## 2 Materials and Methods
### Dataset Extraction  
Since the main purpose of bibliometric analysis is to focus on the development of a particular field, the extraction of the dataset is truly important, and the authors should pay high attention to this when performing the process of selection. The research tool harnessed in the present manuscript for extracting the collection of papers is the Web of Science database [16], also referred to as WoS. This instrument has a pivotal role for bibliometric analysis, facilitating access to a wide variety of academic publications across a multitude of domains. The decision to exclusively use the WoS database is supported by multiple arguments, including the ones exemplified below:  
* It has a preeminent and influential position within the academic community compared to other databases (IEEE, Scopus). * It grants users personalized access to sources, following a subscription-based approach (in the case of this article, the researchers had full access--all ten indexes). The papers by [PERSON] [17] and [PERSON] [18] addressed the importance of having full access to sources when performing bibliometric analysis. * It is up to date and includes a broad spectrum of scientific articles from multiple domains and journals, being highly recognized by academics. The articles written by [PERSON] et al. [19], [PERSON] et al. [20], and [PERSON] et al. [21] provide more insight into this matter. * It offers users the possibility to import data extracted from WoS in row format into the popular R tool (4.3.2), Biblioshiny (3.2.1). * In most of the academic papers employing bibliometric analysis [22; 23; 24; 25; 26; 27], published over the years on different subjects, researchers extracted data from the WoS database. This serves as proof for considering WoS as the main database for the data collection step in our analysis. * This bibliometric analysis includes individual analyses, such as the most cited articles, which is something that would have generated issues when shaping the list of the most cited articles according to the number of citations if we used several databases. However, we are aware of the limitations and impact of our decision on the final dataset, and we agree that if we had used multiple databases in our analysis, the results might have been slightly different. All the exploratory steps and filters applied on the WoS database are explained in detail in Table 1, along with the queries and count for each. As can be observed, the first data selection step involved three distinct queries that were used to search for specific keywords in either the titles, abstracts, or authors' keywords. The primary goal of the first query was to extract the documents that contained the \"social media\" keyword, which generated a set of 113,396 papers. This ensured that the extracted corpus was directly related to social media and addressed topics, issues, and challenges specific to this field, being relevant to the goal of the article. The next query had the purpose of delving further into the subject of \"natural language processing\", and this term was searched for in titles, abstracts, and keywords. The result revealed that 39,766 papers had been written around this trending subject, demonstrating its influence and relevance within the scientific community. The last step was to combine the findings garnered in the aforementioned queries, certifying that the extracted set included only pertinent papers that address the social media domain within the context of natural language processing. This merging process generated a unique dataset composed of 3144 documents. The second exploratory step focused on excluding papers written in languages other than English, since English is considered a universal language within the scientific community. This serves as proof of the widespread understanding of English among researchers, leading to the majority of studies being written exclusively in this language.