# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 4 Discussion
Regarding the countries that have been listed as leading contributors, in our case it has been observed that India, China, and the USA are the top contributors. It was observed that these countries are also found in top-contributors lists for other bibliometric studies existing in the academic literature, such as the ones in the areas of opinion mining and sentiment analysis [68], COVID-19 vaccination misinformation [59], social media research in times of COVID-19 [58], social media research in the age of COVID-19 [58], health-related misinformation in social media [69], text mining and maintenance [67], classifications of artificial intelligence using convolutional neural networks [65], sentiment analysis in times of COVID-19 [12], and arrhythmia detection and classification [70]. As a result of these observations, it can be highlighted, once more, the important contribution of these countries to the body of research. Going back to the listed questions in Section 1, based on the insights discovered during the bibliometric analysis conducted in this paper, there can be some answers provided. Besides the aspects that were already presented above, such as sources, affiliations, and countries, there are still other findings that must be highlighted in this discussion section. When talking about the cited articles, the first position is held by the paper belonging to [PERSON], published in _Information Sciences Journal_, in 2019, entitled \"A survey on fake news and rumour detection techniques\" [46]. Regarding the most prolific authors in this area, [PERSON] holds the first position on top, with six published articles, followed by [PERSON], [PERSON], [PERSON], [PERSON], each with five articles. By paying attention to the number of published articles in the analyzed period, there was observed an increased interest in writing papers in the area of machine learning techniques in fake news research starting with the year 2020, a fact that might be associated with the COVID-19 pandemic. In terms of collaborations, one can notice that researchers preferred to conduct studies with multiple authors from across the world, rather than individual work, a fact which outlines that the papers written in the area of machine learning techniques in fake news research includes diverse points of view, perspectives, varied resources, and wider audiences, along with cross-cultural insights. Another question outlined in the introduction was related to insights drawn from the word analysis. By performing a deep investigation of keywords plus, authors' keywords, bigrams, and trigrams, along with providing graphical views of co-occurrence networks and thematic maps, it was observed that the subject of fake news was mostly debated in a COVID-19 pandemic situation, especially based on the social media messages, and the detection of this phenomena was analyzed using multiple machine learning techniques (Support Vector Machine, convolutional neural network, natural language processing. etc.). That being said, all those findings were deeply explained in the above section, along with providing graphs, images, and tables with all the values and indicators considered. Furthermore, 10 out of the extracted articles, based on the number of citations were presented and briefly summarized from multiple perspectives, including methods, purpose, number of authors, and data collected. Based on each review, it was observed that for the detection of fake news there were utilized multiple machine learning techniques (Recurrent Neural Networks, convolutional neural networks, Clustering and Vector Space Models, BERT, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Naive Bayes), the datasets were collected from different places (Twitter, FaceForensics++, DFDC, Celeb-DF, Sina Weibo, Facebook, academic articles, trade journals, newspapers, magazines), and most of the articles addressed the issue and the danger of fake news's spread in in multiple areas, including politics and health.