Social Computing: Challenges in Research and Applications

Transcription

1 Social Computing: Challenges in Research and Applications Huan Liu, Shamanth Kumar, Fred Morstatters Conducting state-of-the-art research in data mining and machine learning, social computing, and artificial intelligence, dealing with realworld big data Data Mining and Machine Learning Lab 1

2 Computational Research with Social Media Community Analysis Provenance, Deception & Trust ACT Migration Influence & Diffusion Tweet & Blog Tracker Location based Social-network Analysis User Vulnerability Behavior Analysis Linked Feature Selection Data Mining and Machine Learning Lab 2

3 Seeking Provenance Using Network Information Social media lacks provenance information Users can t verify the source and the path To what extent the available network structure can be utilized? More knowledge about information would help in seeking disinformation centers, creating awareness, and curbing virulent spread Assam Exodus (July-Sept, 2012) Key Findings: The higher degree nodes closer to the recipients are more likely to be transmitters. There are edges in the network which are more likely to propagate information. Data Mining and Machine Learning Lab Huan.Liu@asu.edu 3

4 Trust in Social Networks Trust is an essential part of online relations but hard to establish and measure htrust - a large homophily coefficient indicates that users are more likely to establish trust relations mtrust - trust is not a single facet issue and people may not equally trust different facets of other users. Multi-Faceted Trust Key Findings: Homophily is an effective factor Differentiating facets is important Data Mining and Machine Learning Lab Huan.Liu@asu.edu 4

5 Community Detection Finding groups for users in social networks These groups indicate special interests, relations, friendships, etc. We introduce the concept of social dimensions, and system SocialDim to automatically find users different and overlapping communities Key Findings: A user with hundreds of friends can have many social circles These circles can change and evolve unexpectedly Data Mining and Machine Learning Lab Huan.Liu@asu.edu 5

6 Understanding User Migration Patterns in Social Media How and Where are people migrating to? Migration is inevitable Users are primary source of revenue New sites need to attract users, old sites need to retain users Types of Migration Site migration Attention migration a user becomes inactive on a site Longitudinal study across 3 months on 7 social media sites. Established migration patterns between social media sites Validated patterns via hypothesis testing. Demonstrated that these patterns can be capitalized by tracking High Net-Worth Individuals (HNWI) Data Mining and Machine Learning Lab Huan.Liu@asu.edu 6

7 Location-Based Social Networks Location data is a rich source of information on social media Social media websites are increasingly adding geolocation components We look for the ways we can use the user s historical location information and his social ties to predict his future locations and connections Applications Recommend new Points of Interest to a user according to his personal preference Facilitate the user to explore new areas of the city Promote business of mobile marketing Data Mining and Machine Learning Lab Huan.Liu@asu.edu 7

8 Managing User s Vulnerability How to manage user s vulnerability on a social networking site? Users share a large amount of personal information Often, a user is unaware that he could pose a threat to his friends Malicious individuals or groups may take advantage of openness and naivety of users Challenges: Measuring user vulnerability Defining vulnerable users and finding them Making users less vulnerable without suggesting fundamental changes to its architecture What if the vulnerable friends are of some importance such as girlfriend, boyfriend or influential friends? Data Mining and Machine Learning Lab Huan.Liu@asu.edu 8

9 Behavior Analysis, Mining across Social Media Analyzing behavior of individuals across social media Individuals are active on multiple social networks, sites, blogs, etc. Complementary information helps find new patterns and improve current applications Challenges How can we connect individuals? What is the minimum information required to connect an individual? Approach Utilizing Content information Systematic behavioral Modeling Key Findings Behaviors result in information redundancies Link information is rarely useful Benefits Recommendation across sites Advertising across networks Behavioral study: user migration Data Mining and Machine Learning Lab 9

10 Software Systems at the DMML TweetTracker (top): State-of-the-art analysis tool for tracking Twitter data in real time. Provides basic analysis tools in addition to real-time monitoring. Deployed at organizations like Humanity Road and ONR. TweetXplorer (bottom): Off-line analysis tool for data collected from TweetTracker. Provides more detailed analysis of data, including network information Encourages deep understanding through intuitive visualizations. ACT (not pictured): ASU Coordination Tracker Uses social media to track and enhance disaster response efforts at the site. Data Mining and Machine Learning Lab 10

11 Additional Information Textbook and Monographs Social Media Mining, Cambridge University Press, February, 2014 Twitter Data Analytics, Springer Briefs, 2013, Free Download Provenance Data in Social Media, Morgan & Claypool, May, 2013 Community Detection and Mining, Morgan & Claypool, August, 2010 Websites Social Computing Data Repository Feature Selection Data and Algorithms Respository Demos TweetTracker, and TweetXplorer ACT for Humanitarian Assistance and Disaster Relief Data Mining and Machine Learning Lab 11

12 Contact Information + Huan Liu, Professor of Computer Science & Eng. Fellow of IEEE, ACM Distinguished Scientist huanliu@asu.edu, dmml.asu.edu Recent Collaborators: CMU, UIUC, Boston U, GDIT, PDC Recent Users: HumanityRoad, USMA, Welcome to our lab poster display Sponsors: NSF, ONR, ARO, Yahoo! Labs (FREP) Data Mining and Machine Learning Lab Huan.Liu@asu.edu 12