- 권한신청
- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
초록
Abstract
This study mainly investigates the motivations of YouTube and Flicker users for posting videos or images/photos on each service. The motivational framework with ten factors such as enjoyment, self-efficacy, learning, personal gain, altruism, empathy, social engagement, community interest, reputation and reciprocity were used to test the motivations. Those who are users of YouTube and Flickr were recruited from Amazon Mechanical Turk to participate in online surveys. Findings show that learning and social engagement are the two most highly rated motivations. Altruism was rated relatively low, although it was strongly correlated with all other motivations. Personal gain was rated as the lowest by both users but Flickr users rated personal gain higher than YouTube users. Findings from this study could be applicable to specify user motivations for using the services and to upgrade the designs of the services in the future.
초록
Abstract
As IT technology develops rapidly and the personal dissemination of smart devices increases, video material is especially used as a medium of information transmission among audiovisual materials. Video as an information service content has become an indispensable element, and it has been used in various ways such as unidirectional delivery through TV, interactive service through the Internet, and audiovisual library borrowing. Especially, in the Internet environment, the information provider tries to reduce the effort and cost for the processing of the provided information in view of the video service through the smart device. In addition, users want to utilize only the desired parts because of the burden on excessive network usage, time and space constraints. Therefore, it is necessary to enhance the usability of the video by automatically classifying, summarizing, and indexing similar parts of the contents. In this paper, we propose a method of automatically segmenting the shots that make up videos by analyzing the contents and characteristics of language education videos and indexing the detailed contents information of the linguistic videos by combining visual features. The accuracy of the semantic based shot segmentation is high, and it can be effectively applied to the summary service of language education videos.