A Possible Design Direction!

LLM-Powered Facet Navigation (Sidecar)

The LLM-powered Facet Navigation Drill-down Interface enhances NewsVoy’s ability to strategically organize complex news content by applying polynym structures—sets of conceptual facets defined as Parts, Steps, and Types. Language models automatically generate, tag, and interpret these facets, transforming raw information into structured, actionable insight. This enables users to filter content by meaning, drill down into layered topics, visualize narrative trends, and align messaging across teams. Facet Navigation supports deep content analysis and real-time strategic decision-making, offering a valuable layer for nonprofits operating in dynamic and fast-changing information environments.

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Quick Summary (green font):

What It Is

Facet Navigation is a new AI-powered interface for NewsVoy that uses large language models (LLMs) to automatically organize and tag news content by meaning.
It replaces flat or manual tagging with a structured system of “facets” — conceptual categories like Parts, Steps, and Types — to help users explore, filter, and interpret complex news data.

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What It Does

• Uses LLMs to analyze each article, create a short summary and caption, and assign semantic tags (facets).
• Facets are organized into hierarchies (“polynyms”) that can show relationships like:
  • Parts → different sides of an issue (e.g., Access / Security)
  • Steps → stages in a process (e.g., Registration → Voting → Counting)
  • Types → categories or groups (e.g., Media / NGO / Government)
• Users can then drill down through topics, filter by tone or bias, and visualize narrative trends across news coverage.

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Problems It Solves

1. Information overload → Breaks down large streams of articles into topical pathways.
2. Vague tagging systems → Introduces structured, AI-generated tags.
3. Siloed data and tools → Connects outputs from different systems (news feeds, analytics, sentiment data).
4. Fragmented messaging → Links day-to-day content to campaign or organizational goals.
5. Inconsistent collaboration → Provides AI guidance for tagging and organizing content.
6. Shallow analytics → Turns raw counts into interpretable patterns and strategic insights.

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How It Fits in the NewsVoy Ecosystem (Detailed Section)

Facet Navigation acts as the semantic control layer across all NewsVoy plugins.
It organizes, filters, and visualizes data produced by other tools — turning their outputs into actionable insight.

Plugin	Function	How It Integrates with Facet Navigation
Sentiment	Measures article tone	Facet Navigation filters or groups tone within specific facets (e.g., compare “Access” vs “Security” tones).
MetaBias	Detects media bias	Allows users to view or contrast coverage by ideological orientation within each topic facet.
Geotagger	Adds regional data	Links regions to facet Types (e.g., Type: Geography → Midwest). Enables regional trend visualization.
Trend Tracker	Tracks mentions or coverage over time	Shows how coverage of a particular facet (like Mail Voting) changes across weeks or regions.
Scraper	Fetches and imports news content	Passes raw articles to the Facet Navigation system for automatic tagging and structuring.

Ecosystem Role:
Facet Navigation doesn’t just sit beside other plugins — it binds them together. It’s the strategic hub that:

• Unifies sentiment, bias, geography, and trend data under one conceptual framework.
• Provides users with interactive filters, timelines, and maps based on these linked facets.
• Keeps the system modular — plugins can evolve independently while still feeding into a shared semantic structure.
• Translates complex analytics into clear, editorially useful insights for decision-making.

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Why It’s Nice 

Facet Navigation helps teams move from data collection to interpretation.
It gives nonprofits and organizations a way to:

• See not just what is being reported, but how it’s being framed.
• Maintain consistent, strategic understanding across teams.
• Quickly adapt messaging or focus in dynamic information environments.

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In short:
Facet Navigation transforms NewsVoy from a data aggregation platform into a semantic intelligence system, organizing the chaos of modern news into clear, interpretable, and actionable structure

Note: (Eye of Sauron: Security risks and other reasons not to use, as we already touched on. Maybe not necessary could be the best. But it is a discussion worth having. The key principle is isolation, which means separating critical functions, such as data handling and publishing, into independent modules with restricted communication. Plus, there are ways to use polynym hierarchies, LLM free.)

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Extra Information & Clarifications

Core Problems Faceted Navigation Solves

1. Information Overload
Too many articles, posts, and sources flood organizational workflows. Staff can’t keep up, which means important updates get missed or delayed.

Faceted navigation filters and organizes the stream into manageable, topical pathways.

2. Flat or Generic Tagging Systems
Most content is labeled with broad, vague terms like “elections” or “voting,” which offer little clarity or usefulness. Manual tagging is slow and often inconsistent.

Faceted tagging using modes like Part, Step, and Type adds clarity, structure, and search reliability.

3. Siloed Data and Tools
Social media, news monitoring, CMS systems, and analytics tools operate separately. Teams lack a unified view of what content exists, what it says, and how it performs.

Facet-based navigation connects content across systems under shared conceptual frameworks.

4. Lack of Strategic Orientation
Teams often struggle to link daily content to broader campaign objectives. Messaging becomes fragmented or inconsistent across staff, chapters, or affiliates.

Facet systems enable top-down alignment, allowing staff to drill down from goals to actions.

5. Inconsistent Collaboration
Volunteers or junior staff often don’t know how to classify, prioritize, or tag content. Even with permissions, editorial consistency breaks down.

Faceted tools and LLM-powered sidecars provide tag suggestions and editorial context to guide team contributions.

6. Analytics Without Interpretation
Data dashboards may show counts (like “10 mentions”), but fail to explain trends or suggest responses. Strategic insight is missing.

Facets give context to data. Trends become meaningful when filtered by tone, outlet bias, or region.

Summary
Without a structured facet system, organizations are left to sift through overwhelming streams of undifferentiated information. Faceted navigation turns that stream into strategy.

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Polynym Facet Taxonomy by Count

Mononym (1 Facet)
A single core concept that anchors interpretation.
Example: Trust

Bionym (2 Facets)
A binary framing or duality between opposing or balancing ideas.
Example: Fair / Unfair
Mode: Part

Trionym (3 Facets)
A triangulation or spectrum across three related concepts, often including a central theme.
Example: Access / Security / Trust
Mode: Part

Tetranym (4 Facets)
A multidimensional view of a topic, often useful for categorization or analysis.
Example: Voter ID / Mail Voting / Cybersecurity / Audits
Mode: Part

Pentanym (5 Facets)
Used to describe phases or complete cycles.
Example: Registration / Voting / Tabulation / Reporting / Certification
Mode: Step

Hexanym (6 Facets)
Useful for stakeholder mapping or system breakdown.
Example: Voters / Lawmakers / Election Officials / Courts / Media / NGOs
Mode: Type

Heptanym (7 Facets)
A structure for narrative complexity or layered messaging.
Example: Ballot Access / Fraud Claims / Turnout / Legitimacy / Tech / Lawsuits / International Observation
Mode: Part

Octanym (8 Facets)
Represents multilateral systems or structural comparisons.
Example: Federalism / State Control / Local Implementation / Standards / Audits / Rules / Disputes / Remedies
Mode: Step

Enneanym (9 Facets)
Models complete policy ecosystems or deep topic maps.
Example: Civic Education / Registration Access / Tech / Security / Misinformation / Voting Method / Counting / Legal Recourse / Perception
Mode: Step and Type

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Polynym Facet Modes

Part
Facets represent components or opposing sides of a whole.
Example: Fair / Unfair, Security / Access

Step
Facets represent a stage in a sequence or progression.
Example: Registration → Voting → Counting → Certification

Type
Facets represent categories, classes, or kinds.
Example: Left-Leaning / Right-Leaning, NGO / Media / Government

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Sidecar Example: Article Summarization and Facet Tagging Assistant

What It Does
This LLM-powered sidecar is triggered when NewsVoy scrapes a new article. It performs two tasks:

1. Summarizes the article in one to two sentences, generates a headline and social media caption.
2. Applies semantic tags using polynym paths, facet mode, sentiment, media bias, and complexity rating.

How It Works

• Article is scraped by NewsVoy
• Text is sent to the LLM sidecar
• LLM returns:
  • Summary
  • Caption
  • Polynym tags (e.g., Election Integrity > Access > Mail-In Voting)
  • Facet Mode: Step
  • Sentiment: Neutral
  • Bias Score: Lean-Right

Why It Matters

• Gives editors a jump start on content
• Tags content accurately for filtering and analytics
• Keeps the core NewsVoy system modular and focused
• Easily expandable to more content types and languages

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Deeper Look: Polynym Facet Modes

Faceted systems can be organized using three primary facet modes: Part, Step, and Type. This structure supports consistent interpretation and classification across a wide range of topics.

Part
A component or side of a whole.
Function: Expresses opposition, duality, or balance.
Example: Fair / Unfair, Security / Access, Transparency / Secrecy

Step
A stage in a sequence or process.
Function: Expresses order, evolution, or progression.
Example: Registration → Voting → Counting → Certification

Type
A kind or classification.
Function: Groups concepts by nature, category, or identity.
Example: Mail-in Voting / In-person Voting, Right-Leaning / Left-Leaning, NGO / Government / Media

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Facet Modes Across Polynym Sizes

Mononym (1 Facet)
Example: Trust
Can function as a Part of confidence, a Step toward legitimacy, or a Type of voter sentiment.

Bionym (2 Facets)

Trionym (3 Facets)

Tetranym (4 Facets)

Pentanym and Higher (5–9 Facets)
At higher levels, facet modes can be mixed. However, maintaining consistent mode logic is beneficial:

• Use Steps to represent sequences (e.g., 5-stage election lifecycle).
• Use Parts to group mechanisms (e.g., 6 security factors).
• Use Types to filter categories (e.g., 7 types of stakeholders).

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Application in NewsVoy

Each facet can be stored with its mode designation in the system:

This enables structured filtering and analysis:

• Filter by mode: Show all Steps in the voter journey.
• Compare within a mode: Assess how sentiment varies across Parts of the process.
• Visualize by mode: Display trends with heatmaps, timelines, or progression charts.

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Faceted Navigation as a Plugin

Position in System Architecture
Faceted Navigation is not simply another tool. It serves as the organizing framework for the outputs of analytical plugins.
It operates like a semantic control tower—positioned above sentiment analysis, bias detection, geographic filters, and trend tracking—allowing users to drill down through content in structured, meaningful ways.

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Function as a Plugin

Inputs

• Tags generated by language models (topics, themes, types, parts, steps)
• Metadata from other plugins (sentiment, bias, region, timestamps)

Processes

• Organizes inputs into hierarchical facet trees (from mononym to enneanym)
• Applies consistent facet modes: Part, Step, Type
• Enables interactive filtering and drill-down by facet

Outputs

• Sidebar filters, tag clouds, and visual graphs
• Search interface for other plugins (e.g., “Show trends for Mail-In Voting tagged as Type: Left-Leaning, Step: Certification”)
• Reusable tags for editorial workflows and team collaboration

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Why It Belongs in the Plugin System

• Modular: Can be toggled on or off, customized per organization
• Reusable: Applies across topics, from election integrity to climate policy
• Scalable: Adapts as new facets and topics emerge
• UI-Focused: Translates plugin outputs into actionable, non-technical insights

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Ecosystem Fit

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Founder Summary
Faceted Navigation is more than a plugin. It is a strategic interface that turns plugin outputs into structured insight. It helps organizations explore news content by tone, bias, geography, or trend through well-defined conceptual frameworks.

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Facet Count Taxonomy for Polynym Structures

This taxonomy outlines how varying the number of conceptual facets helps structure and interpret complex topics, particularly within political and issue-driven content such as election integrity.

Mononym (1 Facet)
Represents a core concept or anchor term.
Example: Trust

Bionym (2 Facets)
Represents contrast or opposition between two related ideas.
Example: Fair / Unfair

Trionym (3 Facets)
Presents a spectrum or triangulated structure that includes common ground.
Example: Access / Security / Confidence

Tetranym (4 Facets)
Supports dimensional analysis of interrelated concepts.
Example: Voter ID / Mail Voting / Cybersecurity / Audits

Pentanym (5 Facets)
Illustrates systems or phase-based workflows.
Example: Registration / Voting / Tabulation / Reporting / Certification

Hexanym (6 Facets)
Used for stakeholder mapping or role classification.
Example: Voters / Lawmakers / Election Officials / Courts / Media / NGOs

Heptanym (7 Facets)
Represents narrative complexity or layered messaging.
Example: Ballot Access / Voter Fraud Claims / Turnout / Legitimacy / Tech Systems / Lawsuits / International Comparisons

Octanym (8 Facets)
Models multilateral systems or structural comparisons.
Example: Federalism / State Control / Local Implementation / National Standards / Audits / Reporting Rules / Disputes / Remedies

Enneanym (9 Facets)
Depicts full-cycle modeling or comprehensive policy ecosystems.
Example: Civic Education / Registration Access / Tech Infrastructure / Security Protocols / Misinformation Defense / Voting Method / Vote Counting / Legal Recourse / Public Perception

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Application in NewsVoy

Faceted Understanding
Allows editors and users to tailor their view of content according to its conceptual depth or complexity.

Smart Tagging
Supports classification of articles based on polynym size—ranging from single-issue headlines to deep policy overviews.

Search Filtering
Enables users to select the level of conceptual depth needed, from quick binary filters to triangulated or systemic searches.

Visual Analytics
Facilitates meaningful data visualization by mapping content saturation across different facet levels.

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Polynym Tree: Election Integrity

1. Election Integrity (Superordinate Concept)
Framing: Fair, secure, and trustworthy elections

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2. Access and Inclusion
Emphasizes voter rights and ease of participation

• Voter Registration Access
• Mail-In Voting Policies
• Early Voting Availability
• Language and Disability Access

Used by civil rights groups and pro-voter reform advocates.

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3. Security and Fraud Prevention
Emphasizes protection against fraud and manipulation

• Voter ID Laws
• Signature Verification
• Ballot Chain-of-Custody
• Cybersecurity of Voting Machines

Used by some election officials and conservative-aligned narratives.

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4. Transparency and Oversight
Emphasizes public accountability

• Post-Election Audits
• Live Ballot Tracking
• Poll Observer Rules
• Media Access to Counting Rooms

Valued across political lines, though debated in implementation.

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5. Disinformation and Public Trust
Focuses on information quality and voter confidence

• Election Misinformation
• Social Media Manipulation
• Partisan Narratives
• Voter Confidence Metrics

Critical for restoring faith in the system, especially post-2020.

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6. Legislative and Legal Landscape
Focuses on laws, litigation, and regulatory change

• State Voting Law Changes
• Federal Voting Rights Acts
• Litigation Around Ballot Access
• Emergency Provisions (e.g., pandemic-related voting rules)

A fast-evolving and highly contested area.

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7. Partisan versus Nonpartisan Narratives
Highlights framing conflicts in public discourse

• “Securing the Vote” (restrictive framing)
• “Protecting Voter Access” (inclusive framing)
• Fact-Checking Claims
• Polling Data on Public Perceptions

Helps analyze rhetorical bias and partisan framing in coverage.

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Implementation in NewsVoy Interface

Faceted Search Panel
Sidebar with collapsible filters based on facets and subfacets. Example:

• [✓] Election Integrity
  • [✓] Access and Inclusion
  • Security and Fraud Prevention
  • [✓] Disinformation and Public Trust

Users can drill down to specific subtopics such as “Mail-In Voting” or “Cybersecurity”.

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Auto-Tags on Articles
Example: An article from a national outlet may be tagged as:

• Election Integrity > Access and Inclusion > Mail-In Voting
• Disinformation and Public Trust > Misinformation

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Analytics Dashboard
Visual tools reflecting content volume and framing:

• Sentiment chart by facet
• Bar graph comparing article volume for “Security and Fraud” versus “Access and Inclusion”
• Map highlighting regional activity related to “Election Law Changes”

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LLMs and Polynym Generation

Language models can support polynym development by identifying, generating, and refining conceptual facets for complex topics. These facets act as semantic scaffolding that helps structure and interpret information in meaningful ways.

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How LLMs Can Support Polynym Structures

LLMs can be used to:

• Identify foundational facets of a concept
  Example: Democracy → Representation, Participation, Accountability
• Map abstract or philosophical constructs
  Example: Freedom → Autonomy, Self-Determination, Liberation
• Suggest polynyms across different disciplinary lenses
  Example: Framing climate change through economic, psychological, and technological dimensions
• Label clusters of related documents or news content
  Purpose: Improve search, filtering, and organizational insight

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Example: Election Integrity

A polynym for the topic “Election Integrity” could include the following facets:

• Access – Voter availability and inclusion
• Security – Physical and digital protections
• Transparency – Oversight mechanisms and visibility
• Accuracy – Reliability of vote counting
• Trust – Public perception and confidence

These facets can be applied in NewsVoy as:

• Search filters
• Content tagging labels
• Sentiment or bias classification categories
• Data visualization dimensions

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LLM Integration in NewsVoy

NewsVoy can integrate LLMs to:

• Automatically generate polynyms for emerging or ongoing topics
• Tag news content with facet-based labels for deeper filtering and analysis
• Enable editors and teams to view stories through multiple interpretive angles
• Support consistent messaging and shared frameworks across affiliate organizations