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Narrative Mind week 1 H4D Stanford 2016


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

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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