# Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users
## 4 Results Analysis
### Results for Gender and Age Prediction Using Text from Twitter  
In this section, we discuss the results of the methodology that is described in Section 2.5 for text analysis with the PAN 2015 database DBT. The experimental tests were performed 10 times and we obtained, on average, 64% of accuracy for gender prediction and 67% for age. Additional performance metrics for the results are 64% and 65% of precision and recall, respectively, for gender and 58% and 65% of precision and recall, respectively, for age. In general, the age classification is unbalanced and produces values for the precision and the F1 norm that are lower than those values for accuracy and recall. As we mentioned above, social networks like Twitter or Pinterest are essentially used for different purposes. In addition to pinned images, Pinterest users can also publish short text messages added to their images. Contrary to Twitter, text are posted with less frequency than images and, therefore, text in Pinterest could be less representative than images for predicting the user profile. In this context, a comparison with the related works is shown in Table 6 using different datasets. It is important to point out that authors in [5] used the Twitter PAN 2014 with additional images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Age Range** & **Men (61 Users)** & **Women (65 Users)** & **Total Images** & **Distribution per Class** \\ \hline
18–24 & 100 & 14,688 & 14,788 & 24.6\% \\ \hline
25–34 & 1, 471 & 13,601 & 15,072 & 25.0\% \\ \hline
35–49 & 14,375 & 801 & 15,176 & 25.3\% \\ \hline
50+ & 14,304 & 801 & 15,105 & 25.1\% \\ \hline Images per gender & 30,250 & 29,891 & 60,141 & – \\ \hline Distribution per class & 50.30\% & 49.70\% & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of images per gender and age range in DB1 dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Social Network** & **Class** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline SVM (Ma X. 2014 [2]) & Twitter & Gender & - & 66.4\% & 63.4\% & 63.7\% \\ \hline Logistic R (You Q. 2014 [3]) & Pinterest & Gender & 71.3\% & 71.4\% & 71.3\% & 71.2\% \\ \hline VGG16 ([PERSON] 2018 [19]) & Twitter PAN 2018 & Gender & 81\% & - & - & - \\ \hline VGG16 + SVM ([PERSON] 2018 [5]) & Twitter & Gender & 70\% & - & - & - \\  & & Age & 39\% & - & - & - \\ \hline ResNet-50+NCC ([PERSON] 2019 [6]) & Pinterest & Gender & 71\% & - & - & 67\% \\ ResNet-50+RF & & Age & 41\% & - & - & 29\% \\ \hline VGG16 (Our approach) & Pinterest & Gender & 72\% & 72\% & 72\% & 72\% \\  & (DB1) & Age & 49\% & 49\% & 50\% & 49\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Age and Gender prediction comparison with related approaches using Twitter or Pinterest image datasets.  
While the accuracy of our model (using text and images) is not better than [PERSON] et al. (2019) in the state of the art, the benefit of our model is that we also predict age of users using the same architecture of the CNN. The accuracy of our results and the F1 metric for age prediction (67%) outperform the approaches presented in Table 6. In addition, our strategy could be easily extended to combine text and image information to enrich the vector features in the CNN, which could improve the accuracy results for age and gender prediction. However, we expect to combine text and image published by the same user from different social networks to perform further tests.