Real-Time Inference of User Types to Assist with More Inclusive Social Media Activism Campaigns

TL;DR

This paper develops a real-time AI model to classify Twitter users into organizations, males, or females, aiding inclusive social media activism campaigns by providing immediate insights into participant demographics.

Contribution

It introduces a novel framework for real-time user type classification on Twitter, outperforming existing binary classifiers for social cause analysis.

Findings

The model accurately classifies user types in real-time.

It outperforms baseline binary classifiers.

Applicable to future social campaigns for real-time social behavior insights.

Abstract

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.