Axelar (AXL) Sentiment & Fear and Greed Index
As of July 5, 2026, Axelar's Ruma Fear & Greed Index is 28 (Fear), its social sentiment score is 52/100 (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 Axelar insights
A new ecosystem integrated with Axelar via Solana, using Axelar's interoperability infrastructure for communication between Solana and other networks.
Axelar has announced the successful completion of a Proof of Concept (PoC) for a Won-based stablecoin. This pioneering project was conducted in collaboration with XRP Ledger Korea and Hana Financial TI. The PoC demonstrates the feasibility of issuing and managing a stablecoin pegged to the Korean Won, marking a significant step in localized stablecoin development.
LayerZero's protocol infrastructure has reportedly been hacked, prompting questions regarding the exploit's nature and methods. This incident is widely seen as a significant opportunity for competing cross-chain protocols like Wormhole, Circle, Chainlink, and Axelar to capture market share. Despite the immediate setback, industry observers suggest such events can ultimately drive innovation within the broader ecosystem.
Frequently asked questions
What is Axelar's Fear & Greed Index?
Axelar's Ruma Fear & Greed Index is currently 28 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 Axelar bullish or bearish right now?
Axelar's social sentiment is currently mixed, with a sentiment score of 52/100 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 Axelar sentiment?
Ruma reads every relevant social post about Axelar 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.