In this study we investigate whether Facebook fan-page posting types and topics have a significant effect on engagement. More specifically, the media type and content theme of posting on Facebook are examined to see whether or not there was a difference between content topics. In order to achieve this goal, we set hypotheses as follows: (1) the media types of posting have a significant effect on engagement; (2) the topics and sentiment polarity of posting have a significant effect on engagement. We tested these hypotheses using research procedures as follows: (1) collection and preprocessing of social-media data, including posting types, comments, and reactions on Facebook fan pages, (2) topic modeling of fan-page postings using R and SAS, (3) testing hypotheses using a negative binomial regression model, and (4) implications and insights for social-media marketing. Topic modeling applying to textual data and sentiment analysis were conducted. After that, in order to find the factors to affect the number of Facebook fan-page engagements, the negative binomial regression model including post type, topic, sentiment, reactions of “love,” “haha,” and their interaction as exploratory variables was considered. Finally, the results show that post type is the most influential factor to affect social-media engagement, and content topics, sentiments of posts and comments also have significant effects on it.
Due to the rapid increase in images and image data, research examining the visual analysis of such unstructured data has recently come to be actively conducted. One of the representative image caption models the DenseCap model extracts various regions in an image and generates region-level captions. However, since the existing DenseCap model does not consider priority for region captions, it is difficult to identify relatively significant region captions that best describe the image. There has also been a lack of research into captioning focusing on the core areas for story content, such as images in movies and dramas. In this study, we propose a new image captioning framework based on DenseCap that aims to promote the understanding of movies in particular. In addition, we design and implement a module for identifying characters so that the character information can be used in caption detection and caption improvement in core areas. We also propose a core area caption detection algorithm that considers the variables affecting the area caption importance. Finally, a performance evaluation is conducted to determine the accuracy of the character identification module, and the effectiveness of the proposed algorithm is demonstrated by visually comparing it with the existing DenseCap model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.