A Behavioural Plugin Tool for Fake News Identification

The rapid spread of fake news on social media platforms poses significant risks, influencing public opinion and undermining trust in media. This tool aims to enhance the accuracy of fake news detection by developing a multimodal system that integrates both text and image analysis.

Exclusive Features of the tool

Multimodal News Analysis

Combines text, image, and metadata features using deep learning to capture cross-modal signals and context. This fusion improves the accuracy of fake news detection across diverse content types.

Emotion-Based Comments Analysis

Uses NRC Emotion Lexicon and emoji semantics to detect public emotional response and novelty cues in replies.

Region-Based Embeddings

Regions extracted from news titles are mapped to IDs, then encoded using neural networks to generate embeddings. These vectors capture geographic context for improved fake news detection.

Real-Time Trust & Sentiment Scoring

Instantly displays a credibility label and sentiment score with color-coded feedback, helping users quickly assess the authenticity and emotional tone of news content.

How the tool works

The plugin integrates behavioural signals, text semantics, and image content to detect fake news in real-time while browsing social media posts.

Browser Extension

Captures Reddit post IDs in real time as users scroll. Sends the data via an HTTP POST request to the FastAPI backend (/analyze endpoint).

FastAPI Backend on AWS

Hosted on AWS EC2, the FastAPI backend receives requests and uses PRAW (Python Reddit API Wrapper) to fetch the full Reddit post content based on the post ID.

Feature Extraction & Processing

Fetched content is passed through multiple ML feature extractors such as text cleaning, emotion classification, and region detection, preparing the data for analysis.

Prediction & UI Display

The processed data is sent to a Hybrid-BERT trained model that returns a real/fake label and a confidence score. This result is returned as JSON and displayed directly inside the Reddit feed via the extension.

Research & technology

We use a Hybrid BERT model that combines deep language understanding with tabular features like sentiment, emotion, and engagement. This fusion improves fake news detection by capturing both context and behavior. Built with FastAPI and Reddit APIs, our system enables accurate, real-time analysis for social media platforms.

Emotion Analysis

  • Emoji-based sentiment extraction
  • NRC Emotion Lexicon Mapping
  • Emotion Polarity Score Calculation
  • Comments grouped into 3 groups
  • Concatanate with Features

Sentiment Analysis

  • News content extraction and preprocessing
  • Text normalization (stopwords, lemmatization)
  • Sentiment classification using TextBlob
  • Polarity score computation (Positive/Neutral/Negative)
  • Concatenated with features

Image Captioning

  • The process starts by obtaining the image URL.
  • The image is downloaded using the `requests` library.
  • The BLIP model is loaded using `transformers`.
  • The BLIP model generates a caption for the image.
  • The caption is decoded into human-readable text.
  • The decoded caption is sent to the Text Analysis Unit.

Geographic Analysis

Geographic Analysis Process
  • Input Raw Titles – Extract location clues directly from news headlines.
  • Geographic Area Detection – Use NLP to identify place names and map them to real-world locations.
  • Sub-Continent Grouping – Cluster detected places into broader geopolitical regions.
  • Region ID Assignment – Assign numerical IDs to each region for embedding into the ML model.

Technologies

  • Python – Core backend language for processing and ML models
  • FastAPI – A lightweight and high-performance web framework used to connect the browser plugin with the machine learning model
  • TensorFlow & PyTorch – For training and deploying text/image models
  • NLTK & TextBlob – Natural language processing and sentiment analysis
  • BLIP – Vision-language model for generating image captions
  • NRC Emotion Lexicon – Emotional analysis of comment sentiment
  • JavaScript & Chrome Extension API

Backend

Demo of the Extension

User guide to install and use the Browser plugin