 twitter:79 getting:77 tinyurl:75 lunch:74 like:72 podcast:72 watch:71 ready:70 tv:69 need:64 live:61 tonight:61 trying:58 love:58 cliff:58 dinner:56 INFORMATION BRIDGE Key Terms just:312 com:180 work:180 time:149 listening:147 home:145 going:139 day:134 got:126 today:124 good:116 bed:114 night:112 tinyurl:97 getting:88 podcast:87 dinner:85 watching:83 like:78 mass:78 lunch:72 new:72 ll:70 tomorrow:69 ready:64 twitter:62 working:61 tonight:61 morning:58 need:58 great:58 finished:55 tv:54 Akshay Java Information Source, Information Seeker: Different roles in different communities

  25. STAR NETWORKS / SMALL CLIQUES Akshay Java Friendship-relation: Small groups among friends/co-workers

  26. Outline Why We Twitter? Micro-blogging Usage User Intentions • USER INTENTION • What are the distinctive terms? • Are there any trends? Community-based Intentions Content-based Intentions Akshay Java

  27. Wisdom of the Crowds log-likelihood ratio Popular topics: Activities, Current Events, TV shows/Entertainment Akshay Java

  28. Twitterment http://twitterment.umbc.edu Search and Trend analytics on Twittersphere work lunch dinner coffee chipotle lunch panera dinner Akshay Java

  29. Outlook on Micro-Blogging • The future is here! Twitter,Jaiku,Pownce,FaceBook • Status message is now public • Ambient intimacy • Information sharing • Thoughts on new applications • Users can play different roles in different communities • Number of updates received can be quite overwhelming • New tools and services would benefit from allowing greater personalization based on user-intentions e.g. Separating work and friend social network Akshay Java

  30. Conclusions • First study of the Microblogging phenomenon. • Popularity of Micro-blogging due to the combined benefits of • Light-weight blogging • and the ability to share information in the social network. • Main user-intentions • Information sharing • Information seeking • Friendship • Users generated content includes: Status updates, daily chatter, sharing links/News, etc. • Future Work • Automatically classifying the different intentions. • Finding frequent patterns in community structure. Akshay Java

  31. Thank You! Questions? Thanks to Twitter Inc. for providing the Twitter API. http://twitter.com/akshayjava Akshay Java

  32. Backup Slides Akshay Java

  33. User Intentions Akshay Java

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