Analyzing market performance via social media: a case study of a banking industry crisis

Analyzing market performance via social media has attracted a great deal of attention in the finance and machine-learning disciplines.However,the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market.This article aims to address these challenges by proposing a multistage dynamic analysis framework.In this framework,we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm.We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis’s influence on various social media parameters.Then,we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis.Finally,we discuss some interesting and significant results,which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis.     

A Comparison of Tools for Detecting Fake Websites

As fake Website developers become more innovative, so too must the tools used to protect Internet users. A proposed system combines a support vector machine classifier and a rich feature set derived from Website text, linkage, and images to better detect fraudulent sites.     

A Discrete Stock Price Prediction Engine Based on Financial News

The Arizona Financial Text system leverages statistical learning to make trading decisions based on numeric price predictions. Research demonstrates that AZFinText outperforms the market average and performs well against existing quant funds.     

Digital libraries: technological advances and social impacts

Public awareness of the Net as a critical infrastructure in the 1990s has spurred a new revolution in the technologies for information retrieval in digital libraries. The paper discusses a range of research projects that investigate the development and usage of new information technology for substantial collections     

AI and Global Science and Technology Assessment

Strength in science & technology (S&T) is the foundation of a nation's economic power, so an effective, automated means of continually assessing this strength is critical to understanding a country's economic status. Six essays on global S&T assessment present various research frameworks, computational methods, issues, and results relative to knowledge mapping, scientometrics, information visualization, digital libraries, and multilingual knowledge management.     

A comparison of fraud cues and classification methods for fake escrow website detection

The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud. Despite research on related topics, such as web spam and spoof site detection, fake escrow website categorization has received little attention. The authentic appearance of fake escrow websites makes it difficult for Internet users to differentiate legitimate sites from phonies; making systems for detecting such websites an important endeavor. In this study we evaluated the effectiveness of various features and techniques for detecting fake escrow websites. Our analysis included a rich set of fraud cues extracted from web page text, image, and link information. We also compared several machine learning algorithms, including support vector machines, neural networks, decision trees, naïve bayes, and principal component analysis. Experiments were conducted to assess the proposed fraud cues and techniques on a test bed encompassing nearly 90,000 web pages derived from 410 legitimate and fake escrow websites. The combination of an extended feature set and a support vector machines ensemble classifier enabled accuracies over 90 and 96% for page and site level classification, respectively, when differentiating fake pages from real ones. Deeper analysis revealed that an extended set of fraud cues is necessary due to the broad spectrum of tactics employed by fraudsters. The study confirms the feasibility of using automated methods for detecting fake escrow websites. The results may also be useful for informing existing online escrow fraud resources and communities of practice about the plethora of fraud cues pervasive in fake websites.     

Introduction to the Journal on Digital Libraries special issue on Asian digital libraries

International Journal on Digital Libraries -     

Analyzing sentiments in Web 2.0 social media data in Chinese: experiments on business and marketing related Chinese Web forums

Web 2.0 has brought a huge amount of user-generated, social media data that contains rich information about people’s opinions and ideas towards various products, services, and ongoing social and political events. Nowadays, many companies start to look into and try to leverage this new type of data to understand their customers in order to make better business strategies and services. As a nation with rapid economic growth in recently years, China has become visible and started to play an important role in the global business and economy. Also, with the large number of Chinese Internet users, a considerable amount of options about Chinese business and market have been expressed in social media sites. Thus, it will be of interest to explore and understand those user-generated contents in Chinese. In this study, we develop an integrated framework to analyze user sentiments from Chinese social media sites by leveraging sentiment analysis techniques. Based on the framework, we conduct experiments on two popular Chinese Web forums, both related to business and marketing. By utilizing Elastic Net together with a rich body of feature representations, we achieve the highest F-measures of 84.4 and 86.7 % for the two data sets, respectively. We also demonstrate the interpretability of Elastic Net by discussing the top-ranked features with positive or negative sentiments.