YouTube is the world's largest video sharing platform where both professional and non-professional users participate in creating, uploading, and viewing content. In this work, we analyze content in the music category created by the nonprofessionals, which we refer to as user-generated content (UGCs). Non-professional users frequently upload content (UGCs) that are parodies, remakes, or covers of the music videos uploaded by professionals, namely the official record labels. Along with the success of official music videos on YouTube, we find the increased participation of users in creating the UGCs related to the music videos. In this study, we characterize the UGC uploading behavior in terms of what, where, and when. Furthermore, we measure the relationship between the popularity of the original content and creation of the related UGCs. We find that the UGC uploading behavior is different depending on the types of the UGC and across different genres of music videos. We also find that UGC sharing is a highly global activity; popular UGCs are created from all over the world despite the fact that the popular music videos originate from a very limited number of locations. Our findings imply that utilizing the information on re-created UGCs is important in order to understand and to predict the popularity of the original content.

As the number of social networking services (SNS) and their users grow, so does the complexity of individual networks as well as the amount of information to be consumed by the users. Users of SNS exchange short and instantaneous messages interactively, which can be seen as conversations. We explore this conversational aspect of SNS and show how refined topic-based semantic social networks can be formed in order to reduce the complexity and information overload. Among other possibilities, we use the notion of topic diversity and topic purity of SNS conversations between two users and show different types of social relationships can be identified in that they break down a huge “syntactic” social network into topic-based ones based on different interaction types. Resulting semantic social networks can be useful in designing various targeted services on online social networks.

Influential people are known to play a key role in diffusing information in a social network. When measuring influence in a social network, most studies have focused on the use of the graph topology representing a network. As a result, popular or famous people tend to be identified as influencers. While they have a potential to influence people with the network connections by propagating information to their friends or followers, it is not clear whether they can indeed serve as an influencer as expected, especially for specific topic areas. In this paper, we introduce the notion of dedicators, which measures the extent to which a user has dedicated to transmit information in selected topic areas to the people in their egocentric networks. To detect topic-based dedicators, we propose a measure that combines both community-level and individual-level factors, which are related to the volume and the engagement level of their conversations and the degree of focus on specific topics. Having analyzed a Twitter conversation data set, we show that dedicators are not co-related with topology-based influencers; users with high in-degree influence tend to have a low dedication level while top dedicators tend to have richer conversations with others, taking advantage of smaller and manageable social networks.

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The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro-genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.