Search results

The purpose of this paper is to propose a method to detect local events in real time using Twitter, an online microblogging platform. The authors especially aim at detecting local events regardless of the type and scale.

The method is based on the observation that relevant tweets (Twitter posts) are simultaneously posted from the place where a local event is happening. Specifically, the method first extracts the place where and the time when multiple tweets are posted using a hierarchical clustering technique. It next detects the co-occurrences of key terms in each spatiotemporal cluster to find local events. To determine key terms, it computes the term frequency-inverse document frequency (TFIDF) scores based on the spatiotemporal locality of tweets.

From the experimental results using geotagged tweet data between 9 a.m. and 3 p.m. on October 9, 2011, the method significantly improved the precision of between 50 and 100 per cent at the same recall compared to a baseline method.

In contrast to existing work, the method described in this paper can detect various types of small-scale local events as well as large-scale ones by incorporating the spatiotemporal feature of tweet postings and the text relevance of tweets. The findings will be useful to researchers who are interested in real-time event detection using microblogs.

It has become common for children to browse web pages. However, there is no web browser that takes into account children's characteristics on information acquisition. Therefore, even though such general pages have a variety and detailed information, children cannot effectively use the internet with current web browsers, e.g. they have difficulty in understanding the contents and easily get bored when browsing general pages. The purpose of this paper is to propose a children‐oriented web browser, which aims to keep children's interest on pages and help them understand the contents of the pages.

The paper designed and implemented a web browser for children using a bubble metaphor, which converts general pages into a children‐friendly presentation. The browser is displayed in an undersea scene and presents contents of a web page in bubbles of different sizes, speeds, and colors. Furthermore, it presents the details of the content in a picture book style in a way that children can easily understand.

The paper conducts a user experiment with 13 children between four and ten years of age. The experimental results show that the browser changes general pages into a children‐friendly presentation and make a web browsing fun for children.

To the best of the authors' knowledge, this is the first investigation into the web browsing characteristics of children. The findings may be useful to researchers who are interested in the relationships between children and the web, as well as information acquisition of children.

Recently, the importance and effectiveness of Wikipedia Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic relatedness measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.

The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another article represents a semantic relation between these two concepts. Thus, the co‐occurrence of a link pair indicates the relatedness of a concept pair. Furthermore, the authors propose an integration method with tfidf as an improved method to additionally leverage local information in an article. Besides, for constructing a new test collection, the authors select a large number of concepts from Wikipedia. The relatedness of these concepts is judged by human test subjects.

An experiment was conducted for evaluating calculation cost and accuracy of each method. The experimental results show that the calculation cost of this approach is very low compared to one of the previous methods and more accurate than all previous methods for computing semantic relatedness.

This is the first proposal of co‐occurrence analysis of Wikipedia links for semantic relatedness measurement. The authors show that this approach is effective to measure semantic relatedness among concepts regarding calculation cost and accuracy. The findings may be useful to researchers who are interested in knowledge extraction, as well as ontology researches.