Studying the Thesis of PhD Heroes: Munmun De Choudhury

Given that I am in the process of beginning to write my PhD thesis, I am currently reviewing the PhD thesis of doctors that have been more than successful in their career; these are people I admire and find inspirational for my own PhD path: my PhD heroes.
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!:)

Another layman's explanation of: Expert Evolution in Online Social Networks

I was recently reading a very interesting paper titled: Evolution of Experts in Question Answering Communities by Aditya Pal, Shuo Chang and Joseph Konstan. And thought I would share the paper and intend to explain it in Layman's terms.
There has been vast amount of work done in detecting experts in Question Answering Communities, typically this analysis is either through graph based methods or feature based methods. Graph based methods tend to analyze the link structure of a user in an online social network to find authoritative users. They analyze things such as: to how many other people is the user "friends" to? Feature based methods, on the other hand, analyze the characteristics of the users: how many best answers does the user have? What language style does he use? etc etc
The work we are analyzing seeks to identify experts, but then does a temporal analysis, to study how experts evolve in a community and how they influence a community's dynamics. The online community studied is Stackoverflow. To identify experts, the authors used two approaches: On one hand, they identify the number of positive votes a user's answerers and questions have received (a user gets a positive vote, when his/her answer is helpful to the community, or when his/her question is interesting or relevant to someone in the community) and labeled the top 10% of users with the highest number of votes as experts.
To analyze how experts evolve and how a community can be influenced in time by the answers and social interactions of experts, the authors performed the following:

1. the questions and answers of the community were divided into bi-weekly buckets. Were the first bucket would hold the questions and anwsers of the first two weeks of the stackoverflow data they had collected, the second bucket the questions and answers created in the 3-4th weeks etc etc 
2. For each user it is then possible to calculate per bucket (per every 2 weeks,) the number of questions, answers and best answers he/she have given. 
3. For each user a relative time series is computed of each data type he/she has generated (questions, answers and best answers). This relative time series is constructed so that the contribution of a user can be valued relatively to the contribution of other users. For this, what is done,  is that in each of the time buckets the mean and standard deviation for each data type  are calculated. (lets recall that a bucket holds the number of answers, questions and best answers different  users have given in that particular time period, so for each type of variables, we can calculate the mean and standard deviation. It is then possible to normalize a data point in the time bucket as:
   X_b=(X_b - Mean_b)/(standardDeviation_b)
   
   Where X_b represents the number of answers a particular user has generated in time bucket b. And Mean_b represents the mean of all the number of answers different users have given in time bucket b
4. After this step, each user is associated with 3 relative time series: the time series of their answers, questions and best answers. From the answers and best answer time series, a point wise ratio between best answers and answers is then calculated. This point wise ratio indicates  the probability of a user's answers being selected as the best answer.
   The following figure shows an interesting plot where we see how the likelihood of an expert and an average user receiving the votes for best answer changes over time.

 

What we notice is that the likelihood of receiving the best answer increases significantly over time for experts in comparison to average users. Initially the likelihood of receiving a best answer is the same for both experts and average users. The authors believe that this occurs, because when a new person, who happens to be an expert, joins the community, other users are wary of marking the answers of newcomers as the best. But as the expert gains reputation, the rest of the community members become more and more comfortable in marking their answers as the best.
The next interesting thing the author's analyzed was the the likelihood of having a user ask a question. It was seen that in general expert users do not ask questions. They found that the overall question to answer ratio among experts was 1/15 !!! To compare the time series of questions and answers, the authors computed an aggregate time series of the number of questions and answers of experts, and then normalize the time series such that it has mean=0 and standard deviation =1. From these two resulting distributions (questions and answers) a cross-covariance was computed. Now, the cross-covariance will give us information about just how similar two signals are, as a function of a time-lag applied to them. The authors found that the optimal time lag was zero for the majority of expert users. Which indicates that likelihood of an expert asking or responding to a question vary simultaneously.