// ACADEMIC CONTENT PORTAL · Computer Science
Thesis Topic Ideas: Nepali Fake News Detection Using NLP
🎓 Academic Journey Progression Map
1. Introduction & Problem Statement
Overview: Develop an NLP classifier trained on Devanagari news text datasets to detect sensationalized and fake news articles across Nepali digital media portals.
Background Context (Nepal): The proliferation of unverified news portals and social media clickbait in Nepal highlights the need for automated Devanagari text classification tools.
2. Research Objectives
- ›Curate and label a dataset of 3,000+ authentic and deceptive Nepali news headlines and articles
- ›Implement Devanagari text preprocessing (stop-word removal, stemming, tokenization)
- ›Train TF-IDF + XGBoost and Devanagari BERT (NepBERT) NLP models
- ›Achieve 85%+ F1-score on news veracity classification
- ›Deploy web browser extension or demo portal flagging suspicious articles
3. Proposed Methodology
- Scrape news articles from verified and unverified online news portals in Nepal
- Preprocess Devanagari text using NLTK / Stanza NLP pipeline tuned for Devanagari script
- Extract linguistic features (hedging words, exclamation density, sentiment polarity)
- Train and compare Logistic Regression, Naive Bayes, NepBERT transformer models
- Evaluate precision, recall, confusion matrix, and inference speed
$ Worked Example / Sample Scenario
Sample Scenario: A user pastes a viral news headline into the web portal. The NepBERT model analyzes Devanagari syntactic structure and flags 'Fake News Risk: 92%' due to sensationalized phrasing and missing source attribution.
4. Thesis Chapter-by-Chapter Outline
Chapter 1: IntroductionTU/KU/NEB standard
Background on digital news expansion in Nepal, clickbait problem, and research goals
Chapter 2: Literature ReviewTU/KU/NEB standard
Devanagari text processing, transformer models (NepBERT), and fake news detection literature
Chapter 3: Research MethodologyTU/KU/NEB standard
Dataset annotation rules, feature engineering, and model architecture design
Chapter 4: Experiments & ResultsTU/KU/NEB standard
Model performance comparison, ROC curves, F1-scores, and error analysis
Chapter 5: Conclusion & DeploymentTU/KU/NEB standard
Summary, browser extension demo overview, and future dataset expansion
5. Recommended Tools & Technologies
To implement the practical, technical, or analytical portions of this thesis topic, the following software tools, libraries, or APIs are recommended:
6. Core References & Academic Sources
- [1]NepJOL — Devanagari NLP and Text Classification Research Papers <!-- VERIFY: needs confirmation -->
- [2]IEEE Transactions on Computational Social Systems <!-- VERIFY: needs confirmation -->
7. Frequently Asked Questions (FAQs)
Q: Where can I get a Devanagari news dataset in Nepal?
You can scrape public articles from major news portals (e.g., Ekantipur, OnlineKhabar) and annotate a benchmark set with guidance from your advisor.
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