Chat-log data is a little used resource for analyzing human communication in social networks. Some statements in this data do not include the intended username of a receiver or any variant of it, and thus are termed "misaddressed statements". Constructing social networks from such a semi-structured data and subsequent analyzing require a reliable process to make sure that the social network representation is as truthful as possible. Due to the large size of data, human assignment of statements to receivers is prohibitive. In this paper, we present and evaluate different methods to reliably predict a receiver for these misaddressed statements. We use a set of prediction rules which follow human communication behavior in a group chat and we show their success in constructing social networks.
Abstract-To understand a node's centrality in a multiplex network, its centrality values in all the layers of the network can be aggregated. This requires a normalization of the values, to allow their meaningful comparison and aggregation over networks with different sizes and orders. The concrete choices of such preprocessing steps like normalization and aggregation are almost never discussed in network analytic papers. In this paper, we show that even sticking to the most simple centrality index (the degree) but using different, classic choices of normalization and aggregation strategies, can turn a node from being among the most central to being among the least central. We present our results by using an aggregation operator which scales between different, classic aggregation strategies based on three multiplex networks. We also introduce a new visualization and characterization of a node's sensitivity to the choice of a normalization and aggregation strategy in multiplex networks. The observed high sensitivity of single nodes to the specific choice of aggregation and normalization strategies is of strong importance, especially for all kinds of intelligence-analytic software as it questions the interpretations of the findings.
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