# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 2 Materials and Methods
### Dataset Extraction
This small difference of only 3 articles being excluded from the analysis after applying the language restriction suggests the fact that the vast majority of researchers opt for writing articles in this area in English, being the most well-known language among the scientific community. The document types occupying the center of attention in the fourth exploratory step followed in the selection of the data set. Therefore, the analysis focused on the exclusive selection of works marked as articles, a restriction that led to a significant decrease in the number of documents, namely 510. Here, it shall be noted that the inclusion of a paper into the \"article\" category by WoS is based on the fact that, according to the WoS, the paper provides new and original work ([PERSON] et al., 2016). As a result, in this category, WoS includes research papers, brief communications, technical notes, chronologies, full papers, and case reports that were published in a journal and/or presented at a symposium or conference, as well as proceedings papers ([PERSON] et al., 2016). We have added this note as it is well stated in the scientific literature the importance of the document type when conducting bibliometric analyses ([PERSON] et al., 2016). This exclusion criterion has been used also by [PERSON] and [PERSON] (2017) in their research. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Exploration** & **Filters on Web of** & **Description** & **Query** & **Query** & **Count** \\
**Steps** & **Science** & **Description** & **Number** & **Count** \\ \hline \multirow{8}{*}{\(1\)} & \multirow{8}{*}{Keywords} & Contains specific keywords & ((TI = (“fake\_news”)) OR & \multirow{2}{*}{\#1} & \multirow{2}{*}{5671} \\  & & related to fake news in & AB = (“fake\_news”)) OR & & \\  & & title/abstract/authors & AK = (“fake\_news”) & & \\  & & keywords & ((TI = (“machine\_learning”)) & & \\  & & & title/abstract/authors & (“machine\_learning”)) OR & & \\  & & keywords & AK = (“machine\_learning”) & & \\ \cline{2-6}  & & & Contains specific keywords & ((TI = (“deep\_learning”)) OR & & \\  & & related to machine learning in & AB = (“deep\_learning”)) OR & & \\  & & title/abstract/authors & AK = (“deep\_learning”) & & \\  & & keywords & & & \\ \hline \multirow{8}{*}{\(2\)} & \multirow{8}{*}{Title/Abstract/} & Contains one of the machine & \multirow{2}{*}{\#2 OR \#3} & \multirow{2}{*}{\#4} & \multirow{2}{*}{532,179} \\  & & & specific keywords & & \\ \cline{1-1} \cline{3-4}  & \multirow{2}{*}{Author’s Keywords} & Contains one of the machine & \multirow{2}{*}{\#1 AND \#4} & \multirow{2}{*}{\#5} & \multirow{2}{*}{900} \\  & & learning-specific keywords and & & & \\ \cline{1-1} \cline{3-4}  & & fake news keywords & & & \\ \hline \hline
3 & Language & Limit to English & (\#5) AND LA = (English) & \#6 & 897 \\ \hline
4 & Document Type & Limit to Article & (\#6) AND DT = (Article) & \#7 & 510 \\ \hline \multirow{2}{*}{\(5\)} & \multirow{2}{*}{Year published} & Exclude 2023 & (\#7) NOT PY = (2023) & \#8 & 347 \\ \cline{2-6}  & & Exclude 2024 & (\#8) NOT PY = (2024) & \#9 & 346 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data selection steps. With the goal to conduct the final exploratory stage of the selection process, the years 2023 and 2024 were excluded as a time limitation. By restricting the number of articles evaluated in this study to those published over a 6-year time limit, from 2017 to 2022, this temporal cut guaranteed that our analysis was grounded in a precise time span. As a result, the corpus was subsequently reduced to 346 articles.