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Narrative Mind
Customers spoken to this week: 14
Total customers spoken to: 14
Sponsor: 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.
MVP
Problem: Extracting message meaning is
difficult with current commercial tools.
MVP Solution: Crowdsourced Categorization
1. Consume data from source network (e.g.
Twitter through GNIP)
1. Search/filter to identify target messages
1. Distribute raw messages to crowd
network (Crowdflower)
1. Return sorted messages by relevant
categories
1. Optimized interface for
consuming/viewing data by topic
category
Customer Discovery
Hypotheses ❏ Commercial software for social media analysis has limited military utility.
❏ More automation for analyzing social media by content is desired.
❏ Information overload of messages is a problem for end-users.
❏ Tracking success of counternarratives is a pain point.
Experiments ❏ Interviewed customers to uncover key pain points.
❏ Explored commercially available tools and current limitation of options.
❏ Presented options for new scalable categorization tools for uncovering topic
meaning and categorization.
Results ❏ Content categorization is a critical component for strategic responses.
❏ Monitoring viral potential of social media content is a major, underexplored area.
❏ Sentiment analysis of message data has limited utility.
❏ Generating counternarratives is difficult without human element.
Actions Moving forward with expanded tweet categorization/storage 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.
Mission Model Canvas
- Categorize social media
posts by content for
monitoring and tactical
purposes.
- Understand viral
potential 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.
- Algorithmic virality
predictor to bubble up
important, time-sensitive
threats.
- Build on design of now-
defunct Palantir Torch to
present content in a
streamlined manner.
- Force multiplier for intelligence analysts: receive cleaner, pre-
categorized data, target the most urgent priorities.
- 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.
- Testing with analysts
- MechanicalTurk or crowdsourcing labor (microtasks)
- UI Development/Testing with CYBERCOM/ARCYBER analysts.
- Software Development
- Access to Twitter
firehose (Stanford
academic license)
- Language specific
crowdsourcing staff.
- Individual Analysts
- ARCYBER
- 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
Value Proposition Canvas
Products
& Services
Desktop tracking and
analysis platform.
Customer
Jobs
Develop potential
counter narratives.No way to easily track
real-time action of
tweets/hashtags
Gains
Pains
Gain
Creators
Pain
Relievers
Platform for automatically
categorizing tweets,
escalating potentially viral.
- Better detection ability and
improve response time to
potentially viral narratives, shut
down or respond before it gains
momentum
- Semi-automation of tweet
categorization and virality
detection
- Filing tools for user &
hashtag histories

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Narrative Mind week 1 H4D Stanford 2016

  • 1. Narrative Mind Customers spoken to this week: 14 Total customers spoken to: 14 Sponsor: 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.
  • 2. MVP Problem: Extracting message meaning is difficult with current commercial tools. MVP Solution: Crowdsourced Categorization 1. Consume data from source network (e.g. Twitter through GNIP) 1. Search/filter to identify target messages 1. Distribute raw messages to crowd network (Crowdflower) 1. Return sorted messages by relevant categories 1. Optimized interface for consuming/viewing data by topic category
  • 3. Customer Discovery Hypotheses ❏ Commercial software for social media analysis has limited military utility. ❏ More automation for analyzing social media by content is desired. ❏ Information overload of messages is a problem for end-users. ❏ Tracking success of counternarratives is a pain point. Experiments ❏ Interviewed customers to uncover key pain points. ❏ Explored commercially available tools and current limitation of options. ❏ Presented options for new scalable categorization tools for uncovering topic meaning and categorization. Results ❏ Content categorization is a critical component for strategic responses. ❏ Monitoring viral potential of social media content is a major, underexplored area. ❏ Sentiment analysis of message data has limited utility. ❏ Generating counternarratives is difficult without human element. Actions Moving forward with expanded tweet categorization/storage 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. Mission Model Canvas - Categorize social media posts by content for monitoring and tactical purposes. - Understand viral potential 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. - Algorithmic virality predictor to bubble up important, time-sensitive threats. - Build on design of now- defunct Palantir Torch to present content in a streamlined manner. - Force multiplier for intelligence analysts: receive cleaner, pre- categorized data, target the most urgent priorities. - 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. - Testing with analysts - MechanicalTurk or crowdsourcing labor (microtasks) - UI Development/Testing with CYBERCOM/ARCYBER analysts. - Software Development - Access to Twitter firehose (Stanford academic license) - Language specific crowdsourcing staff. - Individual Analysts - ARCYBER - 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
  • 5. Value Proposition Canvas Products & Services Desktop tracking and analysis platform. Customer Jobs Develop potential counter narratives.No way to easily track real-time action of tweets/hashtags Gains Pains Gain Creators Pain Relievers Platform for automatically categorizing tweets, escalating potentially viral. - Better detection ability and improve response time to potentially viral narratives, shut down or respond before it gains momentum - Semi-automation of tweet categorization and virality detection - Filing tools for user & hashtag histories