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.
Explore the advanced capabilities of our fake news detection plugin designed for real-time analysis and trust scoring. Built using cutting-edge technology and behavioural insights.
Combines text, image, and comment behavior analysis to assess news authenticity across multiple dimensions.
Uses NRC Emotion Lexicon and emoji semantics to detect public emotional response and novelty cues in replies.
Generates semantic captions for tweet images using ResNet and BiLSTM models to match visuals with content.
Instantly displays a trust score badge with color-coded feedback to help users assess news credibility at a glance.
The plugin integrates behavioural signals, text semantics, and image content to detect fake news in real-time while browsing social media posts.
Automatically scrapes tweet text, image, and top replies from the current browser tab.
Applies language models to analyze the factual tone, keywords, and sentence structures.
Classifies comment emotions using NRC Lexicon and emoji patterns to detect user sentiment.
Uses AI to describe images and compare them with text for semantic alignment or contradiction.
The system fuses text, image, and comment-based emotion vectors to generate a final trust score shown to the user instantly.
The consequences of fake news are far-reaching, impacting political stability, public health, and societal trust in media sources. Detecting and addressing fake news is essential for maintaining the integrity of information and reducing the harmful effects of misinformation on society.
