## 3 Dataset Analysis
### Dataset Overview  
Table 2 brings key details about the data collection set into the limelight, comprising 1852 articles, disseminated across 697 sources, during a relatively large period of time--specifically, 14 years between 2010 and 2023--highlighting the fact that area of natural language processing in social media research seems to capture the attention of researchers over a considerable timespan and simultaneously proving its significance within the scientific community. The relevance and current importance of the study performed by researchers in this article is further supported by the low value of 2.98, registered as the average years from publication, suggesting that most of the articles written around the studied domain are recent papers. Furthermore, the academic significance of this field is also deduced from the recorded values of 12.76 for average citations per document and 2.467 for average citations per year per document. In terms of references, a substantial number was noted--more precisely, 67,244. Furthermore, in terms of paper type, the following categories have been included in the \"article\" type provided by WoS: _article_ (1715 papers), _article: book chapter_ (19 papers), _article: data paper_ (5 papers), _article: early access_ (102 papers), and _article: proceedings papers_ (11 papers). The annual scientific production evolution is reflected in Figure 3. One can easily notice an evident rising tendency in published articles that address the topic of natural language processing in social media research, experiencing an annual growth rate of 62.18%. Of the total amount of 1852 articles selected in the analysis, only one article was published in 2010. In light of its humble beginnings, there is a slight annual rise in the article count until 2018, when it reached a number of 70 documents. Henceforth, this domain registered astonishing growth, an expansion in the production of articles in this field, attaining a significant number of 537 published papers in 2023, highlighting both the relevance of the field and the enthusiasm and burgeoning interest of scientists. Figure 4 presents a fluctuating pattern of average citations per year, marked by variable numerical values between 0.1 and 8.5, suggesting strong visibility of the articles within the scientific community, covering the years 2010 through 2023. Regarding the evolution of the number of citations per year, it should be mentioned that, as expected, the papers published in the years closely to the end of the year of analysis have a smaller time window in which the papers could be cited, which explains the drop in citations registered for the 2020-2023 period. Therefore, by considering a reduced time window for 2010-2020, in terms of citations, it can be observed that the average citations per document indicator equals 29.57 citations per document--visibly higher than the value for the same indicator provided in Table 2 for the entire dataset (12.76 citations/document). Furthermore, the value of the average citations per year per document is 4.353 for the selected 2010-2020 \begin{table}
\begin{tabular}{l c} \hline \hline
**Indicator** & **Value** \\ \hline Timespan & 2010:2023 \\ Sources & 697 \\ Documents & 1852 \\ Average years from publication & 2.98 \\ Average citations per documents & 12.76 \\ Average citations per year per document & 2.467 \\ References & 67,244 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main information about the data. period, even higher in this case than the value recorded for the same indicator in the case of the entire dataset, namely 2.467 citations/year/document. Moreover, as depicted in Figure 4, a spike in data can be observed for 2015 and 2012 in terms of the evolution of average article citations per year. Considering the papers published in the above-mentioned years, it can be observed that the spike in the annual average article citations for 2015 can be attributed to the paper authored by [PERSON] et al.