TY - GEN
T1 - Real-Time Inference of User Types to Assist with more Inclusive and Diverse Social Media Activism Campaigns
AU - Karbasian, Habib
AU - Purohit, Hemant
AU - Handa, Rajat
AU - Malik, Aqdas
AU - Johri, Aditya
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - 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.
AB - 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.
KW - feature engineering
KW - multi-class classification
KW - real-time user-type classification
KW - twitter
UR - http://www.scopus.com/inward/record.url?scp=85051216660&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051216660&partnerID=8YFLogxK
U2 - 10.1145/3278721.3278781
DO - 10.1145/3278721.3278781
M3 - Conference contribution
AN - SCOPUS:85051216660
T3 - AIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
SP - 171
EP - 177
BT - AIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
PB - Association for Computing Machinery, Inc
T2 - 1st AAAI/ACM Conference on AI, Ethics, and Society, AIES 2018
Y2 - 2 February 2018 through 3 February 2018
ER -