  Social media provides a mechanism for people to engage with social causes
across a range of issues. It also provides a strategic tool to those looking to
advance a cause to exchange, promote or publicize their ideas. In such
instances, AI can be either an asset if used appropriately or a barrier. One of
the key issues for a workforce diversity campaign is to understand in real-time
who is participating - specifically, whether the participants are individuals
or organizations, and in case of individuals, whether they are male or female.
In this paper, we present a study to demonstrate a case for AI for social good
that develops a model to infer in real-time the different user types
participating in a cause-driven hashtag campaign on Twitter,
ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a
Twitter user into three classes: organization, male and female in a real-time
manner. The framework is tested against two datasets (ILLAE and a general
dataset) and outperforms the baseline binary classifiers for categorizing
organization/individual and male/female. The proposed model can be applied to
future social cause-driven campaigns to get real-time insights on the
macro-level social behavior of participants.
