JunGugugugu

JunGugugugu / AI-Based-Academic-Email-Filtering-System

Public

Experimental academic email scam detection system for undergraduate thesis research.

20
0
100% credibility
Found Feb 05, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool that uses AI to scan academic emails for scams by checking suspicious links, fake conferences, and urgent claims.

How It Works

1
🔍 Discover Email Protector

You learn about a helpful tool that spots fake emails pretending to be from academic conferences or journals.

2
📁 Gather Your Emails

Collect your suspicious academic emails and save them in a folder on your computer.

3
🧠 Connect Smart Helper

Link the tool to an AI thinking service so it can analyze tricky scam patterns like you would with a sharp friend.

4
▶️ Run the Check

Start the scan and let it examine senders, links, and conference mentions in your emails.

5
📊 Get Clear Results

Receive a simple report labeling each email as safe or scam, with easy explanations of why.

✅ Stay Safe from Scams

Your inbox is now guarded, letting you focus on real academic opportunities without worry.

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AI-Generated Review

What is AI-Based-Academic-Email-Filtering-System?

This Python-based experimental system detects scam emails targeting academics, like fake conference invites or predatory journal pitches. You feed it emails from EML files or IMAP inboxes (Gmail, Outlook), and it uses GPT-4 for chain-of-thought analysis, URL safety checks, and conference verification to classify them as scam or legit—with confidence scores, reasoning, and red flags. Built for undergraduate thesis research on academic email filtering, it outputs JSON results you can pipe into alerts or logs.

Why is it gaining traction?

In a sea of generic spam filters, this stands out with academic-specific smarts: it flags shady .xyz submission links or unverified NeurIPS knockoffs that slip past Gmail. Developers dig the quick demo script for testing scam samples, plus easy OpenAI integration for custom LLM prompts. As an experimental GitHub project akin to haystack experimental GitHub or ue4ss experimental GitHub efforts, it hooks researchers tweaking detection logic without starting from scratch.

Who should use this?

PhD students drowning in bogus ICML invites, profs verifying conference deadlines amid inbox chaos, or undergrads building AI security theses. Ideal for scanning research email folders before forwarding suspicious ones, or prototyping scam detection in Python scripts for personal filters.

Verdict

Skip for production—1.0% credibility score, 20 stars, and demo-only docs scream experimental research prototype, not battle-tested tool. Fork it for academic-based detection experiments if you're okay hacking LLM prompts yourself.

(178 words)

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