Band 5, Heft 6

In this era of a data-driven society, useful data (Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information, however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.

Chauffeured car service (CCS) has developed rapidly in recent years. Although CCS brings convenience and effectiveness, it also triggers some new problems like vicious competition. This work studies the duopoly competition between CCS company and taxi company, in terms of different average cost and number of vehicles of two companies. To find the solutions like the pricing scheme to ease the vicious competition, Hotelling model is introduced. The Hotelling-type model is used to present passengers’ preferences to the companies. Besides, failing situation is taken into account in the Hotelling model to describe the situation where passenger’s demand is not satisfied in reality. This work theoretically analyzes the price scheme and equilibrium market segmentation based on the average cost and number of vehicles of each company. Furthermore, companies’ profits, passengers’ utility and social welfare in equilibrium are revealed based on the game theory. The study shows that both of companies can get optimal profits by setting effective price scheme. The company which has higher price, definitely gets less market share. In addition, moderate competition can lead to positive influence on social welfare.

Major societal problems affect the social stability. It is necessary to understand the public opinion toward those issues to avoid social conflicts. Nowadays the social media become the major platform to track what the public is concerned about and which may be of the societal risk. However, it is very tough to capture the public attention in short time due to huge flow of user-generated contents. In this paper, we approach this problem by expanding the method of generating storyline with the result displayed by a multi-view graph. One real-world example is illustrated and evaluation is given to show the effectiveness of the proposed method.

The study of human mobility patterns is of both theoretical and practical values in many aspects. For long-distance travel, a few research endeavors have shown that the displacements of human travels follow a power-law distribution. However, controversies remain regarding the issue of the scaling laws of human mobility in intra-urban areas. In this work, we focus on the mobility pattern of taxi passengers by examining five datasets of three metropolitans. Through statistical analysis, we find that the lognormal distribution with a power-law tail can best approximate both the displacement and the duration time of taxi trips in all the examined cities. The universality of the scaling laws of human mobility is subsequently discussed, in view of the analysis of the data. The consistency of the statistical properties of the selected datasets that cover different cities and study periods suggests that, the identified pattern of taxi-based intra-urban travels seems to be ubiquitous over cities and time periods.

This paper focusses on the information asymmetry in crisis news after a serious incident in Tianjin, China, in 2015. The incident caused enormous damage and resulted in societal unrest because of the lack of reliable information from the formal media channels. Social media — micro blogs — played a major role in reporting on crisis situations. We divided netizens (i.e., the citizens of the net) into high and low types according to their information-critical level to the crisis news. The data shows information deterioration on the crisis news, related to the netizens’ information-critical level. For the traditional media there is the opportunity to use information quality distortion to make more marginal profits. This is possible only if the citizens’ information stays under a certain quality level. The result is overprovision of low quality news and high quality news driven out of the market, whereupon adverse selection (i.e., a lack of symmetric information) appears. However, by adopting a process view, we found self-correcting mechanism (i.e., dying out of rumors) of the social media communities in China. We provided a agent-base model and simulation to show that the more media exist in the market, the faster speed of the information deterioration, but also the capacity to ‘discuss’ rumors.

To simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.