[CMU AI Seminar] Sep 28 at 12pm (Zoom) -- Ashique Khudabukhsh (CMU) -- Novel Frameworks for Quantifying Political Polarization and Mitigating Hate Speech -- AI Seminar sponsored by Morgan Stanley

[Shaojie Bai]

Dear all,

We look forward to seeing you *next Tuesday (9/28)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of our *CMU AI seminar*, sponsored
by Morgan Stanley <https://www.morganstanley.com/about-us/technology/>.

To learn more about the seminar series or see the future schedule, please
visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

On 9/28, *Ahique Khudabukhsh* (CMU LTI) will be giving a talk on "*Novel
Frameworks for Quantifying Political Polarization and Mitigating Hate
Speech*".

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

*Talk Abstract*: This talk is divided into two parts. Each part summarizes
a broad line of NLP research outlining a new framework. 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.

*Speaker 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.

*Zoom Link*:
https://cmu.zoom.us/j/96000432347?pwd=TXVhU2dlSVZyM3hjTzVVVEhUclVIdz09

Thanks,
Shaojie Bai (MLD)
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