###### Abstract
As a result, this field has attracted a large number of researchers who have included these subjects in their publications. The outcomes of these research investigations can be extremely helpful for developing strategies, comprehending current problems in society, increasing the general level of happiness, or even combating the spread of fake news and disinformation. In this context, [PERSON] et al. [12] underline the use of natural language processing for sentiment analysis in social media marketing, while [PERSON] et al. [13] explore the role of emotions in social media detecting. Furthermore, the work of [PERSON] and [PERSON] [14] discusses the public perception of the COVID-19 pandemic on Twitter by addressing issues related to sentiment analysis and topic modeling. Thus, this current study aims to bring the area of natural language processing in social media research into focus by choosing a relevant set of articles from this field; presenting the materials and methods used in detail; and then performing a comprehensive analysis of various perspectives, including sources, authors, an examination of the existing literature, and a summary of the first 10 most cited articles, as well as an investigation of words, universities, and countries, by employing a multitude of graphs, indicators, and visual representations. Considering the purpose of our paper, bibliometric analysis was chosen as the investigation technique. As [PERSON] and [PERSON] [15] noted in their research, contrary to a review analysis whose scope is to provide a summary of a particular research field in terms of content, with an accent on the most important findings, bibliometric analysis focuses more on the structure of a particular field, making an emphasis on its development. Furthermore, as we think that it is important to also have a view of NLP in social media research, we decided to enhance the analysis by providing a review of the top 10 most cited papers. The choice for this option is grounded on the idea that the papers with the highest number of citations have represented an important base for other studies in the field, succeeding in reaching a broader audience compared to the other papers included in the dataset. By proceeding in this manner, the advantages of a review analysis have been combined with the advantages of a bibliometric analysis, providing a more comprehensive view of NLP use in social media research. As noted, the main purpose of this work is to present the insights gained from the analysis and to discover new trends, hidden information, and results that can lay the foundations for future political, economic, medical strategies, etc. This manuscript also aims to answer questions such as those listed below:  
* Q1. Which are the most cited articles in the area of natural language processing in social media research, and what insights can be uncovered? * Q2. Who are the most prolific authors in the studied domain, and what is their country of origin? * Q3. What valuable information can be brought to the fore, regarding the collaboration of authors in the area of natural language processing in social media research? * Q4. How can the authors' production over time be characterized? * Q5. What facets come to the forefront through the performed analysis of words? In view of the preceding statements, this paper is organized in six sections, as follows: Introduction (Section 1)--an overview of the background information and the topics addressed; Materials and Methods (Section 2)--a presentation of the tools employed and the process of selecting and filtering the dataset; Dataset Analysis (Section 3)--a detailed analysis of the dataset; Discussions (Section 4)--a presentation of the most important insights found following the analysis; Limitations (Section 5)--drawing attention to limitations and boundaries; Conclusions (Section 6)--a presentation of the findings along with suggestions for future work.