Distinguishing Fake and Real News of Twitter Data with the help of Machine Learning Techniques
Bibliographic record
Abstract
News articles have an influence on people's belief and views about various circumstances. In this regard, some news publishers with political or ideological bias try to spread news which are distorted or totally wrong. Natural language processing was used to preprocess the text. Some general features like, number of words, sentences, stopwords, non-alphabetic words, verbs, nouns, and adjectives were identified. Word positioning was labeled to distinguish a word as a noun, a pronoun, an adjective or a verb in the sentences. Preprocessing was followed by feature extraction methods namely, count vectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and word2vec embedding. It was observed that the results obtained by TF-IDF feature extraction method were superior compared with the other two methods. Various machine learning models were used for training the model namely, Naive Bayes, Logistic Regression, Random Forest, K-nearest neighbors (KNN), Support Vector Machine (SVM) and Recurrent Neural Network (RNN) as a deep learning model. The models were successfully tested on two datasets. On the first dataset, SVM achieved an accuracy of 98.5% and RNN achieved an accuracy of 98.03% which is much improvement over the best results of Agarwalla et al., 2019 (83.16 % accuracy). On the second dataset, SVM achieved an accuracy of 97.76%, RNN achieved 97.1% and Logistic Regression achieved 97.50% which is an improvement over the best results of Vijayraghavan et al. 2020 (94.88% accuracy).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".