I have decided to create blog posts that describe some of the main contributions that these PhD thesis had, the new ways of thinking that these doctors brought in. I will begin this series with the PhD dissertation of Munmun De Choudhury, currently working in Microsoft research; she has several publications in top conferences such as CSCW, CHI, ICWSM, WWW, among others. Munmun is indeed one of my main true PhD heroes.
Her thesis focused on designing frameworks and computational models to obtain a detailed understanding of how communication happens in online social networks. It was considered that online communication patterns are divided in two main forms: the actual message discussed, and the channel or media used to discuss the message. Work before Dr. De Choudhury's thesis focused more on studying the network structure and dynamics, and little emphasis was given to providing tools that could characterize the type of messages present in an online community, providing insightful observational studies on large-scale social communication datasets.
In particular, her research explored 3 main areas: (1) how information is diffused in an online social network, analyzing in particular how the influence of users and the fact that you can have very many similar users talking to each other, affects information spread; (2) how communication dynamics in online communities can be modeled, particularly focused on external and internal communication factors; (3) how "interestingness" of conversations can be modeled and measured , in particular focusing on detecting interesting conversations and identifying the features that turn them into interesting content.
Providing means to explore and analyze what are the dynamics and impact of our online social communications is important because social media data has shown to originate and create real world revolutions, think e.g., elections in Iran, Earthquake in Haiti. Social media also enables viral marketing, enabling collaborations in corporations, and can help users find experts, or even people that can help them connect with others.
In the following we begin exploring in detail each of the main themes discussed in her thesis.
Measuring the Intrestingness of a Conversation: The work considered that a conversation was interesting, when it made participants return to the conversation and continue commenting and posting. Such behavior is observed frequently on youtube, when users have already watched the video, yet they are returning to the video to comment and respond to others.
The work considered that people will participate and return to conversation when the theme of the conversation is engaging and/or interesting people are participating in the discussion. They predicted that users will return to a conversation, when they: (a) find the whole conversation theme interesting; (b) see comments by people that are well known in the community; (c) observe an engaging dialogue between two or more people (an absorbing back and forth between two people).
Additionally conversations that are interesting will be propagated throughout the network; we will observe things like: users will seek other users who participated in interesting conversations; interesting themes will tend to be present in other conversations in the community; users who participated in the interesting conversations will search for other similar conversations about the same theme.
Themes are defined as a sets of salient topics associated with conversations at different points in time.
Interesting users are defined as users who after they comment, they receive a wide variety of comments from others; users that tend to participate in conversations that are currently popular in the community; users that tend to comment and engage in conversations with other interesting users.
Theme modeling: Within the modeling of themes, an idea that I found interesting from this thesis is that while there was a focus on modeling what themes were present in a conversation in a given time period, there was also an emphasis on normalizing the amount of content that was associated with a theme based on time and based on co-participation. This helped identified themes that were not only temporally popular or interesting due to an external event, or themes that certain users tended to frequently comment, not so much because the conversations around the theme were interesting, but rather because they had a probable passion for the subject.
Information Difusion:
(post in progress...come back soon!:)
