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After your analysis completes, you’ll receive a comprehensive argument blueprint that maps out the logical structure, evidence, and potential fallacies in the arguments.

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Analysis Components

Your results contain several key sections:

Argument Blueprint

Visual tree structure of all arguments and counter-arguments

Summary

High-level overview of the topic and key positions

Analysis

Detailed examination of argument quality and logic

Social Pulse

Real-time social media sentiment and discussions (when available)

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The Argument Blueprint

The blueprint is a hierarchical structure that represents how arguments relate to each other.

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Node Types

Each node in the blueprint has a specific role:

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Thesis Node

The central claim or main proposition being argued.Characteristics:
  • type: 'thesis'
  • No parent node (parentId: null)
  • Root of the argument tree
  • Sets the context for all other nodes
Example:
{
  "id": "thesis-1",
  "parentId": null,
  "type": "thesis",
  "side": "for",
  "content": "Universal basic income should be implemented nationwide",
  "logicalRole": "Main proposition"
}

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Understanding Sides

Each node has a side property:

For

Arguments supporting the thesis or parent claim

Against

Arguments opposing the thesis or challenging claims
The side property is always relative to the root thesis. A counterclaim will have side: 'against' even if it supports the overall opposing position.

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Interpreting Nodes

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Node Structure

Every argument node contains these fields: 
type ArgumentNode = {
  id: string;              // Unique identifier
  parentId: string | null; // ID of parent node (null for thesis)
  type: 'thesis' | 'claim' | 'counterclaim' | 'evidence';
  side: 'for' | 'against'; // Position relative to thesis
  content: string;         // The actual argument text
  sourceText: string;      // Original text this was derived from
  source: string;          // Where this argument came from
  fallacies: string[];     // Detected logical fallacies
  logicalRole: string;     // Role in the argument structure
};

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Reading the Hierarchy

1

Start with the thesis

Find the node with type: 'thesis' and parentId: null. This is your starting point.
2

Follow the tree

Look for nodes where parentId matches the thesis id. These are direct supporting or opposing arguments.
3

Trace relationships

Continue following parent-child relationships to see how arguments build upon each other.
4

Check evidence

Look for type: 'evidence' nodes to see what factual support exists for claims.
5

Review fallacies

Examine the fallacies array on each node to identify logical weaknesses.

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Understanding Fallacies

The system automatically detects common logical fallacies in arguments.

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What is a Fallacy?

A logical fallacy is an error in reasoning that weakens an argument. Even if a conclusion is true, a fallacious argument fails to provide proper logical support.

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Common Fallacies Detected

Attacking the person making the argument rather than the argument itself.Example: “You can’t trust his climate research because he drives an SUV.”
Misrepresenting someone’s argument to make it easier to attack.Example: “They want healthcare reform, so they must want full socialism.”
Presenting only two options when more exist.Example: “You’re either with us or against us.”
Claiming something is true because an authority figure says so, without other evidence.Example: “This celebrity says the product works, so it must be effective.”
Arguing that one small step will inevitably lead to extreme consequences.Example: “If we allow this minor regulation, we’ll end up in a totalitarian state.”
Drawing broad conclusions from insufficient evidence.Example: “I met two rude people from that city, so everyone there must be rude.”
The conclusion is assumed in the premise.Example: “The Bible is true because it says it’s the word of God.”
Introducing irrelevant information to distract from the real issue.Example: “Why worry about climate change when we have economic problems?”

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Fallacies in Your Results

The fallacies field on each node contains an array of detected fallacies: 
{
  "id": "claim-3",
  "content": "We shouldn't trust climate scientists because they receive research grants",
  "fallacies": [
    "Ad Hominem",
    "Genetic Fallacy"
  ]
}
A node with fallacies doesn’t mean the conclusion is wrong—it means the reasoning is flawed. Consider whether better arguments exist for the same position.

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The Summary Section

Provides a high-level overview of:
  • The main topic or thesis
  • Key positions on both sides
  • Overall quality of the debate
  • Notable gaps or weaknesses

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The Analysis Section

Offers deeper insights:

Strength Assessment

Evaluates the overall strength of arguments on each side

Evidence Quality

Examines the quality and reliability of supporting evidence

Logical Coherence

Checks for internal consistency and logical flow

Gaps & Weaknesses

Identifies missing perspectives or weak reasoning

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Social Pulse

When available, this section shows:
  • Recent social media discussions (primarily from X/Twitter)
  • Sentiment analysis
  • Popular perspectives
  • Trending talking points
Social Pulse requires valid API credentials for X/Twitter. If not configured, this section may be empty.

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Practical Tips

1

Start broad, then narrow

Read the summary first, then the analysis, then dive into specific nodes in the blueprint.
2

Look for balance

Check if both sides have similar depth of argumentation, or if one side dominates.
3

Verify evidence

Follow up on evidence nodes with external sources to verify claims.
4

Identify strongest arguments

Focus on nodes with solid evidence and no fallacies—these are the most reliable.
5

Consider missing perspectives

Think about what arguments might be missing from the blueprint.

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Example Interpretation

Let’s interpret a simple argument structure:
[
  {
    "id": "thesis-1",
    "parentId": null,
    "type": "thesis",
    "side": "for",
    "content": "Remote work should be the default for tech companies",
    "fallacies": []
  },
  {
    "id": "claim-1",
    "parentId": "thesis-1",
    "type": "claim",
    "side": "for",
    "content": "Remote work increases employee productivity",
    "fallacies": []
  },
  {
    "id": "evidence-1",
    "parentId": "claim-1",
    "type": "evidence",
    "side": "for",
    "content": "Studies show 47% productivity increase in remote settings",
    "source": "Stanford study 2023",
    "fallacies": []
  },
  {
    "id": "counter-1",
    "parentId": "claim-1",
    "type": "counterclaim",
    "side": "against",
    "content": "Remote work reduces collaboration and innovation",
    "fallacies": ["Hasty Generalization"]
  }
]
Interpretation:
  1. The thesis proposes remote work as default
  2. A claim supports this with productivity benefits
  3. Evidence backs the claim with specific data from a credible source
  4. A counterclaim challenges the productivity argument, but contains a fallacy
  5. The “for” side has stronger support (evidence without fallacies)
  6. The “against” side needs better argumentation

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