Employer brand monitoring: what candidates see about you online

November 8, 2026

Candidates research you the way they research a product: they search your name plus "interview", "layoffs" or "working at" on Reddit, TikTok and YouTube, and they read your LinkedIn page. Employer brand monitoring means running those same searches on a schedule, coding what you find by topic, and comparing it with the employers you compete with for hires.

Who does this

• Talent acquisition teams who want to know why offers are declined.
• Employer brand and HR comms measuring whether careers content reaches anyone.
• Recruiting agencies briefing clients on how candidates see them.
• Founders of growing companies before a hiring push.

The data, per platform

Once a month, read deeper on the top ten videos: youtube comments API or tiktok comments API for what viewers asked, and tiktok transcript API for what was said. That adds about 20 credits.

Worked example: cost

52 credits a week × 4.3 = about 224 credits, plus 20 for the monthly deep dive: 244 credits a month, about $0.46 on the $47 pack. The 100 free signup credits cover almost two weeks. The real cost is the hour someone spends coding results, so keep the query set fixed and small.

A topic frame for coding results

Tag every relevant result with one topic and one tone (positive, neutral, negative). Six topics cover most of what candidates discuss:

The metrics

• Share of candidate voice = your relevant results / all relevant results across you and your peers. Track it monthly.
• Net sentiment by topic = (positive − negative) / total for that topic. One overall number hides that pay is fine and interviews are not.
• Interview complaint rate = negative interview-process results / all interview-process results. This is the one recruiting can fix fastest.
• Careers content engagement = (reactions + comments) on careers posts / company followers × 1,000. Compare with your other posts, not with peers of very different size.
• Peer gap = your net sentiment − the peer average, per topic. Put this on the first slide.

A weekly Reddit pull

import collections, os, requests

KEY = os.environ["SCRAPEIO_API_KEY"]
H = {"x-api-key": KEY}
COMPANIES = ["acme", "globex", "initech", "umbrella"]
TOPICS = {
    "pay": ["salary", "offer", "equity", "bonus"],
    "interview": ["interview", "take-home", "ghosted", "recruiter"],
    "culture": ["manager", "culture", "toxic", "burnout"],
    "stability": ["layoff", "freeze", "reorg"],
}

counts = collections.Counter()
for co in COMPANIES:
    for q in (co + " interview", "working at " + co):
        d = requests.get("https://scrapeio.com/v1/reddit/search",
                         params={"query": q, "sort": "new", "timeframe": "week"},
                         headers=H, timeout=60).json()["data"]
        for post in d.get("items", d.get("posts", [])):
            text = ((post.get("title") or "") + " " + (post.get("caption") or post.get("selftext") or "")).lower()
            for topic, words in TOPICS.items():
                if any(w in text for w in words):
                    counts[(co, topic)] += 1

for (co, topic), n in sorted(counts.items()):
    print(co, topic, n)

Check the list key and text fields in your first response. Keyword tagging is a first pass; a person should confirm the tone.

Pitfalls and limits

• Aggregate, never individual. Report topics and counts. Do not track named employees, try to identify anonymous posters, or keep files on people who criticize you.
• Employee speech is protected in many places. In the US, the NLRB says employees may talk with co-workers about wages, benefits and working conditions, and that an employer cannot discharge, discipline or threaten them for that protected concerted activity (NLRB). This is not legal advice; involve counsel before acting on anything you find about current staff.
• Name collisions. "Acme interview" also finds cartoon fans. Add your industry word or exclude obvious noise.
• Small samples. Ten Reddit threads a month is anecdote. Use quarterly totals for trends.
• Old threads resurface. Filter by date; a 2023 layoff thread is still the top search result for years.

Run it from an AI agent

claude mcp add --transport http scrapeio https://scrapeio.com/mcp --header "x-api-key: $SCRAPEIO_API_KEY"

Then ask: "For acme and three peers, run reddit_search for '[company] interview' sorted new, this month, and linkedin_search_posts for '[company] hiring', last month. Tag each result by topic (pay, interview, culture, stability, work setup, growth) and tone. Give me a table of net sentiment by topic per company, with links. Do not name individual employees." Tools are on the LinkedIn MCP server and Reddit MCP server pages.

How to do it with ScrapeIO

LinkedIn company pages and post search, Reddit, TikTok, YouTube and X search all come through one key, mostly at 1 credit per call, with the same field names for likes and comments everywhere. Under 250 credits a month means one $47 pack covers years of this routine; credits never expire.

Sources

Checked on 8 November 2026 from each vendor's own pages. Prices and limits change; follow the links before you decide.

• https://www.nlrb.gov/about-nlrb/rights-we-protect/the-law/employees/concerted-activity