Development and Evaluation of a Character Social Network Relationship Map Tool in an Ancient Book Digital Humanities Research Platform This study aims to develop a character social network relationship map tool (CSNRMT) for supporting an ancient book digital humanities research platform to semiautomatically assist humanists through humancomputer interaction in more efficiently and accurately exploring the character social network relationships from texts for useful research findings. With a quasiexperimental design, the experimental results reveal that the use of the ancient book digital humanities research platform with the CSNRMT appears higher effectiveness on the interpretation of characters and character social network relationships than the one without the CSNRMT, but does not achieve significantly statistical difference. Using the CSNRMT to interpret characters and character social network relationships presents remarkably higher technology acceptance of perceived usefulness and perceived ease of use than the one without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could actually assist in the interpretation of character social network relationships. The interview results present positive opinions on the integration of system interface, the smoothness of operation, and the external search function. The CSNRMT developed in this study would be more practical when the offered information about character names and character social network relationships is more accurate and broad.

Interactive Important Social Character Identification from Large Photo Collections In the paper, we describe a mechanism to identify important social characters by analyzing the social structures embedded in photo collections. We first construct a weighted undirected graph from photo collections by examining the coappearance of individuals in photos, wherein the weights of edges are measures of the social closeness of the involved individuals (vertices in the graph). Then a graph clustering algorithm that maximizes the modularity of the graph partition is applied to detect the embedded social clusters. Once the social clusters are identified, we measure individualu0027s contribution to the formation of a social cluster to quantify the social importance of each character in the cluster. To compensate the discrepancy between the userperceived important social characters and the algorithmically computed ones, we propose an interactive browsing scheme to enable viewers quickly identify import social characters that conform to their subjectivity. The effectiveness of the proposed mechanism is demonstrated through experiments on consumer photo collections.