We propose a new method for estimating user attributes (gender, age, occupation, and interests) of a Twitter user from the user's contents (profile document and tweets) and social neighbors, i.e. those whom the user has mentioned. Our labeling method is able to collect a large amount of training data automatically by using Twitter users associated with a blog account. Furthermore, we experiment estimation methods using social neighbors with three adjustable levels of its information and show that our method, which uses the target user's profile document and tweets and the neighbors' profile documents (not including tweets), achieves the best accuracy.

Microblogging services generate huge histories that suit animated visualization based on graph drawing, but response speeds are insufficient. This paper proposes an effective updating method for the real-time visualization of time varying data. We propose an initial positioning method by combining Additional Edge Resizing (AER) with Sorted Sequential Barycenter Merging (SSBM). AER resizes edges between existing nodes when new edges are added before updating the visualization. SSBM initially positions multiple new nodes sequentially through priority based on the degree of connections to the existing graph. The proposed method prevents a decrease in readability at data update and achieves fast convergence with high accuracy. Quantitative and qualitative evaluations using the energy function and RMSE are detailed along with some visualization results. A graph drawing tool based on HTML5 is also introduced as an implementation of the proposed method and present some use cases for twitter data.

This paper proposes a method that analyzes the location log data of multiple users to recommend locations to be visited. The method uses our new topic model, called Geo Topic Model, that can jointly estimate both the user's interests and activity area hosting the user's home, office and other personal places. By explicitly modeling geographical features of locations and users, the user's interests in other features of locations, which we call latent topics, can be inferred effectively. The topic interests estimated by our model 1) lead to high accuracy in predicting visit behavior as driven by personal interests, 2) make possible the generation of recommendations when the user is in an unfamiliar area (e.g. sightseeing), and 3) enable the recommender system to suggest an interpretable representation of the user profile that can be customized by the user. Experiments are conducted using real location logs of landmark and restaurant visits to evaluate the recommendation performance of the proposed method in terms of the accuracy of predicting visit selections. We also show that our model can estimate latent features of locations such as art, nature and atmosphere as latent topics, and describe each user's preference based on them.

We propose a classification model of tweet streams in Twitter, which are representative of document streams whose statistical properties will change over time. Our model solves several problems that hinder the classification of tweets; in particular, the problem that the probabilities of word occurrence change at different rates for different words. Our model switches between two probability estimates based on full and recent data for each word when detecting changes in word probability. This switching enables our model to achieve both accurate learning of stationary words and quick response to bursty words. We then explain how to implement our model by using a word suffix array, which is a full-text search index. Using the word suffix array allows our model to handle the temporal attributes of word n-grams effectively. Experiments on three tweet data sets demonstrate that our model offers statistically significant higher topic-classification accuracy than conventional temporally-aware classification models.

This paper describes the development of an adaptive self-learning environment in which both mobile phones and personal computers can be used as client terminals. The SCORM 2004 specification was used to implement a standard-based learner adaptive function. The specification was extended to enable offline learning using mobile phones and sharing of course structure and learner tracking information for learning activities using both mobile phones and personal computers. Because the application-programming environment of mobile phones varies from carrier to carrier, a common content format was specified for the learning content rendered by the browsers developed for each carrier's environment. The system can provide adaptive courses such that results of a pre-test taken on a mobile phone modify the lecture content on a personal computer, fitting it to each learner's level of knowledge and understanding.