What are the best datasets for tracking online trends?

For tracking online trends, the best datasets combine search interest, web traffic, social conversation, and emerging topic discovery. You’ll usually mix a few sources rather than rely on a single dataset.

1. Search interest datasets

Google Trends

• What it offers: Relative search interest over time for queries/topics, by region and category; related queries and topics.
• Best for: Spotting rising topics, seasonality, brand/category interest, geographic differences.
• Access: Web UI (trends.google.com) with CSV export; programmatic access via unofficial libraries or third‑party wrappers.

Semrush Trends / Trends API

• What it offers: Estimated traffic and audience data for domains, plus trend lines for categories and competitors.
• Best for: Competitive trend analysis, category growth, traffic share shifts.
• Access: Platform UI and API (for enterprise/qualified users).

2. Web traffic and audience datasets

Similarweb

• What it offers: Estimated monthly visits, traffic sources, engagement metrics, top countries, and trend lines for millions of sites and apps.
• Best for: Tracking growth/decline of websites, apps, and digital brands; benchmarking against competitors.
• Access: Platform UI, API, and some bulk datasets.

Semrush / Ahrefs / other SEO platforms

• What they offer: Domain traffic estimates, keyword trends, top pages, and competitive movement.
• Best for: SEO‑driven trend analysis, content opportunity detection, competitor tracking.

Public traffic rankings

• Sites like OneLittleWeb, SerpDino, and similar publish monthly “most visited websites” lists and category trends based on third‑party data (Semrush, Ahrefs, etc.).
• Useful for high‑level views of where web attention is shifting (search engines, social, AI tools, etc.).

3. Social conversation and sentiment datasets

Social listening platforms

• Brandwatch, Talkwalker, Meltwater, Hootsuite Insights, Sprout Social, etc.
• What they offer: Aggregated social mentions, hashtags, sentiment, share of voice across platforms (X/Twitter, Reddit, TikTok, Instagram, news, forums).
• Best for: Detecting emerging narratives, brand crises, campaign performance, and topic momentum.

Reddit and forum data

• Pushshift archives, Reddit APIs, Social Computing Data Repository, SNAP network datasets.
• Best for: Community‑level trend analysis, niche topics, early signals before mainstream adoption.

Twitter/X and other platform datasets

• Research datasets (e.g., DocNow tweet collections, academic corpora) and platform APIs (where available) for real‑time or historical social data.
• Best for: event‑driven trend analysis, political/social movements, brand monitoring (subject to platform rules).

4. Emerging topic and “exploding” trend datasets

Exploding Topics

• What it offers: Curated list of fast‑growing search topics across categories, with trend curves and channel breakdowns (search, social, news, e‑commerce, podcasts).
• Best for: Early discovery of breakout trends for content, product, and investment ideas.
• Access: Web UI and paid plans; some API‑style access via partners.

GDELT (Global Database of Events, Language, and Tone)

• What it offers: Massive, continuously updated dataset of global news events, themes, and sentiment.
• Best for: Macro trend analysis, geopolitical risk, narrative tracking over time.
• Access: Free bulk downloads and BigQuery tables.

5. Consumer and survey‑backed trend datasets

GWI (Global Web Index), Morning Consult, YouGov, Kantar

• What they offer: Survey‑based consumer behavior and attitude trends across countries and demographics.
• Best for: Understanding why trends are happening, demographic breakdowns, cross‑market comparisons.

E‑commerce and marketplace trend data

• Platforms like Jungle Scout, Helium 10, Stackline, and marketplace intelligence tools provide trend data on product categories, bestsellers, and search share within marketplaces (Amazon, Walmart, etc.).
• Best for: product and category trend analysis in retail/e‑commerce.

6. Public and open web datasets

• Common Crawl – Petabytes of crawled web data; useful for building custom trend indicators (e.g., frequency of terms across time).
• Internet Archive / Wayback Machine – Historical snapshots of websites for longitudinal content analysis.
• Awesome Public Datasets, Social Media Archive (SOMAR), Stanford SNAP – Curated social and web datasets for research.

How to combine them in practice

A robust online‑trend stack often looks like:

• Search demand: Google Trends + Semrush/Similarweb for query and domain trends.
• Web traffic: Similarweb/Semrush for site/app growth and category shifts.
• Social conversation: A social listening tool + Reddit/Twitter data for narrative and sentiment.
• Early signals: Exploding Topics + GDELT for breakout topics and macro narratives.
• Consumer context: Survey‑based tools (GWI, Morning Consult) to interpret trends by demographic.

If you tell me your focus (e.g., “consumer brands,” “tech/AI trends,” “investment research,” “content strategy”), I can suggest a tighter set of datasets and a simple workflow for tracking trends in that area.