Machine learning approaches for understanding social interactions on Twitter

Speaker: Alice Oh , KAIST

Date: Tuesday, May 06, 2014

Time: 2:00 PM to 3:00 PM

Refreshments: 1:45 PM

Public: Yes

Location: Patil/Kiva 32-G449

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Host: Rob Miller

Contact: Juho Kim,

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Reminder Subject: TALK: Machine learning approaches for understanding social interactions on Twitter

Abstract: In this talk, I will share the experiences and challenges of
my research group in applying probabilistic topic models (e.g.,
LDA,HDP) and other machine learning algorithms to interesting research
questions about social interactions on Twitter. Specifically, I will
discuss three papers on the topics of emotion cycles, self-disclosure
behavior, and bilingualism on Twitter.

In the first paper, we developed a computational framework based on
LDA for understanding the social aspects of emotions in Twitter
conversations. We looked for meaningful patterns of emotional
exchanges in a conversation, where those patterns may depend on the
topics and words of the conversation. We looked at how conversational
partners can influence each others' emotions and topics, and we
discovered interesting patterns in the overall emotions of the
conversations. We also found that tweets containing sympathy, apology,
and complaint are significant emotion influencers. Finally, we
discovered lexical patterns, such as the usage of profanity, that
influence the overall emotion of a conversation. In the second paper,
we looked at the relationship between tie strength and
self-disclosure. In social psychology, it is generally accepted that
one discloses more of his/her personal information to someone in a
more intimate and trusting relationship,often called a "strong tie" in
the social network literature. We question and study how tie strength
affects self-disclosures in the context of Twitter conversations. Our
results illustrate that in general, there is a significant trend that
validate the findings in social psychology, however, there are
interesting exceptions to this general trend.
We also uncover a positive and significant relationship between
self-disclosure and online conversa- tion frequency over time. In the
third paper, we looked at how many Twitter users, located in
multilingual societies such as Switzerland and Qatar, tweet in two
different languages. We discovered that just as in the real world,
bilinguals act as bridges connecting monolingual groups. We showed
that there are patterns in the amount of each language used, highly
correlated with the number of followers in the respective language. We
found that there are interesting patterns about which topics are more
actively tweeted in which language.

Bio: Alice Oh is an Assistant Professor of Computer Science at Korea
Advanced Institute of Science and Technology. She leads the Users and
Information Lab with the vision of modeling various types of
information from multiple perspectives and understanding users in
terms of their individual and group behaviors. To that end, she
studies and employs methods from machine learning, human-computer
interaction, and statistical natural language processing. Alice
completed her M.S. in Language and Information Technologies at CMU and
her Ph.D. in Computer Science at MIT.

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See other events that are part of the HCI Seminar Series 2013/2014.

Created by Juho Kim Email at Monday, April 21, 2014 at 5:18 PM.