Rumor Stance Prediction

Project Code :TCMAPY413

Objective

The main objective of this project is to use machine learning techniques for rumor classification in twitter data and evaluate the results obtained by those machine learning algorithms.

Abstract

Now a days false claims and rumors are been spreading very widely through the social media which may affect people perspective and may also lead some harm. This rumors are mainly growing faster through the twitter platform which is essential to classify or detect the posted news is a true incident or just a rumor. In our proposed model we are using Machine learning and deep learning techniques to classify the posted news was a rumor or not. Here, we are using Naive Bayes, Support Vector Machine (SVM) and Logistic Regression models from the machine learning and Neural Network from the deep learning. The classification is performed on the stance that was possessed by the tweet. Those stance are divided into four categories, named as supporting, deny, query and commenting. We will use this categories for our classification using the mentioned machine and deep learning techniques and predict the accuracies of each model we are using. At final, we are comparing the accuracies of each model we are using that which can be used further for the detection of the rumor.


Keywords: Rumor Classification, Supporting, Deny, Query, Commenting, Machine Learning, Deep Learning, Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Neural Network.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE CONFIGURATION:

Processor                                - I3/Intel Processor

RAM                                         - 4GB (min)

Hard Disk                                - 128 GB

Key Board                               - Standard Windows Keyboard

Mouse                                     - Two or Three Button Mouse

Monitor                                   - LCD

               

SOFTWARE CONFIGURATION:

Operating System                            :   Windows 7+                  

Server side Script                             :   Python 3.6+

IDE                                                      :   PyCharm IDE

Libraries Used                                  :   Pandas, Numpy, scikit-learn, Matplotlib, tensorflow.

Learning Outcomes

  • About Python.
  • About PyCharm.
  • About Pandas.
  • About Numpy.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

Demo Video

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