1. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 1 S OCIAL M EDIA M INING CSE 494/598, F ALL 2011 Huan Liu Huan.Liu@asu.edu S CHOOL OF C OMPUTING, I NFORMATICS, AND D ECISION S YSTEMS E NGINEERING A RIZONA S TATE U NIVERSITY http://www.public.asu.edu/~huanliu/SMM11F/cse494-598.htm Fall 2011 I NTRODUCTION

2. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 2 Course Information Social Media Mining Line numbers: – CSE494 - 85511 – CSE598 - 84001 Prerequisites: – CSE 471 or equivalent – CSE 310 Data Structures and Algorithms Classroom and Hours: – CDN62, TTh 01:30 – 02:45pm Webpage: – http://www.public.asu.edu/~huanliu/SMM11F/cse494 -598.htm http://www.public.asu.edu/~huanliu/SMM11F/cse494 -598.htm

3. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 3 Introduction Instructor: Huan Liu, huan.liu@asu.edu,http://www.public.asu.edu /~huanliuhttp://www.public.asu.edu /~huanliu – Office Hours: TTh 2:45 - 3:45pm, BYE 566 Other times: by appointment only TA and Office Hours – Reza Zafarani: Mondays and Wednesdays, 3:00 - 4:00pm, BYENG 214 (tentative) – Ali Abbasi Please send email to the TAs for meetings

4. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 4 Course workload and evaluation A lot of work is expected from you! Are you ready? – 3 Homework assignments (20%) – Projects (20%) – 3 Exams or equivalent (60%) – Class participation, Quizzes (extra points for upgrading) – Late penalty: YES and exponentially increased (-1, -3, -7) Academic integrity (http://www.public.asu.edu/~huanliu/conduct.html)http://www.public.asu.edu/~huanliu/conduct.html

5. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 5 Slides and Schedule Announcements are made regularly in Blackboard. Emails will be sent out on a need basis. Weekly Schedule - Please visit Blackboard for Course Documents and Blog (i.e., Discussion Board). Experienced researchers or practitioners may be invited as guest instructors for specific topics.

6. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 6 Weekly Schedule (tentative) and Syllabus WeekTitle W1Introduction to Social Media Mining W2Introduction to Linear Algebra W2, W3Social Network Analysis W4Visualization of social networks (Pajek) W4, W5Measures and Metrics W6Small World and Random Graph Exam 1 W7Sentiment Analysis and Opinion mining W8Game Theory W9Influence And Homophily Oct 18Exam 2 W10Information Cascade W11, W12Social Recommendation W13Community Analysis (Detection, Formation, and Evolution) W14Behavioral Analysis W15Privacy, Security, and Trust in Social Network W17Final Exam

7. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 7 Text and Reference Books Networks, Crowds, and Markets: Reasoning about a Highly Connected World, (available in pdf), David Easley and Jon Kleinberg, Cambridge University Press 2010.available in pdf Networks: An Introduction, Mark Newman, Oxford University Press, 2010. Community Detection and Mining in Social Media, Morgan & Claypool, 2010. (available via ASU eLibrary)Morgan & Claypool Modeling and Data Mining in Blogosphere, Morgan & Claypool, 2009. (available via ASU eLibrary) Modeling and Data Mining in Blogosphere Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data, Bing Liu, Springer, 2011 (2 nd Edition) Web Data Mining Mining the Web - Discovering Knowledge from Hypertext Data, Soumen Chakrabarti, Morgan Kaufmann, 2003 Mining the Web Exploratory Social Network Analysis with Pajek, Wouter de Nooy, Andrej Mrvar, Vladimir Batagelj, Cambridge, 2005, Package and Manual Exploratory Social Network Analysis with PajekPackage and Manual Social Network Analysis - Methods and Applications, Stanley Wasserman and Katherine Faust, Cambridge, 1994 Social Network Analysis

8. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 8 What is Social Media Mining

9. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 9 Course Description Social Media Mining is concerned with the study of social behavior and mining social media data with both attribute- and link- data. The Web 2.0 technologies allow a passive reader (or an average Joe) to become an active producer (or a shining online star), which creates a phenomenal landscape change in terms of Web-based activities. With various social media (Digg, del.licio.us, Twitter, Facebook, LinkedIn, etc.), people can share content, opinions, insights, experiences, perspectives, and media themselves, as well as producing many new media via techniques such as mashing up. Social networks naturally emerge with the pervasive use of social media. Combining social computing and Web analytics, this course aims to introduce the state-of-the art developments in Web 2.0 techniques, social networks and analysis, network analysis and graph theory, information extraction, link analysis, and Web mining, to study new problems with social media, and to learn innovatively how to apply multidisciplinary approaches to problem solving. The ultimate goal is to sharpen problem solving skills of our senior and graduate students, and prepare them with this unique set of expertise for the increasing demands in IT industry and for advanced research.

10. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 10 An Emerging Field What is next big impact of computer science? – Bioinformatics manifests the significant and lasting impact of CS on biology – Pervasive use of Web and Internet fosters cultural and behavioral changes Friendship networks Citizen journalism and Blogosphere Folksonomy (digg, reddit) – Rich activities in a virtual world and in the boundary of virtual and physical worlds – An unprecedented laboratory for research

11. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 11 A New Trend Social Media Mining – Social networks Communities Interactions – Social media Observational data and lots of them Both content and link information – Mining Modeling Learning Prediction

12. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 12 New Challenges Everyone can develop a unique voice or publish – Challenge 1: How can I be heard? There exist numerous of online sources – Challenge 2: Which one should I use? People participate in activities on Web – Challenge 3: How can one improve their experience and create relevant business opportunities?

13. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 13 Challenge 1: How can I be heard Everybody can speak up, but people only listen to a few – Information is cheap, but attention is costly. – If you were one of the few, you could at least make living without any “traditional” job Sometimes, a real treasure might be buried forever – How to discover hidden treasures – If that is difficult, how to make us first visible – Study how a star develops, shines, and what others do (influence, diffusion, and cascading)

14. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 14 Challenge 2: Which source should I use Information overloading – Abundance is good, but too many choices can mean no choice (any déjà vu?) Information fusion or integration – Filtering irrelevant and/or redundant information – Discovering complementary information – Comparison shopping Trust and reliability – How to discern the real from the fake – Spam

15. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 15 Challenge 3: Improve user experience A business perspective – how to help generate profit for a business or make me rich – Better experience means (or leading to ) more traffic or more purchases Understanding – Who are they? – Who influence them? – How to engage their influencers? Data, data, and data – Data collection and analysis

16. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 16 Objectives of Our Course Understand social aspects of the Web – Social media, mining, social networks Learn what collectable data/statistics are – Data collection, cleaning, and summarization Study or ask interesting research issues from individual experience – start-ups, … Learn representative algorithms and tools Investigate something interesting and new Share our findings among us and in the world

17. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 17 Format Lectures – Guest lectures Class participation - important – Discussion, Class wiki, Reading, Presentation, Critiquing Project, Presentation, and Report Exams, homework assignments, …

18. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 18 Social Media

19. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 19 Definition Social Media is the use of electronic and Internet tools for the purpose of sharing and discussing information and experiences with other human beings in more efficient ways.

20. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 20

21. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 21 Social Media, Samples A wiki article Web reviews and ratings of a popular hamburger restaurant in your city – E.g., Yelp.com An online social network of your professional contacts – E.g., Facebook.com, LinkedIn.com an iPhone application that informs you about the police and camera locations in a road – “The Web is dead.” – Chris Anderson of wired.com

22. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 22 Types of Social Media Online Social Networking Publishing – Blogging – Wiki Micro blogging Social News Social Bookmarking Media Sharing – Video Sharing – Photo Sharing – Podcast Sharing Opinion, Review, and Ratings Websites Answers Entertainment

23. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 23 Online Social Networks are web-based services that allow individuals and communities to connect with real world friends and acquaintances online Interactions – Friendship interaction Friends, like, comments, … – Media Sharing – Send and receiving messages Samples – Facebook.com – MySpace.com – Bebo.com – Orkut.com Online Social Networking

24. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 24 Blogging A blog is a journal-like website for users, a.k.a. bloggers, to contribute textual and multimedia content, arranged in reverse chronological order maintained both individually or by a community (See a tutorial at KDD http://videolectures.net/kdd08_liu_briat/) http://videolectures.net/kdd08_liu_briat/ Usages: – sharing information and opinions with friends and strangers – A medium for disseminating subject-specific content. – Who is the influential http://videolectures.net/wsdm08_agarwal_iib/ http://videolectures.net/wsdm08_agarwal_iib/

25. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 25 Different types of blogs Personal blogs Political blogs – blogs are being written about politics Business blogs – Blogs related to businesses and professionals http://blogmaverick.com/ ‘Almost media’ blogs – Some blogs are media businesses in their own right, taking advertising and employing a blogger or a group of bloggers full-time http://www.businesspundit.com/ Mainstream media blogs – editors and reporters of newspaper’s blogs http://www.bbc.co.uk/blogs/thereporters/robertpeston/

26. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 26 Microblogging Microblogging is a relatively new phenomenon that can be considered as a counterpart to blogging, but with limited content Usage – communication medium – social interaction – citizen journalism Service Providers: – Twitter – Google buzz

27. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 27 Wiki A wiki is a collaborative editing environment that allows users to develop Web pages using a simplified markup language Wikipedia allows interested individuals to collaboratively develop articles on a variety of subjects. Using the wisdom of crowds effectively, it has become a comprehensive repository of information useful to a variety of individuals

28. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 28 Wiki-Samples Wikipedia.org Wikileak.com Wikimedia.com Wikitravel.com WikiHow.com WikiAnswers.com Wiki.asu.edu Dmml.asu.edu/wiki

29. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 29 Social News Social News refers to the sharing and selection of news stories and articles by a community of users. Users can share articles that they believe would interest the community Samples: – Digg.com – Slashdot – Fark – Reddit

30. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 30 Social Bookmarking Social Bookmarking sites allow users to bookmark web content for storage, organization and sharing. These bookmarks can be tagged with metadata to categorize and provide context to the shared content, allowing users to organize information making it easy to search and identify relevant information. Samples – Delicious.com – StumbleUpon.com

31. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 31 Media Sharing Media sharing is an umbrella term that refers to the sharing of a variety of media on the web. Users share such multimedia content of possible interest to others Samples: – Video Sharing: YouTube.com – Photo Sharing: Flicker.com, picasa.com – Document Sharing: Scribd.com, Slidesharec.om – Livecasting: Justin.tv, Ustream.com

32. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 32 Opinion, Review, and Ratings Websites Opinion, review, and ratings websites are websites whose primary function is to collect and publish user-submitted content in the form of subjective commentary on existing products, services, entertainment, businesses, places, etc. Some commercial sites may serve a secondary purpose as review sites by publishing product reviews submitted by customers. Examples – Cnet.com – Epinions.com – yelp.com – tripadvisor.com

33. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 33 Socially-Provided Answers In these sites, users who require certain guidance, advice or knowledge can ask questions. Other users from the community can answer these questions based on knowledge acquired from previous experiences, personal opinions or from relevant research. – Unlike review and opinion sites, which contain self- motivated contribution of opinions, answer sites contain knowledge shared in response to a specific query. Samples: – WikiAnswers, Yahoo Answers

34. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 34 Main Characteristics Participation – social media encourages contributions and feedback from everyone who is interested. It blurs the line between media and audience. Openness – most social media services are open to feedback and participation. They encourage voting, comments and the sharing of information. There are rarely any barriers to accessing and making use of content – password-protected content is frowned on. Conversation – whereas traditional media is about “broadcast” (content transmitted or distributed to an audience) social media is better seen as a two-way conversation. Community – social media allows communities to form quickly and communicate effectively. Communities share common interests, such as a love of photography, a political issue or a favorite TV show. Connectedness – Most kinds of social media thrive on their connectedness, making use of links to other sites, resources and people.

35. Huan Liu(Huan.Liu@asu.edu) Arizona State University Data Mining and Machine Learning Lab Arizona State University Data Mining and Machine Learning Lab Social Media Mining, CSE 494/598, Fall 2011 35 Why Social Media now? Sharing ideas Cooperating and collaborating Thinking, debating, writing Find new friends, rediscover the old ones People can find information, inspiration, like- minded people, communities and collaborators faster than ever before. New ideas, services, business models and technologies emerge and evolve at dizzying speed