# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
###### Abstract
By empowering platforms and consumers to make informed and educated choices regarding the reliability of news sources, this technology helps to strengthen the fight against the spread of false information. Those subjects have attracted the interest of many researchers that have conducted multiple investigations, such as, but not limited to, combating fake news with transformers [14], providing an automated classification of fake news spreaders for the purpose of breaking the misinformation chain [15], detecting fake news through the use of machine learning and deep learning [16], employing automatic fake news detection in the case of online news [17], using a feature-centric classification approach that integrates both ensemble learning and deep learning methods for fake news detection [13], and predicting the evolution of news spread [18] or broader subjects related to discussing the trends and challenges in identifying fake news on social networks based on natural language processing [19]. The COVID-19 outbreak represents just one recent example of an event that generated a wave of fake news [20]. Not only has the COVID-19 pandemic caused tensions to rise, but, additionally, there has also been a shocking explosion in false information and fake news, which has had considerable consequences on how the public reacts to the virus. Conspiracy theories questioning the virus's origin and the efficacy of vaccinations, as well as risky advice and unconfirmed scientific treatments for the illness, have all been included in COVID-19 fake news [21]. The misunderstanding brought on by this fake news has made it difficult for people to communicate clearly and take preventative action, and among the negative effects are poor confidence in trustworthy sources of information, unwillingness to be vaccinated, disapproval of public health initiatives, and, in certain situations, unjustified fear. This subject was debated and analyzed by multiple researchers, who have tried to address the fake news phenomenon in the case of COVID-19 pandemic from multiple perspectives, such as, but not limited to, using the pre-bunking (psychological inoculation theory) as an efficient solution for increasing the resistance in large populations to fake news [22], proposing a fake news detector in the case of COVID-19 by mixing named entity recognition and stance classification [23], discussing the predictors of fake news sharingin the case of social media users [24], and identifying the trends in fake news in times of COVID-19 [25]. Given that fake news phenomenon poses a tangible threat in today's interconnected society, education and the cultivation of professional journalism have emerged as pivotal tools in the ongoing fight against it. Understanding this phenomenon, identifying its origins, and implementing effective countermeasures are crucial steps to safeguard truth, democracy, and information integrity in our contemporary era. As can be deduced from the intriguing title of the article, the purpose of the work conducted in this paper is to bring to the fore a bibliometric perspective in the cutting-edge area of machine learning techniques in fake news research, including comprehensive analysis in terms of authors, sources, words, countries, universities, and many more, in order to discover trends, insights, themes, and opportunities, and develop strategies for combating the spread of fake news in our age of information. This study sheds light on the key contributions existing in the current scientific literature, offering crucial details in this area by analyzing numerical values, important indicators, tables, graphs, and visual representations based on the extracted papers. The contribution of this work is truly important for the scientific community, since it offers an objective point of view of the current literature, regarding popular sources and highly cited articles, the leading contributors, and the most prolific authors, affiliations, etc.