To answer this question, researchers conduct authorship attribution studies. These studies typically involve a dataset of texts written by different authors, and the task is to correctly attribute each text to its author based on its linguistic features. One common approach is to use a machine learning algorithm, such as a support vector machine (SVM) or a neural network, to classify texts based on their word frequencies or other linguistic features.
The number of words required for accurate authorship attribution depends on several factors, including the distinctiveness of the authors' writing styles, the length of the texts, and the specific NLP techniques used. In general, longer texts provide more information and thus require fewer words for accurate attribution. For example, a study by Moschitti and Sebastiani (2006) found that an SVM classifier could achieve an accuracy of over 90% in attributing English texts of 500 words or more to their authors. However, for shorter texts, such as tweets or emails, more words may be necessary for reliable attribution.
Another factor that influences the number of words required for authorship attribution is the linguistic diversity of the authors. If the authors have very similar writing styles, it may be more difficult to distinguish between them, even with a large number of words. On the other hand, if the authors have distinct writing styles, even a small number of words may be sufficient for accurate attribution.
In summary, the number of words required to identify an author using NLP techniques depends on several factors, including the text length, the distinctiveness of the authors' writing styles, and the specific NLP techniques used. While longer texts generally provide more information and require fewer words for accurate attribution, shorter texts may require more words to achieve reliable results.
