Online reviews are playing an increasingly important role in understanding and predicting users’ rating behavior, which brings great opportunities for users and organizations to make better decisio...

Inferring drug usage patterns includes age of drug abuse and intention of rehabilitation, which is of much importance for drug abuse surveillance. The challenges are how to mine patterns from posts and interaction relationships between users. In this paper, we propose a novel drug usage pattern inference method, which improves the inference accuracy by integrating the semantic features and interaction relationships effectively. Experimental results on a real-world dataset demonstrate the efficacy of the proposed method.

News diffusion prediction aims to predict a sequence of news sites which will quote a particular piece of news. Most of previous propagation models make efforts to estimate propagation probabilities along observed links and ignore the characteristics of news diffusion processes, and they fail to capture the implicit relationships between news sites. In this paper, we propose an algorithm to model the news diffusion processes in a continuous space and take the attributes of news into account. Experiments performed on a real-world news dataset show that our model can take advantage of news’ attributes and predict news diffusion accurately.

Monitoring the evolution phases of real-time event including occurrence, development, climax, decline and ending is crucial for management department to intuitively and comprehensively understand the event and then make better decisions. However, there have been very few studies on performing phase evolution analysis of event using the number of posts at the specific time unit. The challenge of this problem is how to identify temporal pattern and mine topic of different phases automatically. In this paper, we propose a unified phase evolution mining model, it firstly identifies the temporal patterns of phases based on k-means and empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media platform to understand the event evolution in a more comprehensive way. Experimental results show the characteristics of event evolution on different social media platforms and verify the efficacy of the proposed model.

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and seeking channel for health surveillance, in which users form interactive networks by posting and replying messages, providing and rating reviews, attending multiple discussion boards on health-related topics. Users' behaviors in these interactive networks reflect users' multiple interests. To provide better information service for users, it is necessary to analyze the user interactions and predict users' multi-interests. Most existing work in predicting users' multi-interests based on multi label network classification focuses on using approximate inference methods to leverage the dependency information to improve classification results. Inspired by deep learning techniques, DEEPWALK learns label independent latent representations of vertices in a network using local information obtained from truncated random walks, which provides an efficient way for predicting users multi-interests from user interactions. In this paper, we develop a user's multi-interests prediction model based on DEEPWALK, weight information of user interactions is considered when modeling a stream of short constrained random walks and SkipGram is employed to generate more accurate representations of user vertices, which help identify users' interests. Experimental results on two real world health-related datasets show the efficacy of the proposed model.

Review rating prediction is of much importance for sentiment analysis and business intelligence. Existing methods work well when aspect-opinion pairs can be accurately extracted from review texts and aspect ratings are complete. The challenges of improving prediction accuracy are how to capture the semantics of review content and how to fill in the missing values of aspect ratings. In this paper, we propose a novel review rating prediction method, which improves the prediction accuracy by capturing deep semantics of review content and alleviating data missing problem of aspect ratings. The method firstly learns the latent vector representation of review content using skip-thought vectors, a state-of-the-art deep learning method, then, the missing values of aspect ratings are filled in based on users? history reviewing behaviors, finally, a novel optimization framework is proposed to predict the review rating. Experimental results on two real-world datasets demonstrate the efficacy of the proposed method.

Images on the Web present a major accessibility issue for the visually impaired, mainly because the majority of them do not have proper captions. This paper addresses the problem of attaching proper explanatory text descriptions to arbitrary images on the Web. To this end, we introduce Phetch, an enjoyable computer game that collects explanatory descriptions of images. People play the game because it is fun, and as a side effect of game play we collect valuable information. Given any image from the World Wide Web, Phetch can output a correct annotation for it. The collected data can be applied towards significantly improving Web accessibility. In addition to improving accessibility, Phetch is an example of a new class of games that provide entertainment in exchange for human processing power. In essence, we solve a typical computer vision problem with HCI tools alone.

We introduce Peekaboom, an entertaining web-based game that can help computers locate objects in images. People play the game because of its entertainment value, and as a side effect of them playing, we collect valuable image metadata, such as which pixels belong to which object in the image. The collected data could be applied towards constructing more accurate computer vision algorithms, which require massive amounts of training and testing data not currently available. Peekaboom has been played by thousands of people, some of whom have spent over 12 hours a day playing, and thus far has generated millions of data points. In addition to its purely utilitarian aspect, Peekaboom is an example of a new, emerging class of games, which not only bring people together for leisure purposes, but also exist to improve artificial intelligence. Such games appeal to a general audience, while providing answers to problems that computers cannot yet solve.