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fakenews_DBDA_Mar23.pptx

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deepmitra8

MY CDAC PROJECT

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fakenews_DBDA_Mar23.pptx
1. Yash Changare
(230370625002)
2. Sagar Malankar
(230370625006)
3. Arkaprabha
Majumdar
(230370625004)
4. Snehangsu Mitra
(230350125079)
Contents
• Objective
• Data Collection manual
• Web scrapping
• Data cleaning
• Features Extraction
• Methodology
• Implementation
• Result and Discussion
• Demonstration
• Conclusion and Future Scope
• Project Team Contribution
fakenews_DBDA_Mar23.pptx
Fake news can have serious consequences on
society, including:
• Misinforming and misleading people
• Undermining trust in institutions and
experts
• Fueling prejudice and hate speech
• Influencing elections and political
outcomes
• Causing panic and harm in emergency
situations
• Damaging reputations and careers
• Diverting attention and resources from
real issues
• Contributing to the spread of
misinformation and conspiracy theories
OBJECTIVE
• The objective of this project is to
develop an accurate and efficient
fake news classification system
using NLP and machine learning
techniques to analyze textual
features of news headlines and
content.
• The system aims to promote
media literacy, combat the spread
of misinformation, and provide a
robust solution for detecting fake
news in various contexts
Ad

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fakenews_DBDA_Mar23.pptx

  • 2. 1. Yash Changare (230370625002) 2. Sagar Malankar (230370625006) 3. Arkaprabha Majumdar (230370625004) 4. Snehangsu Mitra (230350125079)
  • 3. Contents • Objective • Data Collection manual • Web scrapping • Data cleaning • Features Extraction • Methodology • Implementation • Result and Discussion • Demonstration • Conclusion and Future Scope • Project Team Contribution
  • 5. Fake news can have serious consequences on society, including: • Misinforming and misleading people • Undermining trust in institutions and experts • Fueling prejudice and hate speech • Influencing elections and political outcomes • Causing panic and harm in emergency situations • Damaging reputations and careers • Diverting attention and resources from real issues • Contributing to the spread of misinformation and conspiracy theories
  • 6. OBJECTIVE • The objective of this project is to develop an accurate and efficient fake news classification system using NLP and machine learning techniques to analyze textual features of news headlines and content. • The system aims to promote media literacy, combat the spread of misinformation, and provide a robust solution for detecting fake news in various contexts
  • 7. Manual Data Collection From Source • Link to PolityFact.com https://www.politifact.com/ • Manual Data Collection: Pick up the data manually and storing it to the Excel file as shown. And then saving the file as CSV dataset.
  • 8. Data Collection • Challenge: Manual data collection for fake news classification is time-consuming and limited by the researcher's ability to collect articles.
  • 12. Data collection by Web scrapping
  • 13. CSV file generated by web scrapping
  • 14. PolitiFact Web Scrapping Module Scrape Function
  • 17. Count Vectorizer is used to convert a collection of text documents to a vector of term/token counts. It also enables the ​pre-processing of text data prior to generating the vector representation. This functionality makes it a highly flexible feature representation module for text. Document 1 : The car is driven on the road. Document 2: The truck is driven on the highway. Sentence Embedding (Sen2Vec) using LaBSE:
  • 20. PROGRAMMING LANGUAGE Library Purpose Reference NLTK Interfacing with Stanford NLP tools https://www.nltk.org/ Scikit Learn Machine Learning http://scikit-learn.org/ Pandas Data Frames Creation https://pandas.pydata.org/ NumPY Array Creation http://www.numpy.org/ Seaborn Visualization Tool https://seaborn.pydata.org/ Joblib Provides lightweight pipeline https://joblib.readthedocs.io/en/latest/
  • 21. Other Tools Stanford NLP tools through Python interfaces. Hardware Intel Core i5, 4GB RAM OS Ubuntu 16.4 / Windows 7 or later
  • 22. 1. Importing Libraries At first, we have imported all the required libraries 2. Data Pre-Processing: 3. Model Building and Tuning 4. Model Building and Tuning
  • 24. Precision = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 Recall = 𝑇𝑃 𝑇𝑃+𝐹𝑁 Specificity = 𝑇𝑁 𝐹𝑃+𝑇𝑁 Actual positive from total predictive positive True positive from total positive True Negative Rate Confusion Matrix F−measure = 2∗𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗𝑅𝑒𝑐𝑎𝑙𝑙 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙 Harmonic Mean of Precision and Recall Accuracy = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 Evaluation Metrics
  • 25. Accuracy Classifiers Count - Vectorizer TF-IDF Sent2Vec Count - Vectorizer + TF-IDF 1. Gradient Boosting Classifier 0.8084 0.8052 0.7955 0.8102 2. Random Forest Classifier 0.7677 0.7893 0.7834 0.7845 3. Logistic Regression 0.8064 0.8149 0.8109 0.8183 4. Naïve bayes 0.4748 0.5624 0.5906 0.4770 5. neural network MLP Classifier 0.7556 0.7508 0.7643 0.7448 6. Passive Aggressive Classifier 0.7425 0.7417 0.7784 0.7454 7. SVM SVC 0.8090 0.8615 0.8187 0.8121 8. SVM SVC Grid Search CV 0.8090 0.8617 0.8187 0.8648 9. Decision Tree 0.7297 0.7297 0.7008 0.7383 Accuracy
  • 28. Interface to Check news with Headline
  • 29. Interface to Check news with content
  • 30. Conclusion • Fake news classification system with NLP and ML is significant to combat the spread of misinformation on social media. • System detects and flags fake news posts, reducing their spread, and has broad applications in media, politics, and social media platforms. • The system needs further research to improve accuracy and efficiency and stay updated with the latest trends in fake news. • Has great potential to promote a more informed and responsible society
  • 31. Future Scope The fake news classification system has promising future scope- • Multilingual Classification: The system can support multilingual classification. • Social Media Integration: The system can be integrated with social media platforms for real-time detection and classification. • Real-time Detection: The system can be enhanced for real-time detection of fake news. • Transfer Learning and Ensemble Models: Transfer learning and ensemble models can improve system accuracy. • Deep Learning Techniques: The system can be further enhanced using deep learning techniques.
  • 33. Project Team Members Contribution 1. Yash Changare (230370625002) Data Collection, Model Training and Model Evaluation 2. Sagar Malankar (230370625006) Data Collection, Data Exploration and Analysis 3. Arkaprabha Majumdar (230370625004) Data Cleaning and Processing 4. Snehangsu Mitra (230350125079) Result Analysis