WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors (2020)

Abstract: This is a pilot study that aims to explore the potential of using WEKA in forensic authorship analysis. It is a corpus-based research using data from Twitter collected from thirteen authors from Riyadh, Saudi Arabia. It examines the performance of unbalanced and balanced data sets using different classifiers and parameters of word grams. The attributes are dialect-specific linguistic features categorized as word grams. The findings further support previous studies in computational authorship identification.

## related literature this section discusses the notion of idiolect and related literature of artificial intelligence in authorship analysis using short texts. ## methodology and corpus design this section will demonstrate the nasct corpus design, the data collection process, the sample selected for the study, and the pre-processing of the data and training weka. ## findings and discussion to examine different classifiers to see which performs most accurately, the authors ran a test using seven classifiers: linear svm, multinominal naïve bayes, decision tree j-48, knn @xmath0, knn @xmath1, random forest @xmath2, and random forest @xmath3. the results of the first test show that linear svm scores the highest accuracy rates, therefore it was implemented in the next stage. the authors ran three tests to explore a range of parameters that can ensure the highest accuracy rates. in the first range, the minimum value is words that appear once and words that appear in 60% in the data files (@xmath4. the second one eliminates words that appear twice or less and words that occur in 80% of the data files (@xmath5. the last parameter test eliminates words that appear once and words that appear in 95% of the data files (@xmath6. in the first parameter test, both data sets scored the highest accuracy rates. the scores were most accurate across the three n-gram categories (0.59-0.6 respectively). the first data set ts1 scored a consistent and better performance compared to ts2 in the second parameter. the accuracy rates of the balanced data ts2 in the second parameter test were inconsistent. on the other hand, ts2 scored consistently higher results in the third parameter test compared to the unbalanced data set ts1. nonetheless, both training data sets scored consistent results in unigrams, bigrams, and trigrams. furthermore, it appears that the balanced data set scores the highest overall results in unigrams, while the unbalanced data set scores the highest overall results in bigrams. however, the optimum parameter is the first test using bigrams. ## conclusion and future studies this pilot study aimed to explore weka in forensic authorship analysis research. to answer the first question, the authors found that the unbalanced data set performed better than the smaller, balanced one. the large the size of the data provides weka with training and recognizing the features more accurately. as for which classifier performs best, results show that linear svm has the most accurate performance. this conforms to the findings of @xcite and @xcite . lastly, the results show that bigrams can accurately identify authorship, which confirms the findings of @xcite .