Jeon-Hyung Kang

We explore the feasibility of answering questions by asking strangers by mining the public status updates posted on Twitter. Expand

Probabilistic models can learn users' preferences from the history of their item adoptions on a social media site, and in turn, recommend new items to users based on learned preferences. Expand

This paper presents an approach for classifying student discussions according to a set of discourse structures, and identifying discussions with confusion or unanswered questions. Expand

We explore the quality of information solicited from Twitter users in the domain of product reviews, specifically reviews for a popular tablet computer and L.A.-based food trucks. Expand

Group recommender systems are becoming very popular in the social web owing to their ability to provide a set of recommendations to a group of users . Expand

We address this problem by introducing a novel centrality metric for dynamic network analysis that exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. Expand

We use network proximity to capture the degree to which people are "close" to each other in a social network. Expand

The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective and efficient topic modeling approaches. Expand

We propose to apply Affinity Propagation [4] (AP) to analyze such a corpus, and we hypothesize that AP may provide different perspective to the traditional approach. Expand

We propose $\mathcal LA$ -LDA, a latent topic model which incorporates limited, non-uniformly divided attention in the diffusion process by which opinions and information spread on the social network. Expand