Felix Ming Fai Wong

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

We study user behavior in the courses offered by a major massive online open course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education on MOOC and is done via online discussion forums, our main focus is on understanding forum activities. Two salient features of these activities drive our research: (1) high decline rate: for each course studied, the volume of discussion declined continuously throughout the duration of the course; (2) high-volume, noisy discussions: at least 30 percent of the courses produced new threads at rates that are infeasible for students or teaching staff to read through. Further, a substantial portion of these discussions are not directly course-related. In our analysis, we investigate factors that are associated with the decline of activity on MOOC forums, and we find effective strategies to classify threads and rank their relevance. Specifically, we first use linear regression models to analyze the forum activity count data over time, and make a number of observations; for instance, the teaching staffs active participation in the discussions is correlated with an increase in the discussion volume but does not slow down the decline rate. We then propose a unified generative model for the discussion threads, which allows us both to choose efficient thread classifiers and to design an effective algorithm for ranking thread relevance. Further, our algorithm is compared against two baselines using human evaluation from Amazon Mechanical Turk.

Why watching movie tweets won't tell the whole story?

Data from Online Social Networks (OSNs) are providing analysts with an unprecedented access to public opinion on elections, news, movies, etc. However, caution must be taken to determine whether and how much of the opinion extracted from OSN user data is indeed reflective of the opinion of the larger online population. In this work we study this issue in the context of movie reviews on Twitter and compare the opinion of Twitter users with that of IMDb and Rotten Tomatoes. We introduce metrics to quantify how Twitter users can be characteristically different from general users, both in their rating and their relative preference for Oscar-nominated and non-nominated movies. We also investigate whether such data can truly predict a movies box-office success.

Quantifying Political Leaning from Tweets, Retweets, and Retweeters

The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.

A Taxonomy of Censors and Anti-Censors Part II: Anti-Censorship Technologies

This paper presents a conceptual study of Internet anti-censorship technologies. It begins with an overview of previous research on Internet anti-censorship systems and discusses their social, political and technological dimensions. Then for deployed Internet anti-censorship technologies, a taxonomy of their principles and techniques is presented, followed by a discussion of observed trends and implications. Based on the observations, the paper concludes with a discussion on the most critical design features to enable a successful and effective system.

Social learning networks: Efficiency optimization for MOOC forums

A Social Learning Network (SLN) emerges when users exchange information on educational topics with structured interactions. The recent proliferation of massively scaled online (human) learning, such as Massive Open Online Courses (MOOCs), has presented a plethora of research challenges surrounding SLN. In this paper, we ask: How efficient are these networks? We propose a framework in which SLN efficiency is determined by comparing user benefit in the observed network to a benchmark of maximum utility achievable through optimization. Our framework defines the optimal SLN through utility maximization subject to a set of constraints that can be inferred from the network. Through evaluation on four MOOC discussion forum datasets and optimizing over millions of variables, we find that SLN efficiency can be rather low (from 68% to 82% depending on the specific parameters and dataset), which indicates that much can be gained through optimization. We find that the gains in global utility (i.e., average across users) can be obtained without making the distribution of local utilities (i.e., utility of individual users) less fair. We also discuss ways of realizing the optimal network in practice, through curated news feeds in online SLN.