## Materials and Methods
### Data Cleaning and Organization  
The compiled data set was stored for cleaning and organization, extracting a total of thirty-nine variables arranged into six categories: (a) Publication volumes, (b) Publication components, (c) Publication day of the week, (d) Publication time slot, (e) Publication topic, and (f) Recognition obtained by the publication (see Table 2). These six categories, and the variables contained in them, were determined in accordance with previous research.  
Publications volumes were defined considering the proposal of [PERSON] and [PERSON] [36]. Publication components were operationalized through the adaptation of post characteristics from [PERSON] et al. [37]. Publication moment, covering the categories of publication day and publication time slot was based on the analysis of [PERSON] et al. [38] in their study on associations between the moment of publication in social media and the engagement concept. Publication topics were addressed in accordance with the proposal of [PERSON] [39] in her study on communication management in social networks services. And finally, the category that represented the recognition obtained by the publication was determined following the recommendations of authors such as [PERSON] [9] or [PERSON] and [PERSON] [28], among others.  
The independent variables were clearly separated and differentiated from each other. For instance, a publication could be \"Original Tweet\", \"Retweet\", or \"Reply\", but never \"Original Tweet\" and \"Reply\" at the same time. Similarly, a message with a unique publication ID can only be posted on a specific day of the week. In the same way, a publication can not be categorized in two time slots at the same time.  
Nevertheless, there could be potential correlations between variables placed in different categories. Thus, for example, publication days or publication time slots could be correlated with the topics of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **No. of Variables** & **Name of the Variables** \\ \hline (a) Publication volumes & 3 & Original Tweets, Retweets, and Replies \\ (b) Publication components & 3 & Links, Mentions, and Hashtags \\ (c) Publication day of the week & 7 & Pub. On Mon., Pub. On Tue., Pub. On Wed., Pub. On Thu, \\  & & Pub. On Fri., Pub. On Sat, Pub. On Sun. \\  & & Pub. 8.00 to 1000, Pub. 11:00 to 13:00, Pub. 14:00 to 16:00, \\ (d) Publication time slot & 8 & Pub. 17:00 to 19:00, Pub. 20:00 to 22:00, Pub. 23:00 to 1:00, \\  & & Pub. 20.00 to 4:00, Pub. 5:00 to 7:00 \\ (e) Publication topic & 16 & Central topic discussed in the pub. \\ (f) Recognition obtained by the publication & 2 & Retweeted Pubs. and Favorite Pubs. \\ \hline Total & 39 & \\ \hline \hline \end{tabular}  
* The variables gathered in categories (a), (b), (c), (d), and (e) were taken as independent variables, whereas the variables in category (f) were used as dependent variables.  
\end{table}
Table 2: Variables extracted from the data set.  
publication. This could lead us to think that the variables in the publication topics' category could also be considered as independent variables. However, this work, in line with previous studies on the efficiency of social media management in organizations, used as independent variables those commonly taken by the research community when evaluating the recognition of publications [9; 16; 28]. That is, the variables contained in the category (f), Retweeted Pubs. and Favorite Pubs.