Turn Reddit Scout Findings Into a Comment Strategy
Identify citation gaps, compare model-specific citation rates on Reddit threads, and target threads without a brand mention with comments that earn AI citations.
Reddit Scout shows which Reddit threads drive AI citations for a cluster. To retrieve that table, see How Reddit Scout Surfaces Threads. A citation gap is a high-traffic thread that LLMs cite when answering your target prompts, but in which your brand is not mentioned. Targeting these threads with comments converts citation data into a growth channel.
ChatGPT and Perplexity cite sources differently. A thread can receive dozens of citations on ChatGPT and almost none on Perplexity, or the reverse. Reading citation rates per model identifies the threads where a comment is most likely to win citations on the engines you prioritize.
This tutorial uses Content Hub MCP to analyze citation gaps and build a comment strategy.
Retrieve the citation breakdown and isolate gaps
Request the Reddit citations for your cluster, including brand mentions. This example uses the Developer Marketing cluster in the Infrasity organization.
Claude Desktop returns the cluster's citation footprint as two lists. The first lists threads with a confirmed brand mention.
The top 2 mentioned threads account for 38.2% of all citation volume in the cluster. Brand visibility depends on a small number of discussions.
The second list shows citation gaps, ranked by missed-mention opportunity.
These are high-volume threads that LLMs cite for queries such as "recommend an agency" and "best b2b marketing agencies", where Infrasity is not mentioned.
Claude identifies three patterns:
- Engine split: 5 of 6 mentioned threads have ChatGPT citations, and only 2 have Perplexity citations. Several unmentioned threads, such as ranks 4 and 5, drive substantial Perplexity citation volume.
- Subreddit clustering: Mentions appear in generic r/SaaS and r/B2BSaaS threads, not in developer-native communities such as r/devrel or r/webdev, which also generate citation volume.
- Declining mention rate: The overall mention rate decreased as the thread pool grew. Without ongoing engagement, brand presence thins out.
Request takeaways
With the gap data in the conversation, request a summary of the implications for comment prioritization.
Claude returns five findings:
- Concentration risk: Losing either of the top two mentioned threads would remove a large share of AI visibility at once.
- Mention volume: The 2.8% mention rate appears low, but citations in mentioned threads account for approximately 41% of total citation volume.
- High-intent gaps: Ranks 3, 4, 5, 7, and 8 are high-intent agency-recommendation threads with no brand presence. Combined, they hold almost as much citation weight as the top mentioned threads.
- Community gap: The brand appears in generic business forums but not in developer-native subreddits, where technical buyers evaluate tools.
- Engine disparity: Mentions favor ChatGPT, and Perplexity citations are underrepresented.
Build an engine-specific comment plan
Convert the gaps into an ordered, model-specific plan.
1. Target threads without a brand mention, based on model citation rates. Use engine citation counts to match each thread to a goal:
- Close a Perplexity gap: Target unmentioned threads with high Perplexity citation counts, such as rank 4 in r/startups (36 Perplexity citations, 0 ChatGPT).
- Expand ChatGPT citations: Target unmentioned threads with high ChatGPT citation counts, such as rank 3 in r/u_Temporary_Meeting182 (41 ChatGPT citations).
- Compound impact across engines: Prioritize threads cited by both models, such as rank 6.
2. Match the intent of the prompt, not only the topic. The five highest-value unmentioned threads are high-intent recommendation requests. Answer the question the thread asks, such as agency deliverables, pricing transparency, or technical developer expertise. Do not post a generic brand plug.
3. Write comments that LLMs can cite. AI models synthesize detailed, structured answers and rarely cite brief promotional comments. Match the substance of the highest-performing mentioned thread (rank 1, r/B2BSaaS). Include concrete examples, specific capabilities, and objective context that an LLM can extract for recommendation summaries.
4. Re-audit on a recurring schedule. Repeat the retrieval every sprint or month. This identifies new high-citation threads, confirms which unmentioned threads now carry a brand mention, and verifies whether your comments produce new AI citations.
The entire strategy derives from your cluster's live AI citation data.
Open app.infrasity.com and review your own cluster's Reddit citation gaps to select the next threads to target.
