Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A review on application of data mining techniques to combat natural disasters

The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.

A new direction in social network analysis: Online social network analysis problems and applications

Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter)

In the midst of today’s pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g., rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation in modeling methodologies, this paper explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes whether a user tending to spread a rumor message is dependent on specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, the information propagation patterns of rumors versus those of credible messages in a social media environment are differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation and modeling approach. By applying the new approach, we are able to differentiate rumors from credible messages through observing distinctions in their respective propagation patterns in social media. The experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content. Our experimental findings further show that rumors are more likely to spread among certain user groups.

Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment

When a disaster occurred, people try to acquire information through social media such as electronic bulletin boards, SNSs, and video services. Their decision-making tend to be affected by social media information. Sometimes, social media information is useful, so people can get valuable knowledge. On the other hand, social media information is occasionally unreliable. Harmful rumors spread and cause people to panic. In today's information oriented society, a mechanism to detect rumor information in social media has become very important. This paper proposes a framework to detect the rumor information in social media. Our proposed framework clarifies topics in social media, visualizes topic structures in time series variation. Then it extracts rumor candidates and seeks related information from other media such as TV program, newspapers and so on in order to confirm the reliability of rumor candidates. By our framework, potential rumors will be shown.

Rumor analysis framework in social media

In this paper, we will analyze monetary policy of the Bank of Japan, the central bank of Japan, under the second Abe Cabinet term which started in December 2012. The Bank set the "price stability target" at 2 percent in terms the year-on-year rate of change in the consumer price index in January 2013 and has made a commitment to achieving this target at the earliest possible time. The Abe Cabinet's policy feature was monetary easing from the start. At the Monetary Policy Meeting held on October 31, 2014, the Bank decided monetary easing by accelerating the pace of increase in the monetary base and by increasing asset purchases and extending the average remaining maturity of Japanese government bond purchases. We analyzed the Monetary Policy Meeting minutes by text mining technologies. Especially we conducted a topic extraction using the Latent Dirichlet Allocation model from the Meeting minutes from January 2013 to June 2014. The extracted topics clearly showed their monetary easing policies. In addition, we found that a topic corresponding to the consumption tax hike held on April first, 2014 of which topic ratio increased in the last of 2013.

Monetary Policy Topic Extraction by Using LDA: Japanese Monetary Policy of the Second ABE Cabinet Term

In this paper, we will analyze monetary policy of the Bank of Japan, the central bank of Japan, just after the sales tax hike held in April 2014, through November 2014. The period matches the Japan’s turning point to the recession owing to the sales tax hike in April 2014. With the Abe second Cabinet which started in December 2012, the Bank set the “price stability target” at 2 percent. We analyzed the Monetary Policy Meeting minutes by text mining technologies. Especially we conducted a topic extraction using the Latent Dirichlet Allocation model from the Meeting minutes. The extracted topics clearly showed the impact of the sale tax hike. The biggest topic was the economic conditions related topic and its ratio peaks corresponded to the time of Japan’s GDP data released. In addition, we found that the money easing policy topic ratio increased after the tax hike and that the economic growth related topic ratio showed the decline after the tax hike.

Topic Extraction Analysis for Monetary Policy Minutes of Japan in 2014

Latent Semantic Analysis (LSA) is a widely used method in text mining fields to extract the latent concept. The mathematical technique behind LSA is Singular Value Decomposition (SVD) in which the key concept is the eigen values. It is difficult to understand the underlying mathematics for general people, not proficient in mathematics. One reason might be that the linear algebra textbooks available in the market are not written for non -- mathematics majors. We have proposed a visualization of the mathematical process behind LSA to make it easily understandable to people, novice in mathematics. In this paper, we proposed visualization of the eigen values and eigenvectors.

Visual Explanation of Mathematics in Latent Semantic Analysis

Once a disaster occurs, people discuss various topics in social media such as electronic bulletin boards, SNSs and video services, and their decision-making tends to be affected by discussions in social media. Under the circumstance, a mechanism to detect topics in social media has become important. This paper targets the East Japan Great Earthquake, and proposes a time series topic transition discovering method in social media. Our proposed method adopts directed graphs to show topic structures in social media, and then form clusters using modularity measure which expresses the quality of a division of a network into modules or communities. The method computes topic transition using the Matthews correlation coefficient which is a measure of the quality of two binary classifications, and analyzes them over time. An experimental result using actual social media data about the East Japan Great Earthquake is shown as well.

Yukari Shirota

Papers

Rumor analysis framework in social media

Monetary Policy Topic Extraction by Using LDA: Japanese Monetary Policy of the Second ABE Cabinet Term

Topic Extraction Analysis for Monetary Policy Minutes of Japan in 2014

Visual Explanation of Mathematics in Latent Semantic Analysis

Discovering Topic Transition about the East Japan Great Earthquake in Dynamic Social Media