Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
Table 3: Micro-averaged F1 scores of our CNN variants and state-of-the-art authorship attribution methods for 10 and 50 authors (except for IMDb) in the chosen domains. We also show a model’s average performance (Ave) across all scores to give an estimate of how well a model generalizes across domains and consequently might perform in a new domain.
['[BOLD] Model Number of authors', 'Emails 10', 'Emails 50', 'IMDb 62', 'Blogs 10', 'Blogs 50', 'Twitter 10', 'Twitter 50', 'reddit 10', 'reddit 50', 'Ave -']
We evaluate all algorithms on all 62 authors for the IMDb dataset and on 10 and 50 authors respectively for all other datasets. They outperform comparison methods in domains such as Reddit where certain character sequences such as smileys and emoticons are discriminatory by more than 12 %. for 10 authors. Hybrid-channel CNNs outperform CNNs that rely solely on word embeddings, as character-level information is important for the task of authorship attribution. Using a static and a non-static word channel together with the character channel had similar effects as using a multi-channel architecture over a regular word-based CNN, i.e. it increased scores on some datasets. We have found that restricting the vocabulary size by selecting only the 30,000 most frequent space-free character n-grams for the Imposters method generally increased performance as frequency is the most important criterion for selecting features in authorship attribution Even though we improve performance for Imposters on the IMDb dataset in comparison to Seroussi et al.