What types of social media datasets does SocialIntel offer?

SocialIntel offers datasets from 100+ social media platforms including Reddit, GitHub, YouTube, Medium, Discord, Telegram, and more. Each dataset is enriched with 250+ fields covering sentiment analysis, topic detection, financial signals, engagement metrics, and content classification.

How much does SocialIntel cost?

Catalog datasets up to 10K rows are always free to download. Individual datasets start at $18. Pro plans range from $78 to $504 per month, with annual pricing from $780 to $5,040 per year.

Can I request a custom dataset?

Yes! You can submit a custom dataset request specifying the platform, fields, date range, and format. Custom datasets are priced based on row count, starting at $18.

What formats are datasets available in?

Datasets are available in CSV, JSON, JSONL, and Parquet formats. You can download them directly or access them via our REST API.

Is the data ethically sourced and legal?

Yes. We collect only publicly accessible data from social media platforms using official APIs and permissive robots.txt directives. We comply with each platform's terms of service and applicable laws. See our Legality & Compliance page for a full platform-by-platform breakdown.

Sentiment Analysis for Crypto Markets: What Social Data Actually Tells You

Crypto markets are driven by social sentiment more than traditional finance. But not all sentiment signals are equal. Here is how to extract actionable signals from social media noise.

Cryptocurrency markets are uniquely influenced by social media sentiment. A single tweet can move Bitcoin 5%. Reddit threads drive meme coin rallies. Discord communities coordinate buying pressure. Understanding these signals is essential for anyone operating in digital asset markets.

Why Crypto Is Different

Traditional financial sentiment analysis focuses on news, earnings calls, and analyst reports. Crypto sentiment is distributed across social platforms where retail participants dominate the conversation. This creates both opportunity and challenge:

**Opportunity** — Social sentiment is a leading indicator in crypto, often moving before price

**Challenge** — The signal-to-noise ratio is extremely low, and manipulation is rampant

The Social Platforms That Matter for Crypto

Not all platforms contribute equally to crypto sentiment:

**Twitter/X** — Primary channel for crypto influencers, project announcements, and market commentary
**Reddit** — r/cryptocurrency, r/bitcoin, and project-specific subreddits for community sentiment
**Telegram** — Project groups and trading signal channels (often manipulated)
**Discord** — Project communities and trading groups
**YouTube** — Influencer analysis and market commentary
**GitHub** — Developer activity and project health signals

Building a Crypto Sentiment Pipeline

Signal Extraction

We process social media posts through crypto-specific enrichment:

Filtering Manipulation

Crypto social media is heavily manipulated. Our filtering layer addresses:

**Coordinated campaigns** — Bot networks artificially inflating token mentions
**Incentivized posting** — Paid shills and bounty-driven content
**Wash trading signals** — Fake engagement designed to create momentum illusions
**Pump-and-dump patterns** — Sudden coordinated mention spikes in low-cap tokens

Signal Validation

We validate social signals against market data:

**Price correlation** — Does social sentiment predict price movement?
**Volume alignment** — Do social mention spikes correlate with trading volume?
**Cross-platform consistency** — Is the sentiment confirmed across multiple platforms?

What Actually Works

After analyzing millions of crypto social posts, here is what correlates with market outcomes:

**Sentiment shifts** — Changes in overall sentiment (not absolute levels) are predictive
**Volume anomalies** — Sudden mention spikes (excluding coordinated campaigns) precede price moves
**Developer activity** — GitHub commit frequency correlates with long-term project health
**Influencer alignment** — When multiple credible voices converge on the same token, it matters

What Does Not Work

**Single-post analysis** — Individual posts have no predictive power
**Raw mention count** — Volume without context is meaningless
**Simple positive/negative** — Crypto sentiment is nuanced; "to the moon" is positive but different from "strong fundamentals"
**Telegram signals** — Heavily manipulated and unreliable as standalone indicators

Practical Applications

**Portfolio Managers.** Augment traditional analysis with social sentiment data for timing entry and exit points.

**Market Makers.** Use sentiment velocity to adjust spreads and inventory positioning.

**Researchers.** Study the relationship between social sentiment and market microstructure.

— Heshan Sanjuka, Founder