# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 5 Limitations  
The initial point that has to be addressed in this section is the fact that the articles were selected from only one source, namely the WoS database. Despite the fact that WoS seems to be one of the most popular databases used in similar researches, covering a wide range of disciplines and journals and being highly recognized by the research community [28; 29; 30; 33], it should be stated that papers indexed in other databases have been excluded from the current research. Thus, considering other databases for the papers' extraction while using the same keywords and extraction steps, this might have been conducted with a slightly different dataset.  
Another essential constraint pertaining to the present research is the selection and utilization of keywords throughout the literature search process. While these keywords were carefully chosen, the inherent issue in this process is the dynamic nature of language and the constantly changing terminology within the area. The exclusion of pertinent research may have resulted from linguistic limitations and the possibility that subtle notions may be represented using other terminology. Moreover, differences in language usage throughout fields or geographical areas may often unintentionally leave out important information.  
Additionally, since the research takes language into account, excluding non-English publications might neglect important viewpoints and limit the overall breadth of the present analysis, even if, as Table 2 shows, the number of articles dropped by just three after applying this criterion.  
This study's exclusive focus on papers marked as \"articles\" is another drawback. Though intended, this precision might lead to the exclusion of important details from other kinds of papers, such as books, book chapters, book reviews, or reviews in general. The study findings may be impacted by this choice, as depicted in Table 2.  
Furthermore, there is a restriction in the chosen time frame. As the papers written in 2023 have been excluded from the research due to the fact that at the time the dataset was extracted, the 2023 year was not completed yet, the results only refer to the entire timeframe until 2023.  
All those predefined filters were carefully picked so as to obtain the final collection of articles, without duplicates or irrelevant papers for the current analysis, focusing both on quality and efficiency, making sure that the extracted dataset will lead to relevant and accurate insights for the research objectives.  
Although, the use of a filtered dataset includes some drawbacks that must be stated transparently and objectively. By applying specific rules for extracting the collection of data, in terms of databases, keywords, languages, document types, and time frames, may lead to a limited dataset, some useful works can be omitted or excluded from the analysis, and the results may be slightly affected, since the overall picture of the existing scientific literature may possibly be incomplete.  
Regarding the future work directives, interested researchers can try to conduct deeper bibliometric analyses in the area of machine leading techniques in fake news research, using a lager dataset, without applying pre-defined filters during the extraction step (e.g., extending the time frame, using multiple databases).  
All the insights presented in this article represent valuable information that can be further used in educational initiatives, ethical implications, collaborations, political and economic decisions, advancements in technology, development, or the improvement of the automated tools for detecting fake news, and many more.