1INCH (1INCH) Sentiment & Fear and Greed Index
As of July 5, 2026, 1INCH's Ruma Fear & Greed Index is 19 (Extreme Fear), its social sentiment score is 0/100 (bearish), 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 1INCH insights
1inch promptly developed and deployed emergency routing to alleviate ETH withdrawal congestion on Aave. This swift action, completed in under six hours following Fluid's request, helped clear critical liquidity issues during a period of heightened liquidation risk.
Aave's Wrapped Ether (WETH) pool recently experienced 100% utilization, effectively pausing all WETH withdrawals and trapping depositors. In response, ecosystem participants like 0xFluid and 1inch swiftly introduced solutions. These included direct swaps for aWETH against looped positions or into other tokens like wstETH/weETH to help users regain liquidity.
A new '666 FREE mint IP collection' has been announced for the Ethereum blockchain. This collection is notably backed by prominent projects 1inch, Kick, and Stake. It has also secured approval from a T1 streaming service and plans a $MUFFIN token airdrop immediately following the mint.
Frequently asked questions
What is 1INCH's Fear & Greed Index?
1INCH's Ruma Fear & Greed Index is currently 19 out of 100, which is Extreme Fear. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.
Is 1INCH bullish or bearish right now?
1INCH's social sentiment is currently bearish, with a sentiment score of 0/100 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 1INCH sentiment?
Ruma reads every relevant social post about 1INCH 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.