## 5 Conclusions  
Although in recent years some works have used machine learning algorithms and multiple linear regressions to examine the activity that has occurred on social media, these studies tend to focus exclusively on content characteristics (publication volumes, publication components, and publication moments) [16; 62] or in its message (tone, sentiment, or publication topic) [60; 63]. However, few studies have examined both topics simultaneously.  
Perhaps the most emblematic of these studies is that of [PERSON] and [PERSON] [28]. Their work, like the present one, considered the characteristics of the content (publication volumes, publication components, and publication moments) and the message of the content (publication topics). Nevertheless, their study analyzed a data set smaller than that of the present study and applied a text classification with only three publication topics (information, entertainment, and rewards), as opposed to the sixteen considered here.  
The present research not only combines a study of the characteristics (volumes, components, and moments) and the message (topics) of the publications, it also addresses this challenge more comprehensively than previous works. In the authors' opinion, the examination of the characteristics and the message of the publications, in addition to the two steps analysis approach applied in the investigation, are among the key values of the current study. The supervised machine learning algorithm applied in the first term allowed the classification of the texts into the publication topics. Knowing publication topics besides publication volumes, publication components, and publication moments, the authors applied multiple linear regressions to discover the influence of all these variables on the recognition of content.  
The results gathered to answer the first research question (RQI) are in line with the findings of previous studies, whereas the results obtained for the second question (RQII) provide novel and relevant information on the current field of investigation.  
To this second point, regarding the publication topic, the findings confirmed that publications on topics of gender equality achieve much higher recognition than those obtained by content focused on other topics. In the opinion of the authors, this situation is conditioned by the recent social sensitivity around this issue. Likewise, given that no organization goes uninfluenced by social issues of this nature, the knowledge derived from these results in the context of university organizations is likely to be extended to the field of business organizations.  
On the other hand, this finding can prompt reflections on the importance of media professionals' managing these technologies adequately and being able to identify, at all times, the topics of interest to their audience, adapting the content of their organizations to these preferences.  
In view of all the above, the authors confirm the value of applying machine learning algorithms and multiple linear regressions to carry out an in-depth analysis of the enormous amount of information generated by social network services gaining new knowledge about trends and usage patterns in the media. This renewal will ultimately be the basis for the efficient management of social network services in the organizational field.