Complex Networks Measures for Differentiation between Normal and Shuffled Croatian Texts
This provides a method for detecting text shuffling in Croatian, though it is incremental as it applies existing network measures to a specific language.
The paper investigated whether complex network measures could differentiate between normal and shuffled Croatian texts, finding that standard co-occurrence network analysis showed no clear topological differences, but node selectivity values were consistently lower in shuffled texts.
This paper studies the properties of the Croatian texts via complex networks. We present network properties of normal and shuffled Croatian texts for different shuffling principles: on the sentence level and on the text level. In both experiments we preserved the vocabulary size, word and sentence frequency distributions. Additionally, in the first shuffling approach we preserved the sentence structure of the text and the number of words per sentence. Obtained results showed that degree rank distributions exhibit no substantial deviation in shuffled networks, and strength rank distributions are preserved due to the same word frequencies. Therefore, standard approach to study the structure of linguistic co-occurrence networks showed no clear difference among the topologies of normal and shuffled texts. Finally, we showed that the in- and out- selectivity values from shuffled texts are constantly below selectivity values calculated from normal texts. Our results corroborate that the node selectivity measure can capture structural differences between original and shuffled Croatian texts.