Face++ API to automatically capture them from the shared images. Positive emotions are joy and surprise, and negative emotions are anger, disgust, fear, and sadness. In general, for each user u in $U_t$, we process profile/shared images for both the depressed and the control groups with at least one face from the shared images (Table \[Statistics-image\]). For the photos that contain multiple faces, we measure the average emotion.

  ----------- --------- -- ---------
   Depressed   Control      Control
     3466       4127        401435
  ----------- --------- -- ---------

  : Statistics of Processed Shared/Profile Images[]{data-label="Statistics-image"}

Figure \[fig:testEvaluation\] illustrates the inter-correlation of these features. Additionally, we observe that emotions gleaned from facial expressions correlated with emotional signals captured from textual content utilizing LIWC. This indicates visual imagery can be harnessed as a complementary channel for measuring online emotional signals. **General Image Features:** The importance of interpretable computational aesthetic features for studying users’ online behavior has been highlighted by several efforts [@datta2006studying; @liu2016analyzing; @celli2014automatic]. *Color*, as a pillar of the human vision system, has a strong association with conceptual ideas like emotion [@naz2004relationship; @huang2006natural][^17]. We measured the normalized red, green, blue and the mean of original colors, and brightness and contrast relative to variations of luminance. We represent images 