and gender, e.g., “16 years old suicidal girl”(see Figure \[self\_reported\]). We employ a huge dataset of 45,000 self-reported depressed users introduced in [@yazdavar2017semi] where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual’s profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges [@yazdavar2017semi]. This dataset [$U_t$]{} contains the metadata [^14] values of each user such as profile descriptions, followers\_count, created\_at, and profile\_image\_url.

**Age Enabled Ground-truth Dataset:** We extract user’s age by applying regular expression patterns to profile descriptions (such as “17 years old, self-harm, anxiety, depression”) [@sloan2015tweets]. We compile “age prefixes” and “age suffixes”, and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a “date” or age (e.g., 1994). We selected a subset of 1061 users among $U_t$ as gold standard dataset $U_a$ who disclose their age. From these 1061 users, 822 belong to depressed class and 239 belong to control class. From 3981 depressed users, 20.6% disclose their age 