Siren (SIREN) Sentiment & Fear and Greed Index
As of July 5, 2026, Siren's Ruma Fear & Greed Index is 24 (Fear), its social sentiment score is 60/100 (mixed), it holds 0.00% of crypto social mindshare. These signals are computed by Ruma from social posts across crypto Twitter/X and other sources, scored with large language models rather than keyword counts.
Updated continuously · Source: Ruma
Latest Siren insights
A SIREN whale sold holdings on-chain, receiving 28 million USDT over 24 hours. The whale deposited 25.7 million USDT to exchanges Bitget and Bybit, and retains 478 million SIREN tokens.
A whale dumped $7.5 million worth of $SIREN tokens, causing the price to crash over 90% in five days and wiping out $760 million in market capitalization. The whale still holds 82% of the supply, while over $2.4 million in long positions were liquidated. The incident highlights risks of concentrated token ownership and pump-and-dump dynamics.
A significant whale has withdrawn over 31.5 million $SIREN tokens, valued at more than $21 million, including an additional $1.34 million today. These substantial movements coincide with a 51% price drop for the $SIREN token within a 24-hour period.
Frequently asked questions
What is Siren's Fear & Greed Index?
Siren's Ruma Fear & Greed Index is currently 24 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 Siren bullish or bearish right now?
Siren's social sentiment is currently mixed, with a sentiment score of 60/100 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 Ruma measure Siren sentiment?
Ruma reads every relevant social post about Siren 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.