agile, business model, corporate innovation, customer development, h4d, hacking for defense, lean, lean launchpad, lean startup, stanford, steve blank, diux, bmnt

1. Team Narrative Mind
Stanford University
Sponsor: US Army Cyber Command (ARCYBER)
The Narrative Mind team contains experts in software engineering, social media design, and
web-based information operations (IO). We seek to develop tools that will optimize discovery
and investigation of communication trends on social media.
Weekly Total: 10 interviews
Users: 5
Experts: 15
Buyers: 4
Cumulative Total: 24 interviews

2. Customer Discovery
Hypotheses ❏ “C2”, “Recruiting” are sufficiently granular content categories.
❏ Advanced warning virality prediction is valuable for countering threats early.
❏ Content categorization is central to analyst workflow.
Experiments ❏ Interviewed analysts to determine workflow.
❏ Explored open source tools to supplement workflow.
❏ Presented options for new scalable categorization tools for uncovering topic
meaning and categorization.
Results ❏ Analysts categorize data by suspect accounts and relatively broad tags.
❏ Twitter provides potential targets that can be investigated further on other platforms.
❏ Analysts currently use Twitter homepage to access content.
❏ Monitoring viral potential of social media
❏ Government agencies may hesitate to use crowdsourced tools.
Actions Moving forward with tweet categorization MVP that prioritizes:
1. Workflow optimization for analysts with integrative UI.
2. Expedite content categorization with crowdsourcing.
3. Develop better predictive analytics for monitoring viral potential.
4. Research suspect accounts from Twitter quickly on other social platforms.

3. Mission Model Canvas
- Categorize social media
posts and users by
content.
- Understand viral
potential of social media
posts in real-time.
- Gnip/Twitter
- CrowdFlower,
Samasource, or
Mechanical Turk
- Pre-existing social
media service and micro-
labor aggregators
ARCYBER wants to
derive “meaning” from
extremist social media
presence.
Primary: Intelligence
analysts receive a better
platform.
- General public benefits
from more effective
social media monitoring.
- Optimize workflow for
social media analysts
- Expedite categorization
of social media content.
- Use MechanicalTurk to
crowdsource
categorization of content
and users.
- Algorithmic virality
predictor to create alerts
for important, time-
sensitive threats.
- Use design of now-
defunct Palantir Torch as
inspiration for how to
present content in a
streamlined manner.
- Force multiplier for intelligence analysts: receive cleaner, pre-
categorized data, target the most urgent priorities in real time.
- Increase throughput to quantify and flag viral content.
- Improve the categorization of unstructured social media data
points using crowdsourced micro-task labor.
- Architecture that can
support massive
concurrent data
aggregation and
analysis. E.g.
Storm/Hadoop.
- Customized UI
- Testing with analysts
- MechanicalTurk or crowdsourcing labor (microtasks)
- UI Development/Testing with ARCYBER analysts.
- Software Development
- Access to Twitter
firehose or API
- Local language
speaking crowdsourcing
staff.
- Accurate testing for
intercoder reliability
- ARCYBER- for macro
guidance
- Individual Analysts- for
processes
- Continued partnership
with crowdsourcing firms,
CrowdFlower,
Samasource, etc.
Beneficiaries
Mission AchievementMission Budget/Costs
Buy-In/Support
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners

4. Value Proposition Canvas - ARCYBER
Products
& Services
Desktop tracking and
analysis platform.
Customer
Jobs
Detect narratives,
identify influencers
and targets for
messaging
No way to easily track all
known individuals and
real-time action of
tweets/hashtags
Gains
Pains
Gain
Creators
Pain
Relievers
Platform for automatically
categorizing tweets,
escalating potentially viral
content early on
- Better detection ability and
improved response time to
potentially viral narratives, shut
down or respond before it gains
momentum
- Semi-automation of tweet
and user categorization
- Filing tools for user &
hashtag histories

5. Source Data
#hashtag &
keyword search
Identify suspect
accounts
Distribute to Analysts
Category 2Category 1 Category 3 Generic
Cross checking of
accounts
Customer Workflow
Twitter
website
Toolset:
-Excel / spreadsheets
-Google search
-Site search
-Recon-ng
-Maltego
Toolset:
-Topsy (defunct)
-Palantir Torch
-Recorded Future
-4iQ
Action
Legend
Current tools
Other tools
Info
Resellers
-GNIP
-Sifter
?
Toolset:
-Excel / spreadsheets
-SalesForce
-Asana

6. MVP
Problem: Extracting message meaning is difficult with current
commercial tools coming from different SM platforms
MVP Solution: Crowdsourced Categorization & Viral
Scoreboard
1. Consume data from source network (e.g. Twitter
through GNIP)
2. Search/filter queries/hashtags to identify target
messages
3. Distribute raw messages to crowd network
(Crowdflower)
4. Return sorted messages by relevant categories
5. Optimized interface for consuming/viewing data by topic
category
6. Check accounts across social media platforms
(Instagram, Facebook, public channels)