framework for object recognition that often relies on thousands of predetermined low-level features, what matters more for assessing user’s online behavior is the emotions reflected in facial expressions [@pantic2009machine], attributes contributing to the computational aesthetics [@datta2006studying], and sentimental quotes they may subscribe to (Figure \[self\_reported\]) [@liu2016analyzing]. **Facial Presence:** For capturing facial presence, we rely on [@zhou2013extensive]’s approach that uses multilevel convolutional coarse-to-fine network cascade to tackle facial landmark localization. We identify facial presentation, emotion from facial expression, and demographic features from profile/posted images [^15]. Table \[facial-Presence\] illustrates facial presentation differences in both profile and posted images (media) for depressed and control users in $U_t$. With control class showing significantly higher in both profile and media (8%, 9% respectively) compared to that for the depressed class. In contrast with age and gender disclosure, vulnerable users are less likely to disclose their facial identity, possibly due to lack of confidence or fear of stigma.

                   ${\chi}^2$  
  -- ----------- ------------- --------------
      Depressed     Control    
         72%                    163.52\*\*\*
         4%                     167.2\*\*\*
         8%                         2.55

  : Facial Presence Comparison in Profile/Posted images for Depressed and Control Users[]{data-label="facial-Presence"}

**Facial Expression:** Following [@liu2016analyzing]’s approach, we adopt Ekman’s model [^16] of six emotions: anger, disgust, fear, joy, sadness and surprise, and use the 