# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 3 Dataset Analysis
### Literature Analysis
#### 3.4.2 The Top 10 Most Cited Papers--A Review
However, this paper is truly significant, since it addresses a cutting-edge current subject, and it provides valuable information, but the main weakness in this domain is represented by the fact that few public databases exist for certain forms of modification, such as expression swapping, and in order to further the discipline and investigate deeper, scientists are encouraged to provide more realistic datasets [47]. The academic effort on fake news identification mentioned in the article that belongs to [PERSON] et al. [48] covers the development and evaluation of a model entitled FakeBERT. In this paper, experiments are conducted using pre-trained word embedding techniques (BERT and GloVe) along with the suggested model (FakeBERT) that combines deep learning models (CNN and LSTM). The study distinguishes the performance of these algorithms to define criteria and evaluates their effectiveness using a real-world fake news dataset related to the 2016 U.S. general presidential election. The major objective is to prove that FakeBERT outperforms existing models in terms of accuracy, false positive rate (FPR), false negative rate (FNR), and cross-entropy loss, leading to innovative conclusions. The paper continues to discuss the importance of bi-directional pre-trained word embedding (BERT) for faster training and greater efficiency in fake news classification, and as a result of the investigation, it was highlighted that with an accuracy of 98.90%, the proposed model, FakeBERT, surpasses existing benchmarks. The article provides noteworthy results useful for fake news detection, and is an original work very well conducted by the researchers, its main strength being that it presents to the readers an innovative model, based on the popular BERT, and compares it with other deep learning models using relevant indicators. Anyway, there is still place for improvement and potential research in the future directives, such as in the development of hybrid methodologies for multi-class datasets and the study of echo-chambers in social media for enhanced comprehension of the dissemination of false news [48]. In the next listed article, a combination of a long-term memory (LSTM) deep learning algorithm and several traditional machine learning techniques, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, and Naive Bayes, is used by [PERSON] et al. [49] to identify fake news on Facebook through an innovative approach that examines user-generated content and news-related features. The vast dataset includes 42,256,893 posts, 15,328 news stories, and information from over 5000 Facebook profiles, and by considering a wide range of user characteristics and news articles, the primary objective of this research is to increase the accuracy of Facebook's fake news detection system. The outcomes obtained from the study demonstrate that the proposed approach, particularly the LSTM deep learning algorithm, outperforms traditional machine learning algorithms with a remarkable accuracy of 99.4%. Furthermore, the study's ingenious implementation as a Chrome extension for real-time detection highlights the practical utility of the research in fighting erroneous information in online environments, especially on social media. This paper is also considered truly relevant for the academic community according to the number of citations; it provides interesting details, many images, and examples explained in an objective and original manner. Although, there are still some aspects that should be addressed in future work, such as ways to boost the chrome extension's performance, or analyzing the decision making using other deep learning algorithms [49]. The article written by [PERSON] et al. [50] includes two main datasets, namely Liar, with short, labeled claims, classified as either true or false, concerning a variety of news reports, and ISOT, comprised of 21,417 true news pieces and 23,481 fake news pieces.