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Hyperlane (HYPER) Sentiment & Fear and Greed Index

As of July 6, 2026, Hyperlane's Ruma Fear & Greed Index is 32 (Fear), its social sentiment score is 38/100 (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

Fear & Greed32 · Fear
Sentiment38/100
Mindshare0.00%
Price$0.0727 -4.0%

Latest Hyperlane insights

Hyperlane Surges 60% with TRON Integration, Tapping 370M AccountsApr 25, 2026

Hyperlane experienced a 60% price surge following its integration with the TRON blockchain. This strategic move connects Hyperlane to over 370 million TRON accounts, significantly expanding its reach. Despite the breakout, weak money flow signals suggest caution regarding sustained momentum.

TRON Elevates Cross-Chain Role with Hyperlane IntegrationApr 20, 2026

TRON has integrated with Hyperlane, a move widely seen as significantly broadening its capabilities within the blockchain space. This integration transforms TRON from a standard public chain into what is perceived as the 'heart' or 'big treasury backend' for the entire chain. The development suggests enhanced interoperability and a more central role for TRON in the wider crypto ecosystem.

Frequently asked questions

What is Hyperlane's Fear & Greed Index?

Hyperlane's Ruma Fear & Greed Index is currently 32 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 Hyperlane bullish or bearish right now?

Hyperlane's social sentiment is currently bearish, with a sentiment score of 38/100 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 Hyperlane sentiment?

Ruma reads every relevant social post about Hyperlane 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.