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

Meta Platforms (bStocks Tokenized Stock) (METAB) Sentiment & Fear and Greed Index

As of July 5, 2026, Meta Platforms (bStocks Tokenized Stock)'s Ruma Fear & Greed Index is 43 (Neutral), 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

Fear & Greed43 · Neutral
Sentiment0/100
Mindshare0.00%
Price$588.9 -0.0%

Latest Meta Platforms (bStocks Tokenized Stock) insights

PancakeSwap Lists Tokenized Microsoft, Meta, and 7 Other AssetsJun 30, 2026

PancakeSwap listed tokenized versions of nine major assets, including Microsoft (MSFTB), Meta (METAB), Palantir (PLTRB), and Strategy (MSTRB), as well as a Nasdaq 100 ETF token (QQQB) and other top assets.

Frequently asked questions

What is Meta Platforms (bStocks Tokenized Stock)'s Fear & Greed Index?

Meta Platforms (bStocks Tokenized Stock)'s Ruma Fear & Greed Index is currently 43 out of 100, which is Neutral. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.

Is Meta Platforms (bStocks Tokenized Stock) bullish or bearish right now?

Meta Platforms (bStocks Tokenized Stock)'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 Meta Platforms (bStocks Tokenized Stock) sentiment?

Ruma reads every relevant social post about Meta Platforms (bStocks Tokenized Stock) 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.