1. Big Data + Deep Learn = A Universal Solution?
Huan Liu
This photo shows visitors, and former and current students in the lab. Some graduated.
This talk is a discussion of various topics: AI, Social Media, Big Data, Social Media Intelligence
I am very fortunate to have many diligent and intelligent students to work with me since I moved to ASU. I am truly grateful to them. My lab and future students benefit immensely from their original work, fame and success.

2. Big Data and Deep Learning
Big data is ubiquitous as computing technology advances
The success of KDD shows that ‘raw oil’ can be turned into valuable ‘products’
Numerous impressive results of deep learning revive neural networks to a new height for machine learning
Are we close to having a universal solution?
Data is the new oil
Gordon Moore’s law

3. Big Data + Deep Learning = A Universal Solution?
Yes, if we had sufficiently big data
Do we often have enough data?
Social media data is obviously big
We use social media data in this discussion

4. Social Media Data Twitter Facebook Instagram 300 million users
500 million tweets / day
1% (5 million) released for research
Facebook
2 billion users
422 million updates / day
196 million photos / day
Instagram
700 million users
80 million photos / day
Facebook Degree Distribution
Thanks to Dr. Fred Morstatter
Social media data is mainly user-generated with geo-spatial, pictorial, temporal, and social information.
SM contains a lot of newly available information
Instagram Users over Time

5. A Key to Success in Search of a Universal Solution
Make “Big” Data Bigger

6. Making “Big” Data Bigger
What is big data?
A conventional answer is 4Vs
A practitioner’s answer is more nuanced
‘Big’ data can be actually little or thin
For machine learning or data mining to work, the more data, the better
Make little data bigger
Make thin data thicker
4Vs: Volume, Velocity, Variety, Veracity; and Value, Vulnerability
A correct answer or philosopher’s answer is it depends, but depending on what?
Use curse of dimensionality to increase data amount

7. Curse of Dimensionality: Required Samples
Data sparsity becomes exponentially worse as feature dimensionality increases
Conventional distance metric becomes ineffective as far and near neighbors have similar distances
3 samples per unit region
1 sample per region
1/3 sample per region

8. Relevant, Redundant and Irrelevant Features
Feature selection retains relevant features for learning and removes redundant or irrelevant ones
For a binary classification task below, f1 is relevant, f2 is redundant given f1, and f3 is irrelevant
Colors show two classes

9. Feature Selection
Feature selection selects an ‘optimal’ subset of relevant features from the original high-dimensional data given a certain criterion
feature
selection

10. Feature Selection and scikit-feature
Feature selection can make data `bigger’
Assuming all binary attribute values in our toy example
Before FS, 5/210 = 5/1024, after FS, 5/23 = 5/8
Does FS always work?
Yes, for most high-d data
Where can we find it?
scikit-feature, an open- source repository in Python
5/1024 < 0.5%, 5/8 > 50%

11. Making Thin Data Thicker
Most people like many of us are in the long tail
Our data is often thin or sparse
With little data, machine learning is powerless
Social media data offers new opportunities
Multiple facets: posts, profile, linked information
Multiple platforms that offer different functions
Two case studies
Feature selection using social network information
Connecting users across more than one social media site
For the first case, we use addition facet of data (or data variety) to help enrich data; for the second case, we use informtation from multiple sites to make data thicker

12. Use Link Information for Data Thickening
Where can we find additional information for feature selection
Social media data contains various types of data
Link information is additional
Other sources such as sentiment, like, etc.
Are there theories to guide us in using link info?
Social influence
Homophily
Extracting distinctive relations from linked data for feature selection
Having additional information does not mean that we have to use it

13. Representation for Social Media Data
And the following relations between users. Until now, we discuss the different between attribute-value data and social media data, we can see traditional feature selection is unequipped to social media data, that motivates our current work linkedFS. The problem of feature selection for linked social media data is relatively novel. Thus before going through the details of linkedFS, we would like to formally define the problem first.
Social Context

14. Relation Extraction CoPost CoFollowing CoFollowed Following
CoPost – posts belong to a user, CoFollowing - users follow the same person, CoFollowed - followed by the same people, Following – a user follows another user
CoPost
CoFollowing
CoFollowed
Following

15. Evaluation Results on Digg Data
And the following relations between users. Until now, we discuss the different between attribute-value data and social media data, we can see traditional feature selection is unequipped to social media data, that motivates our current work linkedFS. The problem of feature selection for linked social media data is relatively novel. Thus before going through the details of linkedFS, we would like to formally define the problem first.

16. Summary
LinkedFS is evaluated under varied circumstances to understand how it works
Link information can help feature selection for social media data
Unlabeled data is more often in social media, unsupervised learning is more sensible, but also more challenging
An unsupervised method is showcased in our KDD12 paper following social correlation theories
Jiliang Tang and Huan Liu. `` Unsupervised Feature Selection for Linked Social Media Data'', the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2012.
Jiliang Tang, Huan Liu. ``Feature Selection with Linked Data in Social Media'', SIAM International Conference on Data Mining, 2012. 

17. Gather More Data with Little Data
Collectively, social media data is indeed big
For an individual, however, the data is small
How much activity data do we generate daily?
How many posts did we post this week?
How many friends do we have?
When “big” social media data isn’t big,
Searching for more data with little data
We use different social media services for varied purposes
LinkedIn, Facebook, Twitter, Instagram, YouTube, …
Activity data: comment, like, share, retweet; 4Vs

18. An Example Can we connect individuals across sites? Reza Zafarani
- Little data about an individual
+ Many social media sites
- Partial Information
+ Complementary Information
> Better User Profiles
LinkedIn
Twitter
Age
Location
Education
N/A
Phoenix Area
ASU (2014)
N/A
Tempe, AZ
ASU
- How hard can it be to identify an individual across sites?
- Privacy Experts Claim Advertisers Know a lot about People
- Can they stop showing you the same repetitive ads across sites?
Connectivity is not available
Consistency in Information Availability
Can we connect individuals
across sites?
Reza Zafarani and Huan Liu. ``Connecting Users across Social Media Sites: A Behavioral-Modeling Approach", the Nineteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2013), August , Chicago, Illinois.

19. Searching for More Data with Little Data
Each social media site can have varied amount of user information
Which information definitely exists for all sites?
Usernames
But, a user’s usernames on different sites can be different
Our work is to connect the information of the same user provided across sites
Why is it not an entity resolution problem? We define our problem as an identification problem and we solve it via machine learning

20. Our Behavior Generates Information Redundancy
Information shared across sites provides a behavioral fingerprint
How to capture and use differentiable attributes
MOdeling Behavior for Identifying Users across Sites
Behavioral Modeling
Machine Learning
MOBIUS

21. Time & Memory Limitation Personal Attributes & Traits
Behaviors
Human Limitation
Time & Memory Limitation
Knowledge Limitation
Exogenous Factors
Typing Patterns
Language Patterns
Endogenous Factors
Personal Attributes & Traits
Habits

22. Behavioral Modeling Approach with Learning
Generates
Captured Via
Behavior 1
Behavior 2
Behavior n
Information Redundancy
Feature Set 1
Feature Set 2
Feature Set n
Learning Framework
Identification
Function
Data

23. Summary – Making Data Bigger
Gathering more data is often necessary for effective data mining
Reducing dimensionality can make data bigger
Social media data provides unique opportunities to do so by using different sites and abundant user-generated content
Traditionally available data can also be tapped to make thin data “thicker”
Jundon Li, et al. ``Feature Selection: A Data Perspective",
Reza Zafarani and Huan Liu. ``Connecting Users across Social Media Sites: A Behavioral-Modeling Approach", SIGKDD, 2013.

24. Big Data Is Often Not Sufficiently Big
Can we always make our data bigger?
Diminishing returns
What do we sacrifice when making data bigger?
Narrowing domains
Deep learning is confined by available data
Unexpected effectiveness of big data
To accomplishing general AI, we need more and bigger data!

25. Repositories and Recent Books
scikit-feature – an open source feature selection repository in Python
Social Computing Repository
Books: SMM and TDA (in Python) free download

27. Discovering Social Media Intelligence
Graph Theories
Network Measures and Models
Data Mining, NLP, and Visual Analytics
Community Detection and Analysis
Information Diffusion
Influence and Homophily
Recommender Systems
Behavior Analytics
Sentiment analysis
We use social media data to acquire intelligence about user behavior, user needs, sentiment, opinions, and trends.

28. THANK YOU ALL & BigMine17 Organizers
for this opportunity to share our research
Acknowledgments
Grants from NSF, ONR, ARO, among others
DMML members and project leaders
Collaborators: CMU (Minerva), CRA (IARPA-CAUSE)
More information by searching for “Huan Liu” or at
CRA Charles River Aanlytics

29. More Challenges in Acquiring SM Intelligence
Social media data is obviously big, but why are we often still short of data?
How can we make data `bigger’?
Data is power, so it can produce any result
Can we algorithmically evaluate the results from big data?
We don’t know what we don’t know
How can we know if our result of social media analysis is of any value?
Make thin data thicker? Can machine learning help? How?

30. Evaluation without Ground Truth
is in both English and Chinese
The CACM article can be found at dl.acm.org

31. Further Readings
Jundong Li and Huan Liu. ``Challenges of Feature Selection for Big Data Analytics", Special Issue on Big Data, IEEE Intelligent Systems. 32 (2),
Fred Morstatter and Huan Liu. ``A Novel Measure for Coherence in Statistical Topic Models", Association of Computational Linguistics (ACL), August Berlin, Germany
Reza Zafarani and Huan Liu. ``Evaluation without Ground Truth in Social Media Research", Communications of ACM, Volume 58 Issue 6, June 2015 Pages
Lei Tang and Huan Liu. "Community Detection and Mining in Social Media", Morgan & Claypool Publishers, September 2010.