extracting age for Twitter users [@sloan2015tweets]. They also parse description for occupation by consulting the SOC2010 list of occupations[^13] and validating it through social surveys. A novel age inference model was developed while relying on homophily interaction information and content for predicting age of Twitter users [@zhang2016your]. The limitations of textual content for predicting age and gender was highlighted by [@nguyen2014gender]. They distinguish language use based on social gender, age identity, biological sex and chronological age by collecting crowdsourced signals using a game in which players (crowd) guess the biological sex and age of a user based only on their tweets. Their findings indicate how linguistic markers can misguide (e.g., a heart represented as &lt;3 can be misinterpreted as feminine when the writer is male.) Estimating age and gender from facial images by training a convolutional neural networks (CNN) for face recognition is an active line of research [@han2013age; @levi2015age; @masi2016we].

Dataset
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Self-disclosure clues have been extensively utilized for creating ground-truth data for numerous social media analytic studies e.g., for predicting demographics [@mislove2011understanding; @sloan2015tweets], and user’s depressive behavior [@yazdavar2017semi; @de2017gender; @8419435]. For instance, vulnerable individuals may employ depressive-indicative terms in their Twitter profile descriptions. Others may share their age 