social-media-intelligence

Name: social-media-intelligence
Availability: InStock
Author: daddyelonmusk69

Extract real-time financial sentiment signals from social media platforms.

Updated Apr 12, 2026

Search Tags:#social media #twitter #discord #finance #sentiment analysis #reddit #trading

Ready to run

npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill social-media-intelligence-daddyelonmusk69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: social-media-intelligence
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/finance/social-media-intelligence
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill social-media-intelligence-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the manual, fragmented effort of gathering and interpreting financial signals scattered across Twitter/X, Telegram, Discord, and Reddit, providing a unified, real‑time view of market sentiment for traders and analysts.

Core Features & Use Cases

Multi‑platform data collection: Built‑in scripts for Twitter/X (tweepy or ntscraper), Telegram (Telethon), Discord (discord.py), and Reddit (PRAW) fetch recent messages, posts, and channel content related to tickers.
Sentiment quantification: Choose between fast VADER scoring, finance‑specific FinBERT, or optional LLM‑driven analysis, with utilities to weight retail, KOL, and institutional voices.
Buzz and fear‑greed metrics: Compute message volume, author diversity, and buzz‑z‑scores, then synthesize a fear‑and‑greed index that flags extreme market moods.
Factor construction & testing: Turn sentiment scores into cross‑sectional factors, evaluate IC/ICIR, and orthogonalize against traditional signals for robust trading models.
Compliance safeguards: Built‑in guidance for API terms, data masking, retention limits, and platform‑specific usage policies.

Quick Start

Ask the skill to pull the latest sentiment scores for $AAPL from Twitter, Reddit, and Discord and return a combined sentiment index.

Frequently Asked Questions about social-media-intelligence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract financial sentiment signals from social media platforms like Twitter and Reddit?▼

To extract financial sentiment signals from social media, you can use Python libraries like tweepy and PRAW to fetch posts, then compute sentiment using VADER or FinBERT. This provides a real-time view of market sentiment for tickers.

Do I need API credentials for Twitter, Discord, and Telegram to gather market sentiment?▼

Yes, gathering market sentiment from Twitter, Discord, and Telegram requires valid API credentials for each platform. The Skill uses platform-specific libraries like Telethon and discord.py to authenticate and fetch real-time financial discussion data.

What is the difference between VADER and FinBERT for social media sentiment analysis?▼

VADER offers fast, rule-based sentiment scoring, while FinBERT provides finance-specific, machine-learning-driven analysis. You can choose either model to quantify social media posts, with options to weight retail, KOL, and institutional voices.

Can I turn social media sentiment scores into cross-sectional factors for trading models?▼

Yes, you can turn sentiment scores into cross-sectional factors for trading models. The Skill evaluates IC/ICIR and orthogonalizes sentiment against traditional signals to build robust, data-driven trading strategies.

How do I compute a fear and greed index from social media buzz and message volume?▼

You compute a fear and greed index by calculating message volume, author diversity, and buzz-z-scores from social media data. This synthesizes extreme market moods to flag potential shifts in retail sentiment.

What are the limitations of using social media sentiment for financial trading analysis?▼

Limitations include strict API terms, data retention limits, and platform-specific usage policies. The Skill includes compliance safeguards for data masking and retention to ensure social media sentiment analysis adheres to platform regulations.