Truth or Deception: Unveiling Fake News with AI

Prompt

Topic: Fake News & Misinformation Detection using NLP + Transformers Why this is a strong poster topic: Extremely relevant (social media, elections, AI-generated content) Lets you show cool visuals (real vs fake headlines, model pipeline, attention maps) Easy to explain but still research-heavy You can include BERT, RoBERTa, LLMs → makes it look advanced 🧠 Core Idea (for your poster) “Using NLP and Transformer-based models to automatically detect fake news articles and misinformation online.” 📄 3 Strong IEEE Research Papers 1. Title: Fake News Detection Using Machine Learning and Natural Language Processing Source: IEEE Key Points: Uses NLP preprocessing + ML models (SVM, Naive Bayes) Good for basic pipeline diagram Shows feature extraction (TF-IDF, n-grams) 2. Title: BERT-Based Fake News Detection on Social Media Source: IEEE Key Points: Uses BERT Context-aware understanding (better than traditional NLP) Great for showing transformer architecture visually 3. Title: Multimodal Fake News Detection Using Deep Learning Source: IEEE Key Points: Combines text + images Detects fake news using both caption and image mismatch Very creative poster opportunity (real vs fake visuals) 🎨 How to Make Your Poster Creative (this is where you win) 🔹 Section Layout Idea: Hook Title “Can AI Tell Truth from Lies?” Problem Section Show 2 headlines (real vs fake) Pipeline Diagram Data → Preprocessing → BERT Model → Classification → Output Model Visualization Attention mechanism (highlight words like “shocking”, “breaking”) Results Accuracy comparison (ML vs BERT vs Multimodal) Real-world Impact Elections, social media, misinformation

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