## 3 Results
### Second Step: Multiple Linear Regressions  
Two multiple linear regression were performed, one for the dependent variable Retweeted Pubs. and another for the dependent variable Favorite Pubs. The results obtained from these analyses allowed the identification of the variables that increased content recognition in the form of retweets and favorites within the respective models. To examine the explanatory power of each independent variable, the items of the categories (a), (b), (c), (d), and (e) were introduced in their respective model as individual indicators. The researchers applied here the stepwise method for incorporating the variables. In the first regression, the one performed for the Retweeted Pubs., the item \"Links\" (\(\beta\) = 0.560, \(p\)-value \(<\) 0.0001), was added in the first step of the procedure. The variable \"Hashtags\" (\(\beta\) = 0.455, \(p\)-value \(<\) 0.005) was introduced in the second step. Finally, the item \"Gender equality\" (\(\beta\) = 0.447, \(p\)-value \(<\) 0.0001) was added in the third step. The model for this first dependent variable (Retweeted Pubs.) was significant as a whole (F = 78.341, \(p\)-value \(<\) 0.0001), optimally explaining the variance of the dependent variable with values of R = 0.976 and R\({}^{2}\) = 0.951. Therefore, this first regression showed the impact of the variables \"Links\", \"Hashtags\", and \"Gender equality\" when predicting the recognition of content published through retweets (see Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication Topic** & \begin{tabular}{c} **Number of** \\ **Tweets** \\ \end{tabular} & \begin{tabular}{c} **\% of Total** \\ **Tweets** \\ \end{tabular} & \
\begin{tabular}{c} **Average Number of** \\ **Retweets Obtained Per Pub.** \\ \end{tabular} &
\begin{tabular}{c} **Average Number of** \\ **Favorites Obtained Per Pub.** \\ \end{tabular} \\ \hline General news & 2458 & 11.29\% & 18.87 & 9.21 \\ Scholarships & 429 & 1.97\% & 11.21 & 11.16 \\ Science and technology & 1960 & 9.00\% & 13.68 & 8.37 \\ Contests & 267 & 1.23\% & 9.06 & 6.41 \\ Culture and exhibitions & 1437 & 6.60\% & 10.99 & 6.23 \\ Sports & 814 & 3.74\% & 11.87 & 5.01 \\ Entrepreneurship & 308 & 1.41\% & 7.97 & 5.25 \\ Complementary training & 1228 & 5.64\% & 8.01 & 4.18 \\ Gender equality & 700 & 3.22\% & 50.98 & 26.14 \\ Institutional information & 4382 & 20.13\% & 13.87 & 7.21 \\ Employability & 685 & 3.15\% & 8.12 & 4.18 \\ Research & 1796 & 8.25\% & 13.08 & 7.38 \\ Seminars and conferences & 1968 & 9.04\% & 7.98 & 4.07 \\ Awards and recognitions & 1907 & 8.76\% & 11.94 & 3.98 \\ Health and green environment & 1177 & 5.41\% & 18.54 & 11.99 \\ Volunteering & 255 & 1.17\% & 13.87 & 5.29 \\ \hline Total & 21,771 & 100.00\% & 45.84 & 24.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Retweets and favorites obtained by publication topic. For the second regression, the one developed for the variable Favorite Pubs., the item \"Original Tweets\" (\(\beta\) = 0.198, \(p\)-value \(<\) 0.005) appeared in the first step of the process. The variable added in the second step was called \"Pub. 8:00 to 10:00\" (\(\beta\) = 0.237, \(p\)-value \(<\) 0.005). To finish, the item \"Gender equality\" (\(\beta\) = 0.531, \(p\)-value \(<\) 0.005) appeared in the third step.