In this presentation, Ken will describe a portion of an early-phase project that uses social media data (tweets, Facebook posts, etc.) from service personnel to predict suicide rates. There's a lot of motivation to provide better data for military psychologies, since more military wind up taking their own lives than are killed in the line of duty. By analyzing social media data that is voluntarily provided by personnel, plus a predictive analytics system, we can provide assessments that help mental health workers focus their time and energy on the most at-risk individuals. This project uses Cassandra as the scalable storage system for this social media data, which is then analyzed in a distributed environment using Hadoop. The project also uses the Solr search support from DataStax Enterprise to provide ways for users to dig into the underlying data, which is critical when understanding the assigned risk levels.

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Ken
Krugler
|
President,
Scale
Unlimited
Suicide Prevention Using Social Media and Cassandra

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What we will discuss today...
*Using Cassandra to store social media content
*Combining Hadoop workflows with Cassandra
*Leveraging Solr search support in DataStax Enterprise
*Doing good with big data
This material is based upon work supported by the Defense Advance Research Project Agency (DARPA),
and Space Warfare Systems Center Pacific under Contract N66001-11-4006. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the authors(s) and do not
necessarily reflect the views of the Defense Advance Research Program Agency (DARPA) and Space and
Naval Warfare Systems Center Pacific.
Fine Print!

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Obligatory Background
*Ken Krugler, Scale Unlimited - Nevada City, CA
*Consulting on big data workflows, machine learning & search
*Training for Hadoop, Cascading, Solr & Cassandra

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Durkheim Project Overview
Including things we didn't work on...

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What's the problem?
*More soldiers die from suicide than combat
*Suicide rate has gone up 80% since 2002
*Civilian suicide rates are also climbing
*More suicides than homicides
*Intervention after an "event" is often too late

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What is The Durkheim Project?
*DARPA-funded initiative
to help military
physicians
*Uses predictive analytics
to estimate suicide risk
from what people write
online
*Each user is assigned a
suicidality risk rating of
red, yellow or green.
Émile Durkheim

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Current Status of Durkheim
*Collaborative effort involving Patterns and Predictions,
Dartmouth Medical School & Facebook
*Details at http://www.durkheimproject.org/
*Finished phase I, now being rolled out to wider audience

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Predictive Analytics
*Guessing at state of mind from text
-"There are very few people in this world that know the REAL
me."
-"I lay down to go to sleep, but all I can do is cry"
*Uses labeled training data from clinical notes
*Phase I results promising, for small sample set
-"ensemble" of predictors is a powerful ML technique

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Clinician Dashboard
*Multiple views on patient
*Prediction & confidence
*Backing data (key phrases,
etc)

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Data Collection
Where _do_ you put a billion text snippets?

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Saving Social Media Activity
*System to continuous save new activity
-Scalable data store
*Also needs a scalable, reliable way to access data
-Processed in bulk (workflows)
-Accessed at individual level
-Searched at activity level

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Data Collection
*Pink is what we
wrote
*Green is in
Cassandra
*Key data path in red
Exciting Social
Media Activity
Gigya
Daemon
Durkheim
Social API
Users
Table
Durkheim
App
Gigya
Service
Activity
Table

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Designing the Column Families
*What queries do we need to handle?
-Always by user id (what we assign)
*We want all the data for a user
-Both for Users table, and Activities table
-Sometimes we want a date range of activities
*So one row per user
-And ordered by date in the Activities table

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Users Table (Column Family)
*One row per user - row key is a UUID we assign
*Standard "static" columns
-First name, last name, opt_in status, etc.
*Easy to add more xxx_id columns for new services
row key first_name last_name facebook_id twitter_id opt_in

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Activities Table (Column Family)
*One row per user - row key is a UUID we assign
*One composite column per social media event
-Timestamp (long value)
-Source (FB, TW, GP, etc)
-Type of column (data, activity id, user id, type of activity)
row key ts_src_data ts_src_id ts_src_providerUid ts_src_type

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Two Views of Composite Columns
*As a row/column view
*As a key-value map 213_FB_data
213_FB_id
213_FB_providerUid
213_FB_type
"I feel tired"
"FB post #32"
"FB user #66"
"Status update"
"uuid1"
"uuid1" 213_FB_data 213_FB_id 213_FB_providerUid 213_FB_type
"I feel tired" "FB post #32" "FB user #66" "Status update"

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Implementation Details
*API access protected via signature
*Gigya Daemon on both t1.micro servers
-But only active on one of them
*Astyanax client talks to Cassandra
*Cluster uses 3 m1.large servers
Durkheim
Social API
Durkheim
App
AWS Load
Balancer
EC2 m1.large
servers
Durkheim
Social API
EC2 t1.micro
servers

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Predictive Analytics at Scale
Running workflows against Cassandra data

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How to process all this social media goodness?
*Models are defined elsewhere
*These are "black boxes" to us
213_FB_data 213_FB_id 213_FB_providerUid 213_FB_type
"I feel tired" "FB post #32" "FB user #66" "Status update"
307_TW_data 307_TW_id 307_TW_providerUid 307_TW_type
"Where am I?" "Tweet #17" "TW user #109" "Tweet"
Feature
Extraction
Model
model rating probability keywords

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Why do we need Hadoop?
*Running one model on one user is easy
-And n models on one user is still OK
*But when a model changes...
-all users with the model need processing

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Batch processing is OK
*No strict minimum latency requirements
*So we use Hadoop, for scalability and reliability

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Hadoop Workflow Details
*Implemented using Cascading
*Read Activities Table using Cassandra Tap
*Read models from MySQL via JDBC

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Hadoop Bulk Classification Workflow
Convert to Cassandra
Write Classification Result Table
Run Classifier models
CoGroup by user profile ID
Convert from Cassandra
Read User Profiles Table
Convert from Cassandra
Read Social Media Activity Table

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Workflow Issues
*Currently manual operation
-Ultimately needs a daemon to trigger (time, users, models)
*Runs in separate cluster
-Lots of network activity to pull data from Cassandra cluster
-With DSE we could run on same cluster
*Fun with AWS security groups

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Solr Search
Poking at the data

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Solr Search
*Model results include key terms for classification result
-"feel angry" (0.732)
*Now you want to check actual usage of these terms

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Poking at the Data
*Hadoop turns petabytes into
pie-charts
*How do you verify results?
*Search works really well here

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Solr Search
*Want "narrow" table for search
-Solr dynamic fields are usually not a great idea
-Limit to 1024 dynamic fields per document
*So we'll replicate some of our Activity CF data into a new CF
*Don't be afraid of making copies of data

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The "Search" Column Family
*Row key is derived from Activity CF UUID + target column name
*One column ("data") has content from that row + column in
Activity CF
row key "data"
"uuid1_213_FB "I feel tired"
"uuid1" 213_FB_data 213_FB_id
"I feel tired" "FB post #32"
Activity Column Family
Search Column Family

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Solr Schema
*Very simple (which is how we like it)
*Direct one-to-one mapping with Cassandra columns
*Hits have key field, which contains UUID/Timestamp/Service
<fields>
<field name="key" type="string" indexed="true" stored="true" />
<field name="data" type="text" indexed="true" stored="true" />
</fields>

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Combined Cluster
*One Cassandra Cluster can allocate nodes for Hadoop & Search

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Security
Locking things down

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The Most Important Detail
*We don't have any personal medical data!!!
*We don't have any personal medical data!!!
*We don't have any personal medical data!!!

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Three Aspects of Security
*Server-level
-ssh via restricted private key
*API-level
-validate requests using signature
-secure SHA1 hash
*Services-level
-Restrict open ports using security groups

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Summary
Bringing it all home

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*You can effectively use Cassandra as:
A repository for social media data
The data source for workflows
A search index, via Solr integration
Key Points...

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*It is possible to do more with big data than optimize ad yields
And the Meta-Point

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THANK YOU