> ## Documentation Index
> Fetch the complete documentation index at: /llms.txt
> Use this file to discover all available pages before exploring further.

# AI Social Insights

> Qualitative intelligence for topics, sentiment, search intent, and AI research—convert community conversations into actionable product, support, and content decisions.

> [!NOTE]
>
> This dashboard augments quantitative usage analytics with qualitative insight—surfacing what people talk about, how they feel, what they search (but cannot find), and why churn or satisfaction shifts are emerging.

> [!NOTE]
>
> **Sentiment Analysis has moved.** The sentiment analysis capabilities described here have been expanded into a dedicated feature with AI-powered topic detection, per-topic summaries, and thread-level analysis. See [Sentiment Analysis](/analytics-and-moderation/social+-portal/dashboard/sentiment-analysis.md) for the new experience.

> [card] Topic Mentions
>
> Volume & share of conversation

> [card] Sentiment
>
> Emotional direction & skew

> [card] AI Summaries
>
> Condensed thematic reasoning

> [card] Recommendations
>
> Actionable remediation & leverage

> [card] AI Research
>
> Ad‑hoc natural language analysis

> [card] Search Intent & Gaps
>
> Unmet demand & content prioritization

## What Can You Answer in Minutes?

> [tab] Product Feedback
>
> Which features generate praise vs friction and their emerging pain themes.

> [tab] Release Impact
>
> Did the latest update shift topic mix or sentiment trajectory?

> [tab] Support Deflection
>
> Which high-frequency searches fail → candidate help docs / in‑app tips.

> [tab] Content Strategy
>
> Which gap topics warrant articles, videos, or onboarding flows.

> [tab] Risk Monitoring
>
> Identify negative clusters early (bugs, policy issues, safety concerns).

## Daily Operational Workflow

**1. Open & Filter**

Select date range, locale, platform/app segment, and (optionally) user cohort (new vs returning) to focus analysis.

**2. Scan Topic Shifts**

Compare Top Mentions list vs prior period % change to flag emerging themes.

**3. Assess Sentiment**

Check Overall Sentiment gauge; drill into topics with highest negative skew.

**4. Read AI Summaries**

Skim negative → neutral → positive summaries to capture root causes & delights.

**5. Review Recommendations**

Validate AI suggestions; convert high-confidence items into backlog tickets (label with source tag).

**6. Run Research Query**

Pose a targeted natural language question for deeper synthesis (e.g., "Main complaints about video uploads this week").

**7. Evaluate Search Gaps**

Open Search Insights tab; prioritize new content for highest Gap Index terms.

**8. Log Actions**

Record chosen remediations with owner & ETA; schedule follow-up check next cycle.

## Community & Topic Filtering

> [!NOTE]
>
> Refine any insight to the communities and topics that matter—filtering removes noise so trend, sentiment, and gap signals stay actionable.

> [card] Topic Filter
>
> Focus on specific themes

> [card] Community Filter
>
> Isolate audience segments

> [card] Combined Scope
>
> Intersect topic + community for precision

### Why Use Community-Level Filters?

* Targeted insights: Each community exhibits distinct engagement & sentiment patterns.
* Faster diagnosis: Narrow scope to confirm whether an issue is broad or localized.
* Segment strategies: Tailor interventions (content, moderation, onboarding) per cohort.
* Reduce noise: Avoid dilution from mega-communities overshadowing smaller niches.

### Getting Started

| Requirement | Details |
| - | - |
| Access | Portal login with Social Insights permission |
| Feature Enablement | Social Insights enabled (contact support if disabled) |
| Data Window | Default range (e.g., last 30 days)—adjust as needed |

> [!NOTE]
>
> Need Social Insights enabled? Email \[[support@social.plus](mailto:support@social.plus)]\(mailto:[support@social.plus](mailto:support@social.plus)) with your tenant ID.

### Interface Tour

| Element | Location | Purpose |
| - | - | - |
| Filter Bar | Top of insights view | Houses Topic & Community selectors + date range |
| Topic Dropdown | Left selector | Choose one or multiple topics to include |
| Community Dropdown | Right selector | Search & pick one or more communities |
| Date Range | Right side | Adjust temporal scope for comparison |

> [accordion] Default View
>
> All communities & topics aggregated (e.g., total mentions count across full corpus) until any filter is applied.

> [accordion] Progressive Narrowing
>
> Apply topic first to isolate theme, then add community filter to test audience-specific variance.

> [accordion] Reset Strategy
>
> If results feel sparse, clear the most recently added filter first to widen context.

### Applying Filters (Step-by-Step)

**1. Open Topic Menu**

Click Topic: Select topic; search or scroll the list.

**2. Select Topic(s)**

Choose one (start simple) or multi-select to form a thematic cluster.

**3. Observe Metrics**

Note changes in Total Mentions, sentiment bars, emerging topics list.

**4. Add Community Filter**

Open Community: Select community; search e.g. "Fashion & Style"; select.

**5. Compare Before/After**

Is sentiment skew or gap profile materially different? Capture delta.

**6. Iterate**

Test additional communities to spot outliers; avoid over-filtering below statistically meaningful volume.

### Interpreting Filtered Results

> [tab] Low Volume Caution
>
> If filtered mentions fall below reliability threshold (internal baseline), treat insights as directional only.

> [tab] Divergent Sentiment
>
> A community showing negative skew vs global baseline indicates localized friction—investigate context posts.

> [tab] Gap Localization
>
> High Gap Index within one community reveals niche documentation/content deficit.

> [tab] Adoption Variance
>
> Topic share % shifts highlight uneven feature or content adoption patterns.

> [tab] Escalation Routing
>
> Community-specific negative themes can route to specialized support or product squads.

### Best Practice Patterns

> [accordion] Sequential Filtering
>
> Apply topic FIRST to anchor analysis, then layer community to avoid premature fragmentation.

> [accordion] Benchmark Preservation
>
> Keep an unfiltered baseline tab open for quick relative comparisons.

> [accordion] Volume Guardrail
>
> Set a minimum mentions threshold (e.g., 300) for acting on sentiment shifts.

> [accordion] Change Logging
>
> Record filter combinations used when generating recommendations to ensure reproducibility.

> [accordion] Periodic Reset
>
> Once per week, review global view to avoid tunnel vision from niche segments.

### Example Workflow

1. Start unfiltered → identify emerging topic.
2. Apply topic filter → confirm growth & sentiment skew.
3. Add top 3 related communities → detect which exhibits worst negative skew.
4. Run AI Research prompt: "Root causes of negative sentiment about \<topic> in \<community>."
5. Validate references → create targeted remediation ticket.

> [!NOTE]
>
> Combining topic + community filters early in investigation accelerates root cause isolation but always validate sample size before action.

## Modules & Interpretation

### Mentions & Topic Breakdown

Highlights conversation concentration. A healthy distribution usually shows a balanced long-tail—extreme concentration may indicate a blocking issue OR a successful campaign.

### Trend Graph

Multi-line frequency lines reveal acceleration or decay. Sustained upward slope + positive sentiment → amplify; upward + negative → triage.

### Sentiment (Overall + Per Topic)

Overall gauge (−100 to +100, where 0 is balanced). Correlate dips with deployment timestamps or incident reports. Topic sentiment bars isolate outlier issues masked by aggregate positivity.

### AI Post Summaries

Condense thousands of posts into 5–10 key statements per sentiment polarity. Treat as directional—spot check references before broad decisions.

### AI Recommendations

Strategic remediation / opportunity list derived from the weighted intersection of volume, sentiment skew, and recency. Confidence score (if shown) reflects model certainty; low-confidence items may require manual verification.

### AI Research (Conversational Analyst)

Natural language Q\&A across indexed conversations. Returns a structured mini-report: Introduction, Methodology, Findings, References (canonical post links) to enable traceability.

### Search Insights & Content Gaps

Maps expressed intent (search queries) vs existing content coverage. Gap Index ranks unmet demand to drive documentation & self-service improvements.

## Metrics & Signals

| Metric | Definition | Why It Matters | Action Trigger |
| - | - | - | - |
| Total Mentions | Total topic references in filtered period (one thread counts once per topic it is classified under) | Measures conversation volume / engagement energy | Spike > X% without planned campaign |
| Topic Share % | Mentions of topic / total mentions | Identifies dominance or neglect | Single topic >40% share 2+ days |
| Emerging Topic | Topic with high period-over-period growth and baseline low prior volume | Surfacing new opportunities or issues | Growth rate > threshold (e.g., >150%) |
| Overall Sentiment Score | Net sentiment across topic mentions, `(positive − negative) / total × 100`, on a −100 to +100 scale | Health & morale indicator | Drop of >5 pts d/d or >8 pts w/w |
| Topic Sentiment Skew | Deviation between topic sentiment and overall baseline | Prioritizes hotspots | Negative skew > (baseline -10) |
| Negative Summary Themes Count | Distinct negative themes extracted | Complexity of issues / support load | Rapid increase in count |
| Total Searches | Volume of internal search queries | Demand / intent scale | Sustained decline (possible UX findability issue) |
| Trending Searches Count | # of search terms with rapid growth | Emerging informational needs | Large increase → accelerate content creation |
| Content Gap Count | Unique queries with low/no results | Self-service deficiency | > predetermined target |
| Gap Index | Weighted (query volume vs results available) | Prioritization of content production | Top index unchanged across cycles (stale backlog) |
| Zero-Result Rate % | (Queries with no result) / total searches | Support deflection efficiency | Rate > target (e.g., >8%) |

## Leveraging AI Research Effectively

> [tab] Problem Investigation
>
> "Why did sentiment drop after release 5.2?"

> [tab] Feature Validation
>
> "User perception of the new Stories feature this week."

> [tab] Support Intelligence
>
> "Top causes of failed media uploads last 7 days."

> [tab] Adoption Barriers
>
> "Reasons new users mention abandoning chat setup."

> [tab] Opportunity Mining
>
> "Unprompted requests related to live events."

## Prompt Patterns

> [accordion] Comparison
>
> "Compare sentiment for video uploads vs live streaming this month."

> [accordion] Root Cause
>
> "Summarize primary reasons behind negative sentiment about notifications."

> [accordion] Cohort Split
>
> "Differences in topics between new and returning users last 14 days."

> [accordion] Temporal Shift
>
> "What changed in top 5 topics compared to previous week?"

> [accordion] Opportunity
>
> "Identify feature improvement opportunities mentioned positively but low volume."

## Related

> [card] [Sentiment Analysis](/analytics-and-moderation/social+-portal/dashboard/sentiment-analysis.md)
>
> AI-powered sentiment scoring, topic detection, and thread-level analysis

> [card] [Activity Analytics](/analytics-and-moderation/social+-portal/dashboard/activity-analytics.md)
>
> Quantitative usage metrics

> [card] [Raw Data Export](/analytics-and-moderation/social+-portal/dashboard/raw-data-export.md)
>
> Deeper custom analysis

> [card] [User History](/analytics-and-moderation/console/user-and-content-management/user-social-history.md)
>
> Drill into individual behavior

> [!NOTE]
>
> Need advanced taxonomy tuning, extended retention windows, or custom sentiment domains? Contact support for enhanced AI configuration options.


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