RUMA
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Fear and Greed Index

SENTIMENT:

KOL CALLS

Long/Short Calls

MINDSHARE:

Intelligence

Emotions

Social Momentum

FEED:

Cultiness Index

Followers

Volume ($)

Volatility

Mcap vs BTC

Sentiment Timeframes

Tencent (TCEHY) Sentiment & Fear and Greed Index

As of July 5, 2026, Tencent's Ruma Fear & Greed Index is 0 (Extreme Fear), 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 & Greed0 · Extreme Fear
Mindshare0.00%

Latest Tencent insights

China Blocks NVIDIA H200 Shipments Despite US ApprovalMay 20, 2026

Chinese customs are currently blocking shipments of NVIDIA's H200 chips destined for China. This action comes despite prior U.S. government approval for NVIDIA to sell these chips to major Chinese companies like Alibaba and Tencent.

Chinese Buyers Approved for Nvidia H200 Chips; Stock JumpsMay 14, 2026

Chinese tech giants, including Alibaba, ByteDance, Tencent, and JD, have reportedly been approved to purchase Nvidia H200 chips. This significant development, following months of export speculation, has caused Nvidia's ($NVDA) stock to jump nearly 2% overnight. The news is seen as a major shift in the dynamics between Chinese buyers and U.S. chip manufacturers.

Frequently asked questions

What is Tencent's Fear & Greed Index?

Tencent's Ruma Fear & Greed Index is currently 0 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 Tencent bullish or bearish right now?

Ruma scores Tencent's social sentiment as bullish, bearish, or mixed based on LLM analysis of the crypto social conversation. Sentiment reflects market mood, not financial advice.

How does Ruma measure Tencent sentiment?

Ruma reads every relevant social post about Tencent 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.