Semantic Network Analysis of Islamist Sources Using Time Slices as Nodes and Semantic Similarity as Link Strengths: Some Implications for Propaganda Analysis about Jihad

This research analyzes Muslim nation (MN) networks associated with Jihad for the previous two years. We captured all documents from Lexis-Nexis Academic's BBC International Monitoring - which contains translated transcriptions of web pages, broadcasts, newspapers, and other content - for each of 47 Muslim nations (MNs) using the search term: jihad and MN name. We presented a new kind of semantic network time series analysis of this text. Unlike most semantic network analysis, our nodes were time segments, not words. The link strengths were similarity scores of time nodes across 779,192 word pairs. The time nodes were 105 weekly intervals. We created a two-mode matrix. Columns were the frequencies of time slices' word pairs, appearing in a three-word window. Matrix rows were three-quarters of a million word pairs extracted from the aggregate two-year text file. We converted this two-mode matrix to a one-mode matrix by computing the similarity of each pair of time slices across the rows of word pairs. This resulted in a one-mode network of 105 by 105 time units. Pearson correlations were the similarity coefficients. We conducted social network analysis of the time nodes to find the most central ones. Highly central nodes lie more often on the shortest paths between all pairs of time nodes. They therefore contain in their internal lists of highest frequency word pairs the main themes across the two-years of text. The method is highly automated and efficient. In this case only three central nodes provided the basis for an analyst's interpretations of main propaganda themes.