1. Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Introduction TJTSD66: Advanced Topics in Social Media Dr. WANG, Shuaiqiang @ CS & IS, JYU Email: shuaiqiang.wang@jyu.fishuaiqiang.wang@jyu.fi Homepage: http://users.jyu.fi/~swang/http://users.jyu.fi/~swang/ (Social Media Mining)

2. 2 Social Media Mining Measures and Metrics 2 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ About Me - Experience Education – Aug. 2009 – Oct. 2009, exchange Ph.D. student in CS, HKBU, Hong Kong, China – Sep. 2004 – Dec. 2009, Ph.D. in CS, SDU, China – Sep. 2000 – Jul. 2004, B.Eng. in CS, SDU, China Work Experience – Sep. 2014 – Present, Postdoc Researcher of CS, JYU, Finland – Mar. 2011 – Jul. 2014, Assoc. Prof. of CS, SDUFE, China – Jan. 2010 – Feb. 2011, Postdoc fellow of CS, TSU, TX, USA

3. 3 Social Media Mining Measures and Metrics 3 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ About Me - Research Research Interests – Recommender Systems; Information Retrieval; Data Mining; Machine Learning Publications – 20+ papers, including 6 JUFO-3 papers – See http://users.jyu.fi/~swang/publication.htmlhttp://users.jyu.fi/~swang/publication.html Students – 2 doctoral students – 2 master students Welcome self-motivated students with good programming skills and mathematical background!

4. 4 Social Media Mining Measures and Metrics 4 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ About the Course The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. We attempt to deeply understand and process this data for interdisciplinary research, novel algorithms, and tool development. You will learn the main techniques and skills for social media mining – Fundamental concepts, emerging issues, effective algorithms, and possible applications for social data mining.

5. 5 Social Media Mining Measures and Metrics 5 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Contents Part I Essentials – Graph Essentials – Network Measures – Network models – Data mining essentials Part II Communities and Interactions – Community Analysis – Information Diffusion in Social Media Part III Applications – Influence and Homophily – Recommendation in Social Media

6. 6 Social Media Mining Measures and Metrics 6 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Textbook http://dmml.asu.edu/smm/ Most of my slides come from here!

7. 7 Social Media Mining Measures and Metrics 7 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Reference for Social Networks Book: http://www.cs.cornell.edu/home/kleinber/networks-book/ Class: http://www.ymsir.com/networks/

8. 8 Social Media Mining Measures and Metrics 8 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Reference for Data Mining http://hanj.cs.illinois.edu/bk3/

9. 9 Social Media Mining Measures and Metrics 9 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Assessment Assessment criteria – Individual assignment: 20% – Group work deliverable and presentation: 30% – Final exam: 50% Final exam: Written final exam based on course material will take place after the course. Exam dates are provided in Korppi.

10. 10 Social Media Mining Measures and Metrics 10 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Individual assignment You can choose 4 chapters (subchapters). There are two options: – Technical oriented option. Each student is required to implement at least 4 social/data mining algorithms. Any pair of algorithms can NOT belong to a same chapter. The datasets can be either downloaded from the internet or artificially made on your own. Any programming language is acceptable. – Report oriented option. Each student is required to write 4 reports on different potential applications of the social/data mining algorithms. Each report should include: (1) description and motivation of the application scenario, (2) problem formulation (formulated as a social/data mining problem), (3) possible solutions and algorithms, (4) expected results and conclusions, and (5) key literatures.

11. 11 Social Media Mining Measures and Metrics 11 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Group work The group work continuous throughout the duration of the course. There are also two options: – Technical oriented option. Students are expected to apply the theoretical knowledge to solve practical problems. Each group consists of 4-5 students. The group work includes conceiving a social media application scenario, designing and implementing a web-based computer software or mobile app, and presentation. The software/app should have a friendly user interface, and apply at least one social mining algorithm. Any programming language is acceptable. Somehow improvement to the existing algorithm is obviously a big plus. – Paper oriented option. Students are expected to write a research paper. Each group consists of 3-5 students. The paper should use some social/data mining algorithms to analyze the data and achieve the conclusions. It can be an extension/a combination of your previous individual reports, but at least 50% new materials should be introduced.

12. 12 Social Media Mining Measures and Metrics 12 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Facebook How does Facebook use your data? Where do you think Facebook can use your data? How does Facebook use your data? Where do you think Facebook can use your data?

13. 13 Social Media Mining Measures and Metrics 13 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Amazon

14. 14 Social Media Mining Measures and Metrics 14 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Yelp

15. 15 Social Media Mining Measures and Metrics 15 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Twitter

16. 16 Social Media Mining Measures and Metrics 16 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Objectives of Our Course Understand social aspects of the Web – Social Theories + Social media + Mining – Learn how to collect, clean, and represent social media data – How to measure important properties of social media and simulate social media models – Find and analyze communities in social media – Understanding friendships in social media, perform recommendations, and analyze behavior Study or ask interesting research issues – e.g., start-up ideas Learn representative algorithms and tools

17. 17 Social Media Mining Measures and Metrics 17 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Social Media

18. 18 Social Media Mining Measures and Metrics 18 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ 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.

19. 19 Social Media Mining Measures and Metrics 19 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/

20. 20 Social Media Mining Measures and Metrics 20 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Social Media Mining is the process of representing, analyzing, and extracting meaningful patterns from social media data

21. 21 Social Media Mining Measures and Metrics 21 Social Media Mining Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Most of contents are provided by the website http://dmml.asu.edu/smm/http://dmml.asu.edu/smm/ Social Media Mining Challenges 1.Big Data Paradox 1.Social media data is big, yet not evenly distributed. 2.Often little data is available for an individual 2.Obtaining Sufficient Samples 1.Are our samples reliable representatives of the full data? 3.Noise Removal Fallacy 1.Too much removal makes data more sparse 2.Noise definition is relative and complicated and is task- dependent 4.Evaluation Dilemma 1.When there is no ground truth, how can you evaluate?