Content Explorer → Trending Topics

Discover trending content with Ahrefs Content Explorer and validate angles with Mavera Focus Groups

Scenario

Use the Content Explorer to find trending content in your niche — articles with high social shares, referring domains, and estimated traffic. Filter for content published in the last 90 days to catch emerging trends, then create personas representing the audiences engaging with that content and validate content angles through a focus group before investing in production.

Architecture

Code

Python

import os, requests, time
from datetime import datetime, timedelta

AH = os.environ["AHREFS_API_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
MB, TOPIC = "https://app.mavera.io/api/v1", "AI content marketing"
AH_H = {"Authorization": f"Bearer {AH}", "Accept": "application/json"}
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

cutoff = (datetime.utcnow() - timedelta(days=90)).strftime("%Y-%m-%d")
resp = requests.get("https://api.ahrefs.com/v3/content-explorer/search", headers=AH_H, params={
    "query": TOPIC, "mode": "title", "limit": 100,
    "order_by": "referring_domains:desc",
    "select": "title,url,published_at,organic_traffic,referring_domains,facebook_shares",
})
resp.raise_for_status()
articles = resp.json().get("articles", [])
trending = sorted(
    [a for a in articles if a.get("published_at", "") >= cutoff
     and (a.get("referring_domains", 0) >= 5 or a.get("facebook_shares", 0) >= 50)],
    key=lambda a: a.get("referring_domains", 0), reverse=True)
print(f"Found {len(trending)} trending articles for '{TOPIC}'\n")

personas = []
for seg in ["B2B SaaS content manager", "freelance SEO consultant", "VP Marketing"]:
    p = requests.post(f"{MB}/personas", headers=MH, json={
        "name": f"Reader: {seg}",
        "description": f"Searches '{TOPIC}'. Engages with high-share content. Role: {seg}.",
    }).json()
    personas.append(p["id"])
    time.sleep(0.3)

concepts = [f"'{a['title']}' — {a.get('referring_domains', 0)} sites linked"
            for a in trending[:5]]
fg = requests.post(f"{MB}/focus-groups", headers=MH, json={
    "name": f"Trending: {TOPIC}", "persona_ids": personas,
    "questions": [
        "Which angle first?\n" + "\n".join(f"{i+1}. {c}" for i, c in enumerate(concepts)),
        "What's missing from current coverage of this topic?",
        "Would you share this with your team? Why or why not?",
    ], "responses_per_persona": 2,
}).json()

for _ in range(30):
    time.sleep(5)
    data = requests.get(f"{MB}/focus-groups/{fg['id']}", headers=MH).json()
    if data.get("status") == "completed": break
for r in data.get("responses", [])[:9]:
    print(f"[{r.get('persona_id', '?')}] {r.get('answer', '')[:300]}\n")

Example Output

{
  "trending_articles_found": 34,
  "top_article": {
    "title": "How AI Is Replacing Traditional Content Calendars",
    "referring_domains": 47, "facebook_shares": 892, "organic_traffic": 3200
  },
  "focus_group_highlights": [
    { "persona": "B2B SaaS content manager", "preferred_angle": "#2 — AI replacing calendars" },
    { "persona": "VP Marketing", "preferred_angle": "#4 — ROI measurement for AI content" }
  ]
}

Error Handling

Empty results for niche topics

Content Explorer indexes broadly but may miss very niche topics. Broaden your query or remove the mode: title filter to search full text instead.

Social share counts lagging

Facebook and Twitter share counts update asynchronously and may lag by 24-48 hours. Use referring domains as the primary engagement signal for recent content.