disclosure varies among the two gender groups. For statistical significance, we performed chi-square test (null hypothesis: gender and depression are two independent variables). Figure \[gender\_vs\_depression\] illustrates gender association with each of the two classes. Blue circles (positive residuals, see Figure \[gender\_vs\_depression\]-A,D) show positive association among corresponding row and column variables while red circles (negative residuals, see Figure \[gender\_vs\_depression\]-B,C) imply a repulsion. Our findings are consistent with the medical literature [@nolen1987sex] as according to [@ford2002prevalence] more women than men were given a diagnosis of depression. In particular, the female-to-male ratio is 2.1 and 1.9 for Major Depressive Disorder and Dysthymic Disorder respectively. Our findings from Twitter data indicate there is a strong association (Chi-square: 32.75, p-value:1.04e-08) between being female and showing depressive behavior on Twitter.

![Gender and Depressive Behavior Association (Chi-square test: color-code: (blue:association), (red: repulsion), size: amount of each cell’s contribution)](chiseq){width="30.00000%"}

\[gender\_vs\_depression\]

Data Modality Analysis
======================

We now provide an in-depth analysis of visual and textual content of vulnerable users.

**Visual Content Analysis:** We show that the visual content in images from posts as well as profiles provide valuable psychological cues for understanding a user’s depression status. Profile/posted images can surface self-stigmatization [@barney2006stigma]. Additionally, as opposed to typical computer vision 