Abstract
The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.
Highlights
The current massive production of data has brought up plenty of challenges to the areas of Data Mining, Natural Language Processing (NLP) and Machine Learning
Before presenting the results of the classification based on the Life-Like Network Automata (LLNA) approach, we first address the LLNA rule selection, which is detailed
We have addressed the authorship attribution problem, which is a task of practical relevance in many contexts of information science research
Summary
The current massive production of data has brought up plenty of challenges to the areas of Data Mining, Natural Language Processing (NLP) and Machine Learning. An example of a current challenge in information sciences is the authorship attribution task, which amounts to the ability to assign authorship to anonymous or disputed documents. This task has drawn attention from researchers mostly for its implications in real applications, such as plagiarism detection [1, 2], forensics against cyber crimes [3] and resolution of disputed documents [4]. Traditional techniques use text analytics and natural language processing concepts to characterize authors’ writing styles [4]. Deeper paradigms have been employed to tackle
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