# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 2 Materials and Methods  
The present analysis benefits from the comprehensive access to a vast array of academic literature facilitated by the utilization of the Clarivate Analytics' Web of Science Core Collection, formerly known and referred to as Web of Science (WoS) [26]. This platform served as the primary tool for curating a corpus of papers essential for conducting a rigorous bibliometric examination of machine learning techniques in the context of fake news research. Facilitating collaboration and fostering innovation, the WoS database stands as an indispensable instrument in advancing academic endeavors and enriching the collective knowledge base within scholarly circles. Serving as a pivotal gateway, it functions as a central hub, expediting access to a broad spectrum of scientific publications. The platform streamlines researcher browsing, retrieval, and a dissemination of findings, enhancing efficiency through its expansive coverage across diverse domains. Offering reliable and comprehensive means to stay abreast of current developments, engage in interdisciplinary research, and make substantial contributions to the body of knowledge, this platform emerges as a critical resource for academics. Despite the existence of other popular databases, such as Scopus or IEEE, WoS seems to be holding a prominent position in the scientific community. WoS works on a subscription basis, offering users personalized access. The importance of full access to sources used in bibliometric analysis (as is the case of this article) is addressed in the paper of [PERSON] [27]. Furthermore, as [PERSON] et al. [28] pointed out, the WoS platform provides a higher level of coverage when considering the variety of the disciplines included in this database, while being considered at the same time the most credible by the scientific community, even though it is less inclusive than its counterparts. These aspects are further highlighted in the works of [PERSON] et al. [29] and [PERSON] et al. [30]. Nevertheless, with the possibility of directly importing raw data extracted from WoS into Biblioshing, the R tool used in the present analysis was a key factor in our choice [31]. Given that the WoS database operates on a subscription-based model, scholars such as [PERSON] [32] and [PERSON] [27] underscore the criticality of explicitly delineating, at the outset of a bibliometric analysis, the specific indices to which the individual conducting the database extraction has been granted access. Notably, it has been observed that variations in the obtained dataset can arise due to the level of subscription, thereby emphasizing the need for transparency in disclosing the scope of database access. In the present study, it is imperative to affirm that comprehensive access was secured to all ten indexes provided by the WoS platform. Due to all the above-mentioned features and doubled by the use of the WoS platform in similar bibliometric analyses on different themes and areas, we decided to opt for this database when extracting our dataset [28; 29; 30; 33]. Additionally, we based our decision in using solely the WoS database (and not a combination of two or more databases) on two other aspects. The first one was represented by a search of the bibliometric papers that have used WoS as a primarily database, and it has been observed that from the 28,708 papers featuring \"bibliometric\" as a keyword, a higher number of papers mention the WoS database (namely, 9292 papers) when compared to the number of papers that mention Scopus (namely, 5573 papers)--please consider Table 1 for the query we have used (the search was performed on 24 January 2024). Moreover, if we exclude the \"scopus\" keyword from the title/abstract/authors' keywords of the 9292 papers that mention WoS in the title/abstract/authors' keywords, it can be observed that 7622 papers refer to solely the WoS database.