• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.89-96
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• 2018

In online communities, a large number of participants can exchange their opinion using replies without time and space restrictions. While the online space provides quick and free communication, it also easily triggers unnecessary quarrels and conflicts. The network established on the discussion participants is an important cue to analyze the confrontation and predict serious disputes. In this paper, we present a quantitative measure for polarity observed on the discussion network built from reply exchanges in online communities. The proposed method uses the comment exchange information to establish the user interaction network graph, computes its maximum spanning tree, and then performs vertex coloring to assign two colors to each node in order to divide the discussion participants into two subsets. Using the proportion of the comment exchanges across the partitioned user subsets, we compute the polarity measure, and quantify how discussion participants are bipolarized. Using experimental results, we demonstrate the effectiveness of our method for detecting polarization and show participants of a specific discussion subject tend to be divided into two camps when they debate.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.483-491
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• 2013

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.