Dataset: Politifact/Liar-Liar dataset (https://www.politifact.com)
Description of the train TSV file format:
- Column 1: the ID of the statement ([ID].json)
- Column 2: the label.
- Column 3: the statement.
- Column 4: the subject(s).
- Column 5: the speaker.
- Column 6: the speaker's job title.
- Column 7: the state info.
- Column 8: the party affiliation.
- Column 9-13: the total credit history count, including the current statement.
- 9: barely true counts.
- 10: false counts.
- 11: half true counts.
- 12: mostly true counts.
- 13: pants on fire counts.
- Column 14: the context (venue / location of the speech or statement).
- Political Affiliation: How much any political news is inclined towards a respective party
- Sentiment Analysis: Sentiments of people can result in biasing the news
- Sensationalism: Extreme sensationalism is an indication of fakeness. This derieved on the basis of Setiments
- Stance detection: Many versions of the same news are published in several traditional and new media. The language of these articles vary from supporting or denying a claim
- Spam Detection: Spam or Ham are untrue articles/texts made viral in order to deceive masses
- Spelling Error: More spelling errors mean news from unreliable sources
Fakeness = 0.9 * (Sensationalism) + 0.85 * (Political Affiliation) + 0.65 * (Spam Detection) + 0.75 * (Stance Detection) + 0.8 * (Spelling Error)
| Names | Contributions |
|---|---|
| Vidhi Sharma | Sentiment Analysis, Sensationalism |
| Himangini Agrawal | Spam Detection, Stance Detection (Complex feature - distilling with domain ranking) |
| Chidananda Pati | Spelling Error, Political Affiliation |
Techniques and algorithms used:
- Data Enrichment:
- SMS Spam Collection Dataset from Kaggle
- fnc-1 dataset from fakenewschallenge.org
- UCI-News-Aggregator from Kaggle
- Techniques: Topic Modelling, Spam Detection, Stance Detection
- Algorithms: Bag of Words, TF-IDF, LDA, SVM, Doc2Vec, Cosine Similarity