Monitor an Executive's Reputation Across Social Media
Track every mention of a named person, an executive, founder, politician, or public figure, across Twitter/X, Reddit, Instagram, and TikTok; separate routine mentions from reputational risk; and run the sweep on a schedule so a spike surfaces the day it starts, not the week after. The recipe is name-variant keyword search per platform, an AI classification pass over the results, and a recurring digest. This is a person-shaped problem, not a brand-shaped one, and Xpoz serves it by giving an AI assistant raw posts and comments from all four platforms in one subscription, queried in plain English, with a free tier to start.
Last updated: July 21, 2026
The Problem
Executive reputation is company reputation. Global executives attribute [45% of a company's reputation and 44% of its market value to the CEO's reputation alone](https://webershandwick.com/news/the-ceo-reputation-premium-a-new-era-of-engagement) (Weber Shandwick, The CEO Reputation Premium), and reputation overall accounts for [63% of market value in the same firm's 2020 research](https://www.prnewswire.com/news-releases/reputation-accounts-for-63-percent-of-a-companys-market-value-300986105.html). When a founder gets accused in a viral thread, the damage lands on the business. Brand-monitoring dashboards miss the person. They key on brand handles, product terms, and owned accounts, but a CEO is discussed by full name in a Reddit thread, by first name in a quote tweet, and by name-plus-company in a TikTok comment section, none of which match a brand keyword list. Person mentions are also sparser than brand mentions, so a real accusation hides easily in low-volume noise. Name ambiguity makes the naive fix worse. Searching just the person's name floods results with strangers who share it; searching too narrowly misses the thread where the accusation never mentions the company. The workable middle is name-variant queries plus disambiguation context, which is tedious by hand across four platforms and impossible to sustain daily.
The Workflow
### Step 1: Build the Name-Variant Query Set
Example Queries
Ask Claude in natural language. Here are some examples with the underlying API calls:
Sweep Twitter/X for the person, disambiguated
>"Find tweets from the last 7 days mentioning "Jane Doe" AND (Acme OR logistics) OR @jdoe. Show text, author, likes, and date"
Catch Reddit discussion, including comments
>"Search Reddit posts and comments from the last week mentioning "Jane Doe". Include the subreddit and permalink for each"
Check Instagram and TikTok chatter
>"Find Instagram and TikTok posts from the last month mentioning "Jane Doe" or "Acme CEO", sorted by engagement"
Classify the sweep into risk tiers
>"Read all mentions you just collected and sort them into: routine, positive, and reputational risk (complaints, accusations, legal claims, pile-ons). Quote the riskiest 5 with links"
Why XPOZ
Frequently Asked Questions
Executive reputation monitoring is tracking every public mention of a named person (a CEO, founder, or public figure) across social platforms and classifying which mentions carry reputational risk: complaints, accusations, legal claims, or coordinated pile-ons. It differs from brand monitoring in subject (a person, not a company handle), search shape (name variants instead of brand keywords), and volume (sparser mentions where single posts matter more).
Search both posts and comments for the person's name variants, because Reddit discussions about a person usually happen in comment threads under related topics. With Xpoz, `getRedditPostsByKeywords` and `getRedditCommentsByKeywords` accept boolean queries like `"Jane Doe" AND Acme`, return the subreddit and permalink for each hit, and can be scoped to a date window for a weekly or daily sweep.
Yes, end to end. An AI assistant connected to Xpoz runs the name-variant searches across Twitter/X, Reddit, Instagram, and TikTok, reads the results, separates risk from noise using criteria you define, and produces a digest, on a schedule if you ask for one. The assistant's judgment layer is the part dashboards lack: it reads context, so a joke, a namesake, and a genuine accusation get sorted differently.
Three ways. Subject: brand tools key on handles and product terms; a person is discussed by name, often without the company attached. Ambiguity: names collide, so queries need disambiguating context (company, industry, exclusions). Volume: person mentions are sparser, so the goal is catching the one accusation-class mention early rather than charting thousands of mentions.
Get Started
Connect Xpoz to Claude or ChatGPT, then run the first sweep: ``` Find every mention of "[person's name]" across Twitter, Reddit, Instagram, and TikTok from the last 14 days. Use name variants and the company name for disambiguation. Sort into routine, positive, and reputational risk, and quote the risk items. ``` When the single sweep works, add the name variants as tracked keywords and schedule the digest. The free tier covers the first sweeps at no cost. ## Related Use Cases - **Real-Time Brand Monitoring Dashboard**: The company-side counterpart: continuous monitoring keyed on the brand rather than a person. - **Never Miss a Brand Crisis Again**: Crisis detection patterns and escalation timing that pair with the executive digest. - **Multi-Platform Brand Sentiment Aggregator**: Aggregate sentiment across the same four platforms when the subject is the company.
Open Claude settings and navigate to Connectors
Add a custom connector with URL: https://mcp.xpoz.ai/mcp
Authenticate with your account
Your first results are free: up to 75,000 across Twitter, Instagram, TikTok, and Reddit, with no credit card required.
Related Use Cases
Real-Time Brand Monitoring Dashboard
Build a real-time dashboard to track every brand mention, analyze sentiment shifts, and measure engagement across Twitter, Instagram, and Reddit from a single interface.
Security & RiskNever Miss a Brand Crisis Again
Build an AI-powered brand monitoring system that detects reputation threats in real-time and alerts your team before a social media firestorm spirals out of control.
Security & RiskMulti-Platform Brand Sentiment Aggregator
Build a unified sentiment analysis system that aggregates brand perception across Twitter, Instagram, and Reddit, revealing how audiences feel about your brand on each platform.
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