Human vs. AI Text Comparison Study: How to Identify AI-Generated Content Using Linguistic Features

This article introduces an open-source framework that compares the differences between human-written texts and those generated by mainstream large language models like GPT, LLaMA, and Claude using stylometry, readability, and emotional features, providing a practical tool for AI content detection and linguistic research. The project is from GitHub (author: yashasvis2415-cell, published on June 16, 2026) and aims to address issues such as information authenticity and academic integrity caused by the blurring boundary between AI-generated content and human texts.

With the popularization of large language models like ChatGPT and Claude, AI-generated content has permeated all aspects of daily life, blurring the boundary between human and AI texts. Traditional plagiarism detection tools are helpless against AI-generated "original" content, so developing a framework to systematically compare the features of human and AI texts is of great practical significance for understanding AI language characteristics and establishing detection mechanisms.

This open-source project supports text analysis of multiple mainstream large language models (GPT series, LLaMA, Falcon, Gemma, OPT, Claude, etc.). The core dimensions include:

1. Stylometric Features: Vocabulary level (vocabulary size, diversity, average word length, etc.), sentence level (quantity, average sentence length, complexity), and text macro indicators (total word count, character count);
2. Readability Analysis: Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, SMG Index, and Automated Readability Index (ARI);
3. Emotional Feature Analysis: Extracting eight basic emotional dimensions (joy, sadness, anger, fear, trust, disgust, surprise, and anticipation) based on the NRC Emotion Lexicon.

Core Dependencies: Pandas & NumPy (data processing), NLTK (NLP basics), TextStat (readability calculation), NRCLex (emotion analysis), Matplotlib & Seaborn (visualization). Data Processing Flow: 1. Collect human-written samples and AI-generated texts; 2. Preprocess and clean (remove formatting, unify encoding, etc.); 3. Extract the three types of features in parallel; 4. Store results in a structured format; 5. Generate comparative visualization charts.

Typical Features of AI Texts: Lower vocabulary diversity, more standardized sentence structures (lacking human "imperfections"), more even emotional distribution, and concentrated readability scores. Practical Applications: Academic research (standardized analysis tool), content moderation (identifying AI content), education (assisting in identifying AI ghostwriting), author attribution research (forensic identification/literary research), and AI model evaluation (linguistic dimension assessment).

Current Limitations: The dataset size needs to be expanded; rapid model iterations make results prone to obsolescence; it mainly targets English texts; adversarial AI texts may bypass detection. Expansion Directions: Introduce semantic/syntactic features (dependency parse trees, semantic role labeling); train ML classifiers for automated detection; develop interactive visualization dashboards; expand multilingual support; build a real-time detection API.

The study of human vs. AI text comparison is a window to understanding the essence of AI and human language. This open-source project provides a scientific and quantifiable method to explore the similarities and differences between human and AI languages, which is valuable for developers (toolset), researchers (expansion platform), and ordinary users (dealing with the proliferation of AI content). The boundary between human and AI texts may become more blurred in the future, but the exploration process will promote a deeper understanding of language, intelligence, and creativity.