Belle L. Tseng

Why we twitter: understanding microblogging usage and communities

Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitters social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.

Evolutionary spectral clustering by incorporating temporal smoothness

Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. To fulfill this dual purpose, a measure of temporal smoothness is integrated in the overall measure of clustering quality. In this paper, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-known k-means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. Furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.

Identifying opinion leaders in the blogosphere

Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.

Why We Twitter: An Analysis of a Microblogging Community

Microblogging is a new form of communication in which users describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. We present our observations of the microblogging phenomena by studying the topological and geographical properties of the social network in Twitter, one of the most popular microblogging systems. We find that people use microblogging primarily to talk about their daily activities and to seek or share information. We present a taxonomy characterizing the the underlying intentions users have in making microblogging posts. By aggregating the apparent intentions of users in implicit communities extracted from the data, we show that users with similar intentions connect with each other.

Using MPEG-7 and MPEG-21 for personalizing video

As multimedia content has proliferated over the past several years, users have begun to expect that content be easily accessed according to their own preferences. One of the most effective ways to do this is through using the MPEG-7 and MPEG-21 standards, which can help address the issues associated with designing a video personalization and summarization system in heterogeneous usage environments. This three-tier architecture provides a standards-compliant infrastructure that, in conjunction with our tools, can help select, adapt, and deliver personalized video summaries to users. In extending our summarization research, we plan to explore semantic similarities across multiple simultaneous news media sources and to abstract summaries for different viewpoints. Doing so will allow us to track a semantic topic as it evolves into the future. As a result, we should be able to summarize news repositories into a smaller collection of topic threads.

Learning multiple graphs for document recommendations

The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from CiteSeer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method.

Information flow modeling based on diffusion rate for prediction and ranking

Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction . identify where information should flow to, and (2) ranking . identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow.

On evolutionary spectral clustering

Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. To fulfill this dual purpose, a measure of temporal smoothness is integrated in the overall measure of clustering quality. In this article, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-known k-means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. Furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.

Ontology-based multi-classification learning for video concept detection

In this paper, an ontology-based multi-classification learning algorithm is adopted to detect concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. The algorithm consists of two steps. In the first step, each single concept model is constructed independently. In the second step, ontology-based concept learning improves the accuracy of the individual concept by considering the possible influence relations between concepts based on a predefined ontology hierarchy. The advantage of ontology learning is that its influence path is based on an ontology hierarchy, which has real semantic meanings. Besides semantics, it also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This learning algorithm can be used for multiple topic document classification such as Internet documents and video documents. We demonstrate that precision-recall can be significantly improved by taking ontology into account

Mining blog stories using community-based and temporal clustering

In recent years, weblogs, or blogs for short, have become an important form of online content. The personal nature of blogs, online interactions between bloggers, and the temporal nature of blog entries, differentiate blogs from other kinds of Web content. Bloggers interact with each other by linking to each others posts, thus forming online communities. Within these communities, bloggers engage in discussions of certain issues, through entries in their blogs. Since these discussions are often initiated in response to online or offline events, a discussion typically lasts for a limited time duration. We wish to extract such temporal discussions, or stories, occurring within blogger communities, based on some query keywords. We propose a Content-Community-Time model that can leverage the content of entries, their timestamps, and the community structure of the blogs, to automatically discover stories. Doing so also allows us to discover hot stories. We demonstrate the effectiveness of our model through several case studies using real-world data collected from the blogosphere.