influData Community Report analyzes up to 10,000 comments across multiple posts to reveal sentiment, depth, authenticity, topics, intent, and engagement patterns, with drill-down proof in real comments.
Comment tone influences perceptions and behavioral intentions, making monitoring campaign-critical.
Review valence is strongly linked to purchase intention (meta-analysis r = 0.563 across 156 studies).
Engagement manipulation via bots and pods exists at scale, meaning "engagement count" can be misleading.
Regulators target fake social influence indicators. The FTC final rule enables civil penalties.
See the emotional reality of the community, from very positive to very negative, with examples you can verify.
Quantify whether comments are meaningful conversations or empty noise like emojis and generic praise.
Detect signals of inflated engagement: repeated commenters, pod activity, and suspicious timing bursts.
Extract what the community discusses, what they praise, complain about, and where they are in the buying journey.
Engagement counts tell you how many people reacted. Comments tell you what the community believes, feels, and intends to do.
A headline Sentiment Score plus a 5-bucket distribution from very positive to very negative, each clickable to real examples.
See the true tone of the community and the distribution behind the score. "Good creators" can still be "bad partnerships" if the community tone trends negative, cynical, or complaint-heavy, because the comment section becomes part of the ad unit.
Negative comments can significantly reduce product attitude and perceived influencer trustworthiness, and influencer replies can mitigate some of that damage.
ResearchGate (2023)“Measure the emotional reality of a creator's community, not just the engagement count.”
Measure substance, not just volume. See Deep, Medium, and Shallow comment distributions and the top detected attributes.
Platforms reward meaningful interaction. Two creators can have the same comment count, but radically different value: one sparks thoughtful experiences and questions (high signal), the other generates emoji floods and generic "nice!" comments (low signal).
“This really helped my skincare routine! I've been using it for 3 weeks and the difference is visible.”
“🔥🔥🔥 nice!!”
Platforms have explicitly shifted ranking toward posts that spark conversations and meaningful interactions, making "conversation quality" strategically relevant.
Facebook (News Feed FYI)“Not all comments are equal. We quantify substance.”
Turn comments into a map of what the community likes, dislikes, and repeatedly discusses.
Comments are "free focus groups" at scale: what people praise, what they dislike, what they repeatedly ask about, what features and benefits they care about. Topic discovery helps brands align a creator with product positioning and identify risk areas before launch.
A 2024 survey reviewed 154 NLP publications (2013-2023) organizing common applications: sentiment analysis, customer feedback, marketing, and reputation management.
PMC (2024)“Know what the community talks about when the creator posts, and why it matters for your product.”
Catch red flags like repeated commenters, influencer-to-influencer activity, and suspicious timing bursts.
Brands face two risks: overpaying for inflated impact, and attaching the brand to manipulation ecosystems (pods, purchased engagement), which can hurt performance and trust.
Coordinated engagement pods have been measured at scale: 1.8M Instagram posts advertised across 400+ pods, with usage increasing over time.
The Pod People (McCoy et al.)The FTC's final rule (Aug 2024) prohibits buying or selling fake indicators of social media influence.
FTC“Authenticity is not a vibe. It is measurable.”
Classify comments into awareness, consideration, conversion, retention, and advocacy, with stage-specific examples.
Comments often contain intent signals: questions, comparisons, readiness to buy, recommendations. Classifying these into a funnel makes campaigns easier to interpret.
"Just found you"
"Is it worth it?"
"Where do I buy?"
"I already use it"
"You should try this"
Research indicates comment valence can influence behavioral intentions and behaviors, reinforcing that comments shape what people intend to do.
Cyberpsychology Meta-Analysis“See where the audience is in the buying journey, directly from what they say.”
Understand peak activity windows and audit whether comment arrival patterns look natural.
Natural communities often have a healthy long tail of comments; suspicious activity can show unnatural bursts or overly uniform timing. Peak hour and day data helps brands schedule posts and community management coverage.
“Spot unnatural bursts and measure healthy long-tail engagement.”
Ask freeform questions and get answers grounded in real comments with linked examples.
Stakeholders don't always want a score first. They want answers like: "What do people complain about most?", "Are people asking about price, availability, sizing?", "Do comments show purchase intent?"
“Ask a creator's community anything. Get answers grounded in real comments.”
Need content-risk screening too? Explore the influData Brand Safety Analysis: AI-powered scanning of community comments across 10 critical risk categories.
Illustrative example (not real creator data)
Most positive comments mention style inspiration and clear tutorials. The main negative theme is pricing, often phrased as questions rather than criticism.
influData turns creator comments into a decision system: sentiment, depth, authenticity, topics, and intent, backed by real examples.
Up to 10,000 comments across multiple recent posts. This provides a statistically meaningful sample of the community's comment culture.
Sentiment measures how positive or negative comments are (the emotion). Depth measures how substantive they are (the quality). A comment can be positive but shallow (e.g. a heart emoji), or negative but deep (e.g. a detailed criticism).
We analyze patterns like repeat commenter concentration, influencer-to-influencer commenting ratios, comment timing distributions, and burst patterns. These are combined into an Authenticity Score and individual signal breakdowns.
The AI Q&A answers questions based on the analyzed comments. It works best for questions about community opinions, topics, complaints, praise, and purchase intent. It cites real comments as evidence.
Brand Safety is a separate layer that scans comments for 10 critical risk categories (political, religious, violence, etc.). It's part of the Community Report suite but has its own dedicated page and analysis.
Yes. The full Community Report can be exported as a PDF, making it easy to share with stakeholders, include in campaign decks, or archive for compliance.
Sources cited on this page:
Disclaimer: This page is informational. The studies and data cited are from third-party sources; platform rules and market data can change.