[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]

Hi all,

Just a reminder that the CMU AI Seminar <http://www.cs.cmu.edu/~aiseminar/> is
tomorrow *12pm-1pm*:
https://cmu.zoom.us/j/96000432347?pwd=TXVhU2dlSVZyM3hjTzVVVEhUclVIdz09.

*Ashique Khudabukhsh (CMU/RIT)* will be talking about his research on
quantifying political polarization and mitigating online hate.

Thanks,
Shaojie

On Fri, Sep 24, 2021 at 5:42 PM Shaojie Bai <shaojieb at cs.cmu.edu> wrote:

> 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)
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20210927/59ec66ca/attachment.html>