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Fear and Greed Index

Sentiment:

KOL Calls

Long/Short Calls

Mindshare:

Intelligence

Emotions

Social Momentum

Feed:

Conviction Index

Followers

Volume ($)

Volatility

Mcap vs BTC

Sentiment Timeframes

Sophon (SOPH) Sentiment & Fear and Greed Index

As of July 22, 2026, Sophon's Nebula Fear & Greed Index is 31 (Fear), its social sentiment score is 43/100 (mixed), it holds 0.00% of crypto social mindshare. These signals are computed by Nebula from social posts across crypto Twitter/X and other sources, scored with large language models rather than keyword counts.

Updated continuously · Source: Nebula

Fear & Greed31 · Fear
Sentiment43/100
Mindshare0.00%
Price$0.0047 +1.1%

Latest Sophon insights

Base Mainnet Crashes Twice After Sophon Migration NewsJun 26, 2026

Base mainnet crashed twice after Sophon announced it is sunsetting its chain and migrating to Base. The outages occurred on June 26, 2026, highlighting network strain from the migration news.

Sophon Shuts Down L2, Migrates to Base for Consumer AppsJun 25, 2026

Sophon, a ZK-powered Layer 2 project that raised $60 million, announced it is shutting down its native blockchain and migrating to Base. The team will pivot to building consumer applications as a technology studio under the SOPH brand, citing that infrastructure chains no longer hold value.

Frequently asked questions

What is Sophon's Fear & Greed Index?

Sophon's Nebula Fear & Greed Index is currently 31 out of 100, which is Fear. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.

Is Sophon bullish or bearish right now?

Sophon's social sentiment is currently mixed, with a sentiment score of 43/100 based on how bullish or bearish the crypto social conversation is. Sentiment reflects the mood of the market, not price direction or financial advice.

How does Nebula measure Sophon sentiment?

Nebula reads every relevant social post about Sophon across crypto Twitter/X and other sources and scores it with large language models — capturing bullish/bearish tone, emotion, and who is speaking (from retail to smart money) — rather than counting keywords.