The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

https://www.mdpi.com/2078-2489/12/1/38/pdf

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

FAKE news has proliferated to a big crowd than before in this digital era, the main factor derives from the rise of social media and direct messaging platform. Techniques of fake news stories detection ingenious, varied, and exciting. This study aims to apply natural language processing (NLP) techniques for text analytics and train deep learning models for detecting fake news based on news title or news content. Solution proposed in this study aims to be applied in real-world social media and eliminate the bad experience for user to receive misleading stories that come from non-reputable source. For NLP techniques, text preprocessing such as regular expression, tokenization, lemmatization and stop words removal are used before vectorizing them into N-gram vectors or sequence vectors using terms frequency inverse document frequency (TF-IDF) or one-hot encoding respectively. Then, TensorFlow is chosen as the framework to be used with built in Keras deep learning libraries that is having a large community and number of commits on Tensorflow GitHub repository that can be enough to build deep learning neural network models. Results from the models are showing that models trained with news content can achieve better performance with computation time being sacrificed while models trained with news title require less computation time to achieve good performance. Also, overall performance of models fed with N-gram vectors are slightly better than models fed with sequence vectors.

Fake News Detection using Deep Learning

Today, the world is under siege from various kinds of fake news ranging from politics to COVID-19. Thus, many scholars have been researching automatic fake news detection based on artificial intelligence and machine learning (AI/ML) to prevent the spread of fake news. The mainstream research on detecting fake news so far has been text-based detection approaches, but they have inherent limitations such as the difficulty of short text processing and language dependency. Thus, as an alternative to the text-based approach, the context-based approach is emerging. The most common context-based approach the use of distributors’ network information in social media. However, such information is difficult to obtain, and only propagation within a single social media can be traced. Under this background, we propose the use of composition pattern of web links containing news content as a new source of information for fake news detection. To properly vectorize the composition pattern of web links, this study proposes a novel embedding technique, which is called link2vec, an extension of word2vec. To test the effectiveness and language independency of our link2vec-based model, we applied it to two real-world fake news datasets in different languages (English and Korean). As comparison models, we adopted the conventional text-based model and a hybrid model that combined text and whitelist-based link information proposed by a prior study. Results revealed that in the datasets in two languages, the link2vec-based detection models outperformed all the comparison models with statistical significance. Our research is expected to contribute to suggesting a completely new path for effective fake news detection.

A Link2vec-based Fake News Detection Model using Web Search Results

At the present world, one of the main sources of the news is an online platform like different websites and social media i.e. Facebook, Twitter, Linkedin, Youtube, Instagram and so on. However, due to the lack of proper knowledge or deliberate activity of some cunning people, fake news is spreading more than ever. People in general, struggling to filter which news to trust and which one to discard. Even the sly people take advantage of the situation by spreading false news and misleading the people. Natural Language Processing, one of the major branch of Machine Learning, the wealth of research is remarkable. However, new challenges underpinning this development. Here in this work, Naive Bayes Classifier, a Bayesian approach of Machine Learning algorithm has applied to identify the fake news. We showed, besides the algorithms, how the wealth of corpora can assist to improve the performance. The dataset collected from an open-source, has been used to classify whether the news is authenticated or not. Initially, we achieved classification accuracy about 87% which is higher than previously reported accuracy and then 92% by the same Naive Bayes Algorithm with enriched corpora.

https://researchlakejournals.com/index.php/AAIML/article/download/45/32

Effect of Corpora on Classification of Fake News using Naive Bayes Classifier

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.

https://www.revistageintec.net/index.php/revista/article/download/1738/1119

Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm

Fake news consists of news that is not well researched or deliberate steps have been taken to spread misinformation or hoaxes via different forms of news distribution networks. This paper aims to tackle this issue using a computational model of probabilistic and geometric machine learning models. Moreover, the scores of two different vectorizers namely count and Term Frequency Inverse Document Format(TF-IDF) will be compared to find the appropriate vectorizer for fake news detection. English stop words have been used to improve the scores. Various classifiers like Naive Bayes, Support Vector Machine(SVM), Logistic regression and decision tree classifier were used to predict the fake news. Simulation results indicate Support Vector Machine (SVM) with the TF-IDF gave the most accurate prediction.

Geraldine Bessie Amali D

Papers

Comparison of Various Machine Learning Models for Accurate Detection of Fake News