2017/05/11 by Camilo Akimushkin, Diego R. Amancio, Osvaldo N. Oliveira +1 · 39 citations
Computer Science · Physics and Astronomy · Social Sciences · #Artificial intelligence #Authorship Attribution and Profiling #Bigram #Computer science #Data mining #Information retrieval #Linguistics #Measure (data warehouse) #Misinformation and Its Impacts #Natural language processing #Network structure #Opinion Dynamics and Social Influence #Semantic network #Semantics (computer science) #Similarity (geometry) #Theoretical computer science #Viewpoints #cs.CL #cs.SI
paper · pdf · open access · doi:10.1016/j.physa.2017.12.054
published in Physica A Statistical Mechanics and its Applications 495, 49-58 (Elsevier BV)
arxiv created 2017/05/11 · openalex publication_date 2017/12/19 · arxiv updated 2018/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Well-established automatic analyses of texts mainly consider frequencies of linguistic units, e.g. letters, words and bigrams, while methods based on co-occurrence networks consider the structure of texts regardless of the nodes label (i.e. the words semantics). In this paper, we reconcile these distinct viewpoints by introducing a generalized similarity measure to compare texts which accounts for both the network structure of texts and the role of individual words in the networks. We use the similarity measure for authorship attribution of three collections of books, each composed of 8 authors and 10 books per author. High accuracy rates were obtained with typical values from 90% to 98.75%, much higher than with the traditional the TF-IDF approach for the same collections. These accuracies are also higher than taking only the topology of networks into account. We conclude that the different properties of specific words on the macroscopic scale structure of a whole text are as relevant as their frequency of appearance; conversely, considering the identity of nodes brings further knowledge about a piece of text represented as a network.