Hope Speech and Help Speech: Surfacing Positivity Amidst Hate

Dr. Ashique KhudaBukhsh, CMU

Short Bio:
Ashique KhudaBukhsh is currently a Project Scientist at the School of Computer Science, Carnegie Mellon University (CMU). Prior to this role, he was a postdoc mentored by Prof. Jaime Carbonell at CMU. His PhD thesis (Computer Science Department, Carnegie Mellon University, also advised by Prof. Jaime Carbonell) focused on referral networks, an emerging area at the intersection of Active Learning and Game Theory. His Master’s thesis at the University of British Columbia (UBC), advised by Prof. Kevin Leyton-Brown and Prof. Holger H. Hoos, focused on automated algorithm design for combinatorial hard problems. His current research focus is in the intersection of low-resource NLP and AI for Social Impact. In this field, he is interested in analyzing globally important events in South East Asia and developing methods for noisy social media texts generated in this linguistically diverse region.

Date:July 23, 2020
Time: 7:30 PM IST


Tackling online attacks targeting certain individuals, groups of people or communities is a major modern-day web challenge. Research efforts in hate speech detection thus far have largely focused on identifying and subsequently filtering out negative content that specifically targets such communities. However, this blocking the hate approach alone may not suffice in certain scenarios. We focus on two important cases where amplifying the positives is equally important: refugee crisis in the era of ubiquitous internet and heated online discussions during heightened political tension between nuclear adversaries. In the context of the Rohingya refugee crisis and the India-Pakistan conflict triggered by the Pulwama terror attack, we describe two lines of work, help speech and hope speech, exhibiting a thematic similarity of surfacing positivity amidst hate. Our work addresses several low-resource Natural Language Processing (NLP) challenges using annotation-efficient methods, Active Learning and sampling techniques, and cross-lingual sampling techniques that harness code-switching.