Extracting Social Network Contents to Classify ADHD Types Based on Behavioral Symptoms and Activities

The Attention Deficit Hyperactivity Disorder (ADHD) is a complex mental health disorder that affects children's learning activities and also their relationships with other children. There are various types of ADHD symptoms, which some of them can be easily recognized as indicators of having ADHD and some of them are difficult to specify as symptoms. Generally, the average age for diagnosing ADHD in children is around seven years old. It is better to early discover these symptoms in children and classify their ADHD types so appropriate and necessary treatments can be effectively applied to improve ADHD children's behaviors and learning activities. The ADHD diagnosis takes a lot longer for children under six years old. This research aims to improve the ADHD diagnosis and attempt to discover symptoms and behavioral activities that can be the important indicators for classifying ADHD types of the children. Today, many parents share knowledge and experiences about their children in social networks. These shareable contents can be useful to help identify some ADHD symptoms as well as recommended treatments or activities for the children. Thus, in this work, we propose an approach to extract contents from social media (e.g., Facebook contents) in order to discover the information regarding ADHD symptoms and use this extracted information to classify ADHD types based on the behavioral symptoms and activities. To verify our results, we use three sets of criteria from the Diagnostic and Manual of Mental Disorders (DSM) that are used by many ADHD specialists and medical doctors. The three sets of criteria include SNAP-IV, Vanderbilt Assessment Scale, and KUS-SI. Our research goal is to provide a way to help parents to find relevant information and learn about ADHD types and their related symptoms as well as recommended activities for improving ADHD conditions in children.