## Materials and Methods
### Data Analysis
#### 2.3.1 First Step: Machine Learning Algorithms  
The authors applied machine learning algorithms to classify the publication topics (Category e). These publication topics would be used as independent variables in the multiple linear regression carried out in the second step of the data analysis.  
In the field of social network services, machine learning algorithms are used to conduct categorizations or classifications of text publications [40]. These systems allow organizations to classify thousands or millions of pieces of text efficiently, and comfortably, for later exploration.  
The textual information analyzed using machine learning algorithms is classified as unstructured information. These data do not adhere to a previously defined scheme; therefore, their processing requires the application of certain rules (idiomatic, grammatical, and semantic) to extract the information they contain.  
Specifically for the platform under study, the methodologies based on Twitter Analytics approaches addressed by [PERSON] et al. [41], [PERSON] et al. [42] or [PERSON] and [PERSON] [43] generally use machine learning algorithms, either to analyze the sentiment of publications or to study specific hashtags. Examples of works focused on the analysis of the sentiment of publications (positive, negative, and neutral) are the studies by [PERSON] et al. [44] or [PERSON] et al. [32]. Whereas, examples of investigations focused on the observation of specific hashtags are the works of [PERSON] and [PERSON] [45] or [PERSON] et al. [46].  
With respect to the techniques used in these methods, the following stood out: decision trees (DT), random forest (RF), Naive Bayes classifier (NBC), logistic regression (LR), k-nearest neighbors (kNN) and support vector machines (SVM) [47; 48; 49]. However, whereas many of these techniques can be effective in determining the sentiment of publications or for hashtag examinations, the most appropriate technique for the classification of complex publication topics, and the one that offers the highest accuracy, is the SVM technique [14].  
The SVM technique applied in the present study used, specifically, the linear Kernel function as a classification method. This general Kernel function is defined as follows:  
\[\text{K}\left(\text{x}_{i},\text{x}_{j}\right)=\Phi\left(\text{x}_{i}\right) \text{-}\Phi\left(\text{x}_{j}\right) \tag{1}\]  
where K (x\(i\), x\(j\)) is the core function, and \(\Phi\left(\text{x}i\right)\) represents the mapping space associated with the vectors.  
The machine learning algorithm used for the classification of publication topics (Category e) was a supervised machine learning algorithm. With supervised machine learning algorithms, there exists an initial set of already labeled data with input-output pairs that allows for training of the predictive model. From this initial data set, the algorithm learns to assign the appropriate output label to each incoming element in the model [50]. In the case of the specific application of these algorithms in the classification of texts in social media, the labeled data set is typically created, on a small scale, via the intervention of a subject or group of subjects who assign each publication the most appropriate label in each case.  
In line with previous research, the classification was performed through the text classification API of the MonkeyLearn library [51; 52]. This text classification API uses the JASON notation protocol in JavaScript, also allowing the researcher to carry out, before classification, a training process with the algorithm.  
After carrying out this training process, manually categorizing 300 publications, the algorithm had the necessary knowledge to develop a personalized machine learning model. This model allowed the classification of the texts of each of the 21,771 publications into one publication topic.