This document discusses data mining techniques for social media. It begins by reviewing the growth of popular social media sites like Facebook, YouTube, and Twitter. It then discusses how social media generates huge amounts of user data through interactions and content sharing. The document outlines opportunities to use data mining on social networks to gain insights into human behavior, marketing analytics, and more. It reviews common problems studied, like community detection, node classification, and modeling information flow. The conclusion emphasizes that social media provides a massive, open dataset for developing recommender systems and targeting marketing through predictive analysis of user interests and trends.

1. Data Mining for Social Media VNG Corporation – R&D Team 4/23/2011 1 VNG Corporation - R&D Team

2. Content Social Media Growth Social Media Data Data Mining for Social Media Conclusion & Discussion 4/23/2011 2 VNG Corporation - R&D Team

3. 1. Social Media Growth Top sites Globally Google Facebook Youtube Yahoo Live Baidu Wikipedia Blogger MSN Tencent Twitter Top sites in Vietnam Google Vnexpress Zing.vn Yahoo Youtube Facebook Dantri.com.vn 24h.com.vn Mediafire Vatgia.com 4/23/2011 VNG Corporation - R&D Team 3

4. 1. Social Media Growth Some Statistics Facebook - largest social network site 600,000,000 users, half log in everyday 35,000,000,000 online friendships 900,000,000 objects people interact with 30,000,000,000 shared content items / month YouTube – largest video sharing site 2,000,000,000 views per day 1,000,000 video hours uploaded per month Twitter – largest microblogging site 200,000,000 users per month 65,000,000 tweets per day (750 per second) 8,000,000 followers of most popular user ZingMe – largest Vietnamese social network 35,000,000 users, 10,000,000 monthly active 260,000,000 online friendships Plenty of services: music, video, karaoke, games, news, chat, photo, blog … 4/23/2011 4 VNG Corporation - R&D Team

5. 2. Social Media Data Social media data is everywhere Social Overload: Information Overloadblogs, microblogs, forums, wikis, news, bookmarked web pages, photos, videos, etc. Interaction Overloadfriends, followers, followees, commenters, co-members, voters, “likers”, taggers, etc.  How to extract useful information from this chaos? 4/23/2011 5 VNG Corporation - R&D Team

6. 2. Social Media Data Opportunities Social Media captures the pulse of humanity! Can directly study opinions and behaviors of millions of users to gain insights into: Human behaviors Marketing analytics, product sentiment Application & Problems: WWW: search, information retrieval (group web sites or documents) Targeted marketing: identify groups of customers or products to make recommendations (targeted advertising, viral marketing) Personalization (interfaces, services) Epidemiology, Fraud detection, Security (counterterrorism) … 4/23/2011 6 VNG Corporation - R&D Team

7. Quick Recap Social Media Growth Social Media Data Data Mining for Social Media Social Network as a Graph Interesting Problems Community Detection Node Classification Link Classification & Tie Strength Information Flow Conclusion & Discussion 4/23/2011 7 VNG Corporation - R&D Team

8. 3. Data Mining for Social Media Data Mining in Social Network: Graph Mining: Friendship graph, contact lists. Interactions between users. Text Mining: Blogs, status updates, tweets… Texts, messages sent between users. Some interesting problems for data miners: Model Information Flow (e.g. viral marketing) Model evolution (e.g. link prediction) Extract information for learning (e.g. node classification, community detection). 4/23/2011 8 VNG Corporation - R&D Team

9. 3.1 Social Network as a Graph A social network is a graph, but: nodes can have attributes edges (links) may be weighed and/or directed, or not so, the similarity (tie strength, affinity) between two nodes is = f(attributes; links) the network’s graph is not a simple random graph (special structural properties) Large-scale graphs Mining of large-scale graph 4/23/2011 9 VNG Corporation - R&D Team

10. 3.1 Social Graph Characteristics Sparse networks: number of links proportional to the number of nodes. Small world effect: The shortest path between two random nodes is on average small. This property is related to the distribution of the degrees of the nodes: scale-free network (Barabasi, 2000) 4/23/2011 10 VNG Corporation - R&D Team

11. 3.2 Interesting ProblemsCommunity Detection Community Detection in Social Network: Partition the graph into clusters Find the (small) community around a given node Why Community Detection? Capture network’s dynamic Allow local analysis of interactions. Reveal the properties without releasing individual privacy information. Methods Clustering based on shortest-path betweenness Clustering based on network modularity 4/23/2011 11 VNG Corporation - R&D Team

12. 3.2 Interesting Problems Node Classification Node Classification for Social Network: Labeling nodes in the network, indicating demographic values, interest, beliefs or other characteristics. Applications: Used as input for Recommendation Suggest new connections, objects. Personalized ads tailored to users’ interest. Find community based on interests, affiliation. Study how ideas are spread over time. Methods Methods based on traditional classifiers using graph information. Graph-based Methods 4/23/2011 12 VNG Corporation - R&D Team

13. 3.2 Interesting Problems Link Prediction & Tie Strength Link prediction: Given a snapshot of a social network, infer which new interaction among its members are likely to occur in the near future. Tie Strength: combination of amount of TIME, emotional INTENSITY, INTIMACY (mutual confiding), and reciprocal SERVICES. Applications: Predict future friends Find influential users in the networks. Find possible links between users and objects (e.g. online item to be sold). Methods: Supervised Learning: Decision Trees, Logistic Regression, Support Vector Machine … Graph-based methods. 4/23/2011 13 VNG Corporation - R&D Team

14. 3.2 Interesting Problems Information Flow Information flow through Social Media Analyzing underlying mechanisms for the real-time spread of information through on-line networks Motivating questions: How do messages spread through social networks? How to predict the spread of information? How to identify networks over which the messages spread? Application: Indicate trends and attentions Predictive modeling of the spread of new ideas and behaviors Search: Real-time search, Social search 4/23/2011 14 VNG Corporation - R&D Team

15. 4. Conclusion and Discussion Social Media – Rich,Big & Open Data: Billions users, billions contents Textual, Multimedia (image, videos, etc.) Billions of connections Behaviors, preferences, trends... Challenges: Large-scale Problems Noise in data Recommender System for users and enterprises: Maintain users’ interest and attract new users to the network Targeted Marketing: Show appropriate ads and items personalized for users to Predict users’ interests and trends: Make effective plans. … 4/23/2011 15 VNG Corporation - R&D Team

16. 4/23/2011 VNG Corporation - R&D Team 16 Thank you for your attention!