Ashique KhudaBukhsh will be giving a virtual talk on Friday, August 20th, from 11am — 12pm. Zoom information will be distributed via the Berkeley NLP Seminar listserv.

Title: Novel Frameworks for Quantifying Political Polarization and Mitigating Hate Speech

Abstract: The first part of the talk presents a new methodology that offers a fresh perspective on interpreting and understanding political polarization through machine translation. I begin with a novel proposition that two sub-communities viewing different US cable news networks are speaking in two different languages. Next, I demonstrate that with this assumption, modern machine translation methods can provide a simple yet powerful and interpretable framework to understand the differences between two (or more) large-scale social media discussion data sets at the granularity of words.

The second part of the talk presents a new direction for mitigating online hate. Much of the existing research geared toward making the internet a safer place involves identifying hate speech as the first step. However, little or no attention is given to the possibility that the not-hate-speech subset of the corpus may contain content with potentially positive societal impact. I introduce two new tasks, namely hope speech detection — detecting hostility-diffusing, peace- seeking content — and help speech detection — detecting content supportive of a disenfranchised minority. I illustrate applications of these two new tasks in the context of the most-recent India-Pakistan conflict triggered by the 2019 Pulwama terror attack, and the longstanding Rohingya refugee crisis that rendered more than 700,000 people homeless. Beyond the framework novelty of focusing on the positive content, this work addresses several practical challenges that arise from multilingual texts in a noisy, social media setting.

Bio: Ashique KhudaBukhsh is an assistant professor at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). His current research lies at the intersection of NLP and AI for Social Impact as applied to: (i) globally important events arising in linguistically diverse regions requiring methods to tackle practical challenges involving multilingual, noisy, social media texts; and (ii) polarization in the context of the current US political crisis. In addition to having his research been accepted at top artificial intelligence conferences and journals, his work has also received widespread international media attention that includes multiple coverage from BBC, Wired, Salon, The Independent, VentureBeat, and Digital Trends.

Prior to joining RIT, Ashique was a Project Scientist at the Language Technologies Institute, Carnegie Mellon University (CMU) mentored by Prof. Tom Mitchell. Prior to this, he was a postdoc mentored by Prof. Jaime Carbonell at CMU. His PhD thesis (Computer Science Department, CMU, also advised by Prof. Jaime Carbonell) focused on distributed active learning.