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  • Deep Learning
  • NLP

Multimodal Context-Aware Hate Speech Detection

A multimodal system that detects hate speech in social media by fusing text and image signals.

Overview

RoBERTa predicts hate-speech probability from post text while CLIP does the same for the attached image. A weighted 60/40 fusion combines the two into a final decision, tested across both known and unseen datasets to check cross-domain generalisation.

Problem Statement

Hateful posts increasingly split the payload across modalities — benign text with a hateful image, or the reverse. A text-only classifier passes them straight through.

Proposed Solution

Score text and image independently and fuse the two probabilities, so a post is judged on the combination a human actually sees rather than on one modality alone.

Key Features

  • RoBERTa Text Classification

    Predicts hate-speech probability from text content.

  • CLIP Image Classification

    Predicts hate-speech probability from associated images.

  • Weighted Score Fusion

    Combines text and image predictions (60/40 weighting) for a final decision.

  • Cross-Domain Evaluation

    Tested on both known and unseen datasets.

Technologies Used

Deep Learning

  • RoBERTa
  • CLIP
  • PyTorch
  • Hugging Face

Fusion

  • Weighted score fusion
  • Threshold calibration

Application

  • Flask / Streamlit
  • Pandas
  • Matplotlib

Project Workflow

  1. 1

    Post ingestion

    Text and any attached image are extracted.

  2. 2

    Text scoring

    RoBERTa produces a hate-speech probability.

  3. 3

    Image scoring

    CLIP produces an independent probability.

  4. 4

    Fusion

    Scores are combined with 60/40 weighting.

  5. 5

    Evaluation

    Performance is checked on unseen datasets.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

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