# Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users
## 1 Introduction  
Multiple studies have been done in order to explore how to predict age, gender, authorship, region, and other characteristics of the author of a text. In years, along with the rise of social networks, a lot of works have focused on authorship and attributes identification in social media posts, by analyzing text, images, and posting behavior. However, most work is focused on text analysis due to the amount of text generated per minute in social networks as Twitter [1]. At present, several statistics, people similarities, and trends are computed from the user activity in social networks, including the author profiling, which remains as an open multifactor challenge. In this context, the most prominent conference about author profiling (PAN at CLEF) has been studying, since 2013, these topics; however, it was until 2018 when images were included as part of the training sets to explore the age and gender identification using images and text information. The use of images for author profiling provides additional information and, therefore, more data diversity to create a model. For instance, the authors in [2; 3] explored, in 2014, the possibility ofpredicting users' gender using Scale-Invariant Feature Transform or SIFT visual descriptor on images posted by the user, from Twitter and Pinterest, respectively. The authors in [2] classified such posted images into categories before predicting gender and, in [3], they directly computes gender prediction using a Logistic Regression Classifier. Due to several image descriptors spent high computational time and show more complexity, Convolutional Neural Networks (CNN) have influenced most of the methods for gender and age prediction because of their high performance results. For instance, more recently, during PAN 2018 [4], multiple techniques were explored to predict user's gender on twitter posts including images, the best result was reported by [PERSON] et al. [4] who used the CNN model VGG16 to extract the features from images and then combining them with textual features. Additionally, in 2018 [PERSON] [5] used a similar method extracting features with a VGG16 for classifying age and gender with a Support Vector Machine (SVM) on Twitter images. Later, [PERSON] [6] made a comparison between different classifier for age and gender using features that were extracted from a ResNet-50 on Pinterest images. This work focuses on the analysis of users' publications in social networks, specifically Pinterest and Twitter, with the aim of establishing a representative user model based on age and gender. The main idea behind our work is to recommend trend categories to Pinterest users by analyzing the images they have pinned and, with this information, enrich the typical user profile consisting of gender and age user prediction. To do this, we propose two separate classifiers: (1) for predicting age and gender and (2) for classifying images pinned by users on Pinterest. Moreover, for the age and gender classifier, two methods are implemented, one of them is text-based and the other is image-based. Therefore, we analyze distinct and reliable features in tweets or in images published on Twitter or Pinterest respectively, in order to predict age and gender. We evaluate two different types of datasets (from Twitter and Pinterest) to compare text and image features performance separately for predicting an initial profile for each user in the dataset (age and gender). The second classifier predicts the category for any pinned image on Pinterest along with the other 32 predefined categories. The proposed image classifier helps to describe the interest of users and, at the same time, create a set of user preferences. Finally, additional categories can be recommended to the user according to similar image pins published and the association rules based on the activity of thousands of users. Thus, the Pinterest categories predicted for a user are combined and evaluated following proposed association rules.