Building the Multi-layer Theory of Association Semantic based on the Power-law Distribution of Linking Keywords

Abstract-Web information contain plentiful, significant knowledge which is eager to be explored by users. Effective semantic layered technology not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of the related information system. This paper builds the multi-layer theory of association semantic based on the power-law distribution of linking keywords. First, some experiments of four types of keywords with different linking role are done to discover the possible distribution law. Experiment results show that four types of keywords are all reveal power-law distribution. Then, based on the discovered power-law distribution, the multi-layer theory of association semantic is built. The multi-layer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle size on Association Link Network (ALN). Keywords-Association Link Network, power-law distribution, multi-layer theory of association semantic, knowledge discovery in Web resources

Electronic word of mouth in online social networks: strategies for coping with opportunities and challenges

In today's world, the widespread success of the Internet, social media, and online social networks (OSN) provide the basis for electronic word of mouth (EWOM). EWOM can be seen as a digital enhancement of traditional word of mouth that makes communication more efficient and involves less effort by its users. The resulting speed of diffusion and information transparency have caused transformative changes in consumer behaviour in all types of markets, which requires the development of new business strategies for adequately dealing with the new circumstances. This doctoral dissertation is divided into three overall subject areas that concern the investigation of capable strategies for coping with the emerged opportunities and challenges of EWOM in OSN. The first subject area concerns negative electronic word of mouth in OSN and investigates capable countermeasure strategies for firms to adequately address claims of unsatisfied customers. For this, three simulation studies are conducted in which the propagation of a negative message and its countering by a positive message published by the firm are numerically analysed. The results reveal that, in general, the persuasiveness of a firm's response is more important than a quick response with a less persuasive counter-message. To some extent, this also holds if the number of OSN members who initially disseminate the counter-message on behalf of the firm is increased. In the second subject area, an optimisation model for individualised pricing is developed for an online store whose customers are interconnected in an OSN and can share price information via EWOM. The model is solved numerically by artificial intelligence solution methods. The results indicate that personalised prices can be financially worthwhile even under price transparency. The third subject area investigates market entry strategies for social media apps and services that are advertised in an OSN for acquiring new users and examines the role of EWOM in this context. A diffusion model is developed and analysed numerically by simulation. Three different targeting approaches are compared to each other regarding their ability to reach a high share of active users in the OSN: (1) a random marketing strategy, where randomly chosen members in the OSN are presented the advertisement, (2) cluster marketing, where whole clusters of members who are densely connected to each other are simultaneously shown the advertisement, and (3) influencer marketing, where the most influential users in the OSN are selected to share sponsored posts about the app in the OSN. The results suggest that EWOM can have detrimental effects if OSN members are too early informed about the app or service. If the information about the app reaches clusters in the OSN prematurely where a sufficient level of activity is not present yet, it can deplete the excitement of the users. The lack of excitement, in turn, can significantly reduce the effect of subsequent marketing campaigns. However, if applied appropriately, a higher level of EWOM about the app or service can increase the performance of the random marketing strategy to the extent that it outperforms cluster and influencer marketing

Discovering small-world in association link networks for association learning

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of "learning from Web resources"). First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners. © 2012 Springer Science+Business Media, LLC