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Michelle Zhou

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4 papers

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TIST Journal 2015 Journal Article

Who Will Retweet This? Detecting Strangers from Twitter to Retweet Information

Kyumin Lee
Jalal Mahmud
Jilin Chen
Michelle Zhou
Jeffrey Nichols

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed three models: (1) a feature-based model that leverages people's exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; (2) a wait-time model based on a user's previous retweeting wait times to predict his or her next retweeting time when asked; and (3) a subset selection model that automatically selects a subset of people from a set of available people using probabilities predicted by the feature-based model and maximizes retweeting rate. Based on these three models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.

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AAAI Conference 2010 Conference Paper

Constrained Coclustering for Textual Documents

Yangqiu Song
Shimei Pan
Shixia Liu
Furu Wei
Michelle Zhou
Weihong Qian

In this paper, we present a constrained co-clustering approach for clustering textual documents. Our approach combines the benefits of information-theoretic co-clustering and constrained clustering. We use a two-sided hidden Markov random field (HMRF) to model both the document and word constraints. We also develop an alternating expectation maximization (EM) algorithm to optimize the constrained coclustering model. We have conducted two sets of experiments on a benchmark data set: (1) using human-provided category labels to derive document and word constraints for semi-supervised document clustering, and (2) using automatically extracted named entities to derive document constraints for unsupervised document clustering. Compared to several representative constrained clustering and co-clustering approaches, our approach is shown to be more effective for high-dimensional, sparse text data.

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AAAI Conference 2006 Conference Paper

Responsive Information Architect: Enabling Context-Sensitive Information Seeking

Michelle Zhou
Shimei Pan
Vikram Aggarwal

Information seeking is an important but often difficult task especially when involving large and complex data sets. We hypothesize that a context-sensitive interaction paradigm can greatly assist users in their information seeking. Such a paradigm allows a system to both understand user data requests and present the requested information in context. Driven by this hypothesis, we have developed a suite of intelligent user interaction technologies and integrated them in a full-fledged, context-sensitive information system. In this paper, we review two sets of key technologies: context-sensitive multimodal input interpretation and automated multimedia output generation. We also share our evaluation results, which indicate that our approaches are capable of supporting context-sensitive information seeking for practical applications.

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AAAI Conference 2004 System Paper

Responsive Information Architect: A Context-Sensitive Multimedia Conversation Framework for Information Seeking

Michelle Zhou
Rosario Uceda-Sosa
Min Chen

We are building a context-sensitive framework, called Responsive Information Architect (RIA), which engages users in automatically generated multimedia conversations. Unlike existing information browsing paradigm that forces users to explore information following pre-defined paths (e.g., GUI menus), RIA allows users to express their information requests flexibly using a mixture of input modalities, including speech, text, and gesture. Using a rich context, such as conversation history and data semantics, RIA is capable of understanding user inputs, including these complex data queries.

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