Mon, Apr 23, 2018 @ 11:30 AM - 01:30 PM
PhD Candidate: Siddharth Jain
Committee: Ron Artstein (Chair), Paul Rosenbloom, Morteza Dehghani, Kallirroi Georgila, Elsi Kaiser
Title: Identifying Social Roles in Online Contentious Discussions
Time: Mon, April 23, 2018 @ 11:30 AM - 1:30 PM.
Room: SOS B45.
The main goal of this dissertation is to identify social roles of participants in online contentious discussions, define these roles in terms of behavioral characteristics
participants show in such discussions, and develop methods to identify these participant roles automatically. As social life becomes increasingly embedded in online systems, the concept of social role becomes increasingly valuable as a tool for
simplifying patterns of action, recognizing distinct participant types, and cultivating and managing communities. In contentious discussions, the roles may exert a major influence on the course and/or outcome of the discussions. The existing work on social roles mostly focuses on either empirical studies or network based analysis. Whereas this dissertation presents a model of social roles by analyzing the content of the participants' contribution. In the first portion of this dissertation, I present the corpus of participant roles in online discussions from Wikipedia: Articles for Deletion and 4forums.com discussion forums.
A rich set of annotations of behavioral characteristics such as stubbornness, sensibleness, influence, and ignored-ness, which I believe all contribute in the identification of roles played by participants, is created to analyze the contribution of the participants. Using these behavioral characteristics, Participant roles such as leader,
follower, rebel, voice in wilderness, idiot etc. are defined which reflect these behavioral
characteristics. In the second part of this dissertation I present the methods used to identify these participant roles in online discussions automatically using the
contribution of the participants in the discussion. First, I develop two models to identify leaders in online discussions that quantify the basic leadership qualities of participants. Then, I present the system for analyzing the argumentation structure of comments in discussions. This analysis is divided in three parts: claim detection, claim delimitation, and claim-link detection. Then, the dissertation presents the social roles model to identify the participant roles in discussions. I create classification models and neural network structures for each behavioral characteristic using a set of features based on participants' contribution to the discussion to determine the behavior values for participants. Using these behavioral characteristic values the roles of participants
are determined based on the rules determined from the annotation scheme. I show that for both, the classification models and neural networks, the rule based methods perform
better than the model that identifies the participant roles directly. This signifies that the framework of breaking down the problem of identifying social roles to determining
values of specific behavioral characteristics make it more intuitive in terms of what we expect from participants who assume these roles. Although the neural network methods
perform worse than their traditional classification method counterparts, when provided with additional training data, neural network structures improve at a much higher rate.
In the last part of the dissertation, I use the social roles model as a tool to analyze participants' behavior in large corpus. Social roles are automatically tagged in
Wikipedia corpus containing -26000 discussions. This allows determining participants' roles over time in order to identify whether they assume different roles in different discussions, and what factors may affect an individual's role in such discussions. I investigate three factors: topic of the discussion, amount of contention in the discussion, and other participants in the discussion. The results show that participants behave similarly in most situations. However, the social roles model is able to identify instances where participants' behavior patterns are different than their own typical behavior. In doing so, the model provides useful context regarding the reason behind these behavioral patterns by identifying specific behaviors affected by the situation.
Location: Social Sciences Building (SOS) - B45
Audiences: Everyone Is Invited
Contact: Lizsl De Leon