reddit-thread-analyzer

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Reddit Thread Analyzer

Reddit帖子分析器

Extract deep insights from Reddit discussions including sentiment, key arguments, and community consensus.

When a user provides a Reddit thread URL or asks about Reddit opinions, analyze the discussion comprehensively to surface meaningful patterns and insights.

从Reddit讨论中提取深度洞察，包括情感倾向、核心论点和社区共识。

当用户提供Reddit帖子URL或询问Reddit社区观点时，全面分析讨论内容，挖掘有意义的模式和洞察。

Instructions

使用说明

1. Fetch and Parse Thread Data

1. 获取并解析帖子数据

Use WebFetch to load the Reddit thread and extract:

Post title, body, author, score, and timestamp
All comments (not just top-level)
Comment scores, awards, and timestamps
Note verified contributors or expert flair

使用WebFetch加载Reddit帖子并提取：

帖子标题、正文、作者、评分和时间戳
所有评论（不仅限于顶层评论）
评论评分、奖励和时间戳
标记经过验证的贡献者或专家标识

2. Analyze Overall Sentiment

2. 分析整体情感倾向

Determine the dominant sentiment and emotional tone:

Overall sentiment: Positive, negative, neutral, or mixed
Sentiment distribution: Approximate percentages
Emotional tone: Excited, frustrated, skeptical, supportive, angry, enthusiastic
Shift over time: Note if sentiment changes throughout discussion

确定主导情感和情绪基调：

整体情感：正面、负面、中立或混合
情感分布：大致占比
情绪基调：兴奋、沮丧、怀疑、支持、愤怒、热情
随时间变化：记录讨论过程中情感是否发生变化

3. Extract Key Arguments

3. 提取核心论点

Identify the most impactful points:

Top Arguments in Favor (3-5 points):

Quote the argument
Note comment score
Identify supporting evidence or reasoning

Top Arguments Against (3-5 points):

Quote the argument
Note comment score
Identify counter-points and rebuttals

Expert or Verified Opinions:

Highlight comments from verified experts
Note OP responses and clarifications

识别最具影响力的观点：

核心支持论点（3-5条）：

引用论点原文
标记评论评分
标注支持性证据或推理

核心反对论点（3-5条）：

引用论点原文
标记评论评分
标注反驳观点和回应

专家或验证用户观点：

突出经过验证的专家评论
记录原帖发布者（OP）的回复和澄清内容

4. Find Consensus Points

4. 识别共识点

Determine what the community agrees on:

Points with broad agreement (high scores, no controversy)
Emerging patterns across multiple comments
Common ground between opposing viewpoints

确定社区达成共识的内容：

获得广泛认同的观点（高评分、无争议）
多条评论中呈现的共性模式
对立观点之间的共同点

5. Identify Controversial Topics

5. 标记争议话题

Flag heavily debated points:

Topics with mixed upvotes/downvotes
Arguments that sparked long comment chains
Divisive issues where community is split

标记争议性强的观点：

赞踩两极分化的话题
引发长评论链的争论点
社区意见严重分裂的问题

6. Provide Structured Analysis

6. 提供结构化分析报告

Format your analysis clearly:

markdown

undefined

清晰格式化分析内容：

markdown

undefined

Reddit Analysis: [Thread Title]

Executive Summary

[2-3 sentence overview of the discussion and main takeaway]

Overall Sentiment

Dominant Sentiment: Positive/Negative/Neutral/Mixed (X%)
Emotional Tone: [excited/frustrated/skeptical/etc.]
Community Alignment: High/Medium/Low

Dominant Sentiment: Positive/Negative/Neutral/Mixed (X%)
Emotional Tone: [excited/frustrated/skeptical/etc.]
Community Alignment: High/Medium/Low

Top Arguments

In Favor

[Main point] (+XXX score)

"[Direct quote from comment]"
- [Brief explanation of reasoning]
[Main point] (+XXX score)

"[Direct quote]"

[Main point] (+XXX score)

"[Direct quote from comment]"
- [Brief explanation of reasoning]
[Main point] (+XXX score)

"[Direct quote]"

Against

[Main point] (+XXX score)

"[Direct quote]"

[Main point] (+XXX score)

"[Direct quote]"

Community Consensus

✅ [Point most people agree on]
✅ [Another consensus point]

✅ [Point most people agree on]
✅ [Another consensus point]

Controversial Topics

⚠️ [Divisive issue] - Community split roughly 50/50
⚠️ [Another debate point]

⚠️ [Divisive issue] - Community split roughly 50/50
⚠️ [Another debate point]

Notable Insights

Expert Opinion: [Quote from verified expert] (+XXX)
Surprising Take: [Unexpected perspective that gained traction]
Most Helpful: [Most practical or actionable advice]

Expert Opinion: [Quote from verified expert] (+XXX)
Surprising Take: [Unexpected perspective that gained traction]
Most Helpful: [Most practical or actionable advice]

Key Quotes

"[Memorable quote]" - u/username (+XXX score) "[Another impactful quote]" - u/username (+XXX score)

Discussion Quality

Civility: High/Medium/Low
Depth: Superficial/Moderate/Deep
Evidence-based: Yes/No/Mixed

undefined

Civility: High/Medium/Low
Depth: Superficial/Moderate/Deep
Evidence-based: Yes/No/Mixed

undefined

Best Practices

最佳实践

Focus on highly upvoted comments for consensus
Include exact scores to show community agreement level
Quote directly rather than paraphrasing
Preserve nuance - avoid oversimplifying complex debates
Note OP responses - original poster often adds important context
Distinguish facts from opinions clearly
Highlight constructive vs. unproductive discussions
Consider recency - early comments may be less informed than later ones

重点关注高赞评论以识别共识
包含准确评分以体现社区认同度
直接引用原文而非意译
保留细节差异 - 避免过度简化复杂争论
记录原帖发布者（OP）的回复 - 原作者通常会补充重要背景信息
明确区分事实与观点
区分建设性讨论与无效讨论
考虑时效性 - 早期评论可能不如后期评论信息全面

Example Analysis

示例分析

User: "What does Reddit think about the new iPhone?"

Your analysis:

Fetch r/apple or r/iPhone thread
Analyze 300+ comments
Determine sentiment: Mixed (55% positive, 45% negative)
Extract top pros: Camera improvements (+450), Performance (+380)
Extract top cons: High price (+420), Incremental updates (+390)
Note consensus: Good phone, but expensive for what you get
Identify controversy: Whether it's worth upgrading from iPhone 14
Surface expert opinions from tech reviewers
Deliver structured report with quotes and scores

Remember: Focus on substance over noise. Prioritize well-reasoned arguments over emotional reactions.

用户：“Reddit社区对新款iPhone的看法是什么？”

你的分析：

获取r/apple或r/iPhone版块的帖子
分析300+条评论
确定情感倾向：混合（55%正面，45%负面）
提取核心优势：相机提升（+450赞）、性能表现（+380赞）
提取核心劣势：价格过高（+420赞）、升级幅度小（+390赞）
记录共识：手机本身不错，但性价比低
识别争议点：从iPhone 14升级是否值得
呈现科技评论者的专家观点
交付包含引用和评分的结构化报告

注意：聚焦实质内容，而非无关噪音。优先考虑有理有据的论点，而非情绪化反应。