Track TikTok Trending Content by Hashtag (2026)

Published April 27, 2026 · 1504 words · For growth

Thirdwatch's TikTok Scraper makes TikTok hashtag-trend tracking a structured workflow at $0.006 per video — hourly snapshot 30+ hashtags relevant to your niche, compute views-per-hour velocity, surface rising content before peak. Built for social-media managers riding viral trends, brand-monitoring teams watching category hashtags, content researchers studying viral mechanics, and creator-strategy analysts informing recommendation models.

Why track TikTok trending content by hashtag

TikTok trends move fast and concentrated. According to TikTok's 2024 What's Next trend report, the platform's discovery algorithm weights hashtag-tagged content heavily for its For You page distribution — meaning hashtag-feed activity is the leading indicator of which content TikTok will amplify globally. For brands, creators, and analysts, hashtag-feed monitoring catches viral signal before mainstream discovery.

The job-to-be-done is structured. A social-media manager at a CPG brand watches 25 food and lifestyle hashtags hourly to time branded-content responses. A content-research team studies viral mechanics across 50 hashtags to inform their own creation strategy. A creator-economy SaaS surfaces rising hashtag-content for product feature recommendations. A trend-intel function tracks emerging cultural movements per region. All reduce to hashtag list + max results + views-per-hour ranking → structured trend dashboard.

How does this compare to the alternatives?

Three options for getting TikTok hashtag-trend data into a pipeline:

Trend SaaS bundles hashtag-trend tracking with creator search and audience demographics. The TikTok Scraper actor page gives you the data layer at pay-per-result pricing — most teams build their own dashboards on top for far less than the SaaS cost.

How to track TikTok trending content in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token. Every example below assumes the token is in APIFY_TOKEN:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull a hashtag watchlist hourly?

Pass #hashtag-prefixed terms with searchType: "videos" and a high maxResultsPerQuery.

import os, requests, datetime, json, pathlib

ACTOR = "thirdwatch~tiktok-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

HASHTAGS = ["#foodtok", "#cooking", "#recipe", "#easyrecipes",
            "#foodie", "#mealprep", "#healthyrecipes", "#ramadan",
            "#viralrecipe", "#tiktokmademebuyit", "#kitchengadget",
            "#dessert", "#breakfast", "#dinner", "#lunch"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"queries": HASHTAGS, "searchType": "videos",
          "maxResults": 600, "maxResultsPerQuery": 50},
    timeout=900,
)
records = resp.json()
ts = datetime.datetime.utcnow().strftime("%Y%m%d-%H")
pathlib.Path(f"snapshots/tiktok-trends-{ts}.json").write_text(json.dumps(records))
print(f"{ts}: {len(records)} videos across {len(HASHTAGS)} hashtags")

15 hashtags × 50 videos = up to 600 records per hourly snapshot, costing $3.60.

Step 3: How do I rank by views-per-hour velocity?

Compute age in hours from createdAt, divide views by age, filter to recent + high-velocity content.

import pandas as pd

df = pd.DataFrame(records)
df["createdAt"] = pd.to_datetime(df.createdAt)
df["age_hours"] = (
    pd.Timestamp.utcnow() - df.createdAt
).dt.total_seconds() / 3600

df["views_per_hour"] = df.viewCount / df.age_hours.clip(lower=0.5)
df["engagement"] = df.likeCount + df.commentCount * 5 + df.shareCount * 10
df["er_per_kv"] = (df.engagement / df.viewCount.clip(lower=1) * 1000)  # engagement per 1K views

trending = df[
    (df.age_hours <= 168)  # last week
    & (df.views_per_hour >= 50000)
    & (df.er_per_kv >= 4.0)
].sort_values("views_per_hour", ascending=False).head(30)

print(trending[["authorUsername", "description", "viewCount",
                "likeCount", "age_hours", "views_per_hour",
                "er_per_kv", "url"]].head(15))

A video at 50K+ views/hour with 4.0+ engagement per 1K views is genuinely viral. The two filters together separate viral content from sponsored or bot-inflated content.

Step 4: How do I forward newly-detected viral content to Slack?

Persist seen video IDs and forward only new viral entries.

import requests as r

snapshot = pathlib.Path("tiktok-viral-seen.json")
seen = set(json.loads(snapshot.read_text())) if snapshot.exists() else set()

new_viral = trending[~trending.id.isin(seen)]
for _, t in new_viral.iterrows():
    r.post(
        "https://hooks.slack.com/services/.../...",
        json={"text": (f":fire: *@{t.authorUsername}* — {int(t.viewCount/1000)}K views in "
                       f"{t.age_hours:.1f}h ({int(t.views_per_hour/1000)}K/hr)\n"
                       f"_{t.description[:200]}_\n{t.url}")},
        timeout=10,
    )

snapshot.write_text(json.dumps(list(seen | set(trending.id))))
print(f"{len(new_viral)} new viral videos forwarded")

Schedule the actor on Apify's scheduler at hourly cadence (0 * * * *) and the loop runs unattended.

Sample output

A single trending-video record looks like this. Five rows weigh ~5 KB.

{
  "id": "7345678901234567890",
  "description": "Easy 15-minute pasta recipe #cooking #recipe #foodtok",
  "url": "https://www.tiktok.com/@chefmike/video/7345678901234567890",
  "authorName": "Chef Mike",
  "authorUsername": "chefmike",
  "authorFollowers": 1250000,
  "authorVerified": true,
  "viewCount": 8500000,
  "likeCount": 425000,
  "commentCount": 3200,
  "shareCount": 18000,
  "bookmarkCount": 95000,
  "duration": 47,
  "createdAt": "2026-03-15T14:30:00Z",
  "musicTitle": "original sound",
  "musicAuthor": "Chef Mike",
  "hashtags": ["cooking", "recipe", "foodtok"],
  "isAd": false
}

createdAt feeds the age-in-hours calculation that's central to velocity ranking. bookmarkCount is the underrated quality signal — high bookmark-to-like ratio indicates content viewers want to come back to, which correlates with content that ranks well in TikTok's For You page over time. isAd: false filters out branded-content rows when you want pure organic trend signal.

Common pitfalls

Three things go wrong in production TikTok trend pipelines. Hashtag composition drift — a hashtag's video composition changes hourly as new content overtakes old; for trend stability, average velocity over a rolling 6-hour window rather than reading single-snapshot rank. Bot-inflated views — some videos receive bot views from view-farming services, which inflates viewCount without proportional engagement; the engagement-per-1K-views filter (er_per_kv >= 4.0) catches most bot-inflated content cleanly. Original-sound vs trending-audio attribution — when a video uses a viral audio track (separate from the video's musicTitle), some of the views are driven by audio-page traffic; for accurate creator attribution, dedupe on id (TikTok's stable internal video ID) before counting per-creator viral hits.

Thirdwatch's actor uses Camoufox + Playwright with XHR-response interception (TikTok's web search renders client-side). The 4096 MB max memory and 600-second timeout headroom mean even multi-hashtag batch runs complete cleanly. Pair TikTok with our Instagram Scraper and YouTube Scraper for cross-platform creator-trend research. A fourth subtle issue worth flagging: TikTok's algorithm can feature the same viral video across multiple hashtag feeds simultaneously, which means your raw multi-hashtag pull will return the same id from several queries. Always dedupe on id before computing per-hashtag metrics, otherwise viral cross-tagged videos inflate the apparent volume of every hashtag they touch. A fifth pattern unique to TikTok-trend tracking: very recent videos (under 30 minutes old) sometimes return zero or very low view counts because TikTok's distribution flywheel hasn't fully fired yet; treat sub-30-minute-old videos as not-yet-classified rather than failed velocity candidates, and rescore them on the next hourly run.

Related use cases

• Scrape TikTok profiles and videos
• Research TikTok creator engagement
• Monitor brand TikTok presence
• The complete guide to scraping social media
• All Thirdwatch use-case guides

Frequently asked questions

Why hashtag-based trend tracking specifically?

Hashtags are the cleanest TikTok-native discovery primitive. Trends concentrate in 5-50 hashtags per niche, which means tracking the right 30-50 hashtags covers most viral activity for your category. The actor's #hashtag query syntax pulls TikTok's recent-content feed for that tag — surfacing fresh viral content typically within 6-12 hours of peak velocity.

How do I distinguish viral from steady content?

Views-per-hour velocity is the canonical viral signal. Compute (viewCount / age_hours) for videos posted in the last 7 days. Videos at 50,000+ views/hour with 4.0+ engagement-rate-per-thousand-views are in active viral phase. Below 5,000 views/hour is steady-state for hashtag content; above 200,000 views/hour is mega-viral (rare, ~1-2 per major hashtag per week).

How fresh do trend signals need to be?

Hourly cadence catches viral content within 1-2 hours of peak velocity onset. For brands trying to catch a wave (jumping on a trend with branded content), 6-hour cadence is too slow — by the time you respond, the trend has decayed. For competitive intelligence (which trends are competitors riding?), daily cadence is sufficient. Each hourly run on 30 hashtags costs roughly $5-$8.

Can I detect rising hashtags before they go viral?

Yes, indirectly. Track aggregate hashtag-feed activity (sum of views and post-counts per hashtag per hour) over time; hashtags showing 3x-5x post-volume growth within 48 hours often precede viral content emergence by 24-48 hours. The earliest-stage signal is rising posts-per-hour, not rising views — by the time views spike, the trend is already obvious.

How does this compare to TikTok's Creative Center?

TikTok's Creative Center surfaces officially-tagged trending content but skews toward partnerships and ads. The actor scrapes the public hashtag feed which represents organic creator content, including the trends Creative Center misses. Use both — Creative Center for what TikTok promotes, the actor for what creators actually post in volume.

Are deleted or removed videos handled?

TikTok occasionally removes videos for policy violations, often during/after viral peaks. The actor returns whatever TikTok shows publicly at scrape time — videos removed between snapshots disappear from subsequent runs. For long-running trend dashboards, this means historical view counts on now-removed videos remain in your snapshot data but no longer match TikTok's current state. Treat snapshot data as a frozen view, not a live source of truth.

Run the TikTok Scraper on Apify Store — pay-per-record, free to try, no credit card to test.