## 4 Discussion  
In view of everything that has been addressed so far, the main objective of the manuscript was to offer a comprehensive bibliometric evaluation around the topic of natural language processing in social media research. Through the in-depth investigation of multiple perspectives (e.g., references, sources, authors, the existing literature, words, production over time, countries, affiliations), relevant indicators (e.g., collaboration indexes, the h-index), and visual representations (e.g., thematic and collaboration maps, graphs, collaboration networks, co-occurrence networks, three-field plots), the collection of 1852 English articles, published in 2010-2023, was analyzed. After performing this meticulous assessment, valuable insights and hidden trends were revealed. There is no doubt that the first starting point of this discussion should definitely be centered on sources. A source is considered truly relevant and influential when obtaining a top position in consecutive bibliometric analyses, conducted on various topics and subjects. This is strong proof for highlighting the source's significant position in the scientific community, its notable presence in several academic articles, by being frequently cited, and the increased interest of the authors to publish their research in it. As for this investigation, the results exposed that the leadership position is held by _IEEE Access_, a popular journal that seems to occupy a standing among additional top ranks, which has been observed in other bibliometric studies throughout the scientific literature [22; 23; 24; 24; 25; 26; 44; 45; 46; 47; 48]. In terms of affiliations, the University of Pennsylvania occupies the first position based on the number of papers published around the area of natural language processing in social media research. Its relevance within the scientific community, as well as its exceptional productivity, are further supported by its presence in the top for other bibliometric studies, such as the following ones related to social media research [49], machine learning for mental health in social media [50], machine learning used in cancer research [51], innovation research [52], corporate social responsibility [53], or in general scientific rankings made for specific periods, such as the one conducted by [PERSON] and [PERSON] for the 1970-2020 period on the WoS database [54]. Given the countries that are marked as leading contributors in accordance with the number of citations gathered in the area of natural language processing in social media research, one can easily spot that on the top positions are listed as the USA, China, the UK, and India. This is not a surprising outcome, since these regions were also depicted in other bibliometric studies around different subjects, including sentiment analysis, deep learning, health, COVID-19-related topics, and many more. One can refer to the following studies that highlight a similar trend in terms of country contribution as in research by [22; 23; 24; 46; 47]. Coming back to the questions presented in the introduction, in this section, some answers can be outlined, as discussed in the following paragraphs. Regarding the most cited articles, it was observed that the majority of the studies presented experimental results, while two out of ten were focused on analyzing the existingliterature and future work directions in these areas. Apart from this, the methods employed were machine learning techniques, and the data were collected from multiple social network platforms, especially Twitter. The subjects covered varied, including those of the medical domain, rumors, natural disaster crises, traffic incidents, and many more. The number of citations for the top 10 most cited documents fluctuates between 675 and 210. Regarding the most prolific authors, the foremost position is held by [PERSON] with 28 published papers, followed with considerable difference by [PERSON], with 14 articles.