Authorship Verification for A rabic Short Texts Using A rabic Knowledge-Base Model ( A ra KB ) (2022)

Abstract: Arabic is a Semitic language, considered to be one of the most complex languages in the world due to its unique composition and complex linguistic features. It consequently causes challenges for verifying the authorship of Arabic texts, requiring extensive research and development. This paper presents a new knowledge-based model to enhance Natural Language Understanding and thereby improve authorship verification performance. The proposed model provided promising results that would benefit the Arabic research for different Natural Language Processing tasks.

## introduction arabic has a very rich vocabulary; each word has many derivatives that describe the root meaning in more specific or nuanced ways. for example, the word (thirst ‫)ﻋﻄﺶ/‬ has up to 45 synonyms, including those alluding to different stages of thirst. compared to other languages, arabic's structure increases the complexity of preprocessing, whereby unnecessary characters must be removed or carefully replaced. moreover, the complexity of arabic morphology tends to increase the set of features, syntax, and semantic structures, which might not be effective for the purposes of authorship verification (av), the process of determining whether or not two pieces of writing are written by the same author by comparing their writing styles @xcite . although extensive research has been carried out on av in different languages, few studies have focused on the arabic language. av in arabic entails numerous particular linguistic difficulties, including with regard to inflection, elongation, diacritics, word length @xcite , and other challenges, as described below: inflection: in arabic, one word can generate three or more different words with minor change, therefore orthographical properties result in lexical variation. when the number of features increases, then determining the right number of features may impact authorship analysis performance @xcite . elongation: proper pre-processing is necessary to remove unnecessary characters, but this may lose word emphasis or stylometric features of writers @xcite . diacritics: less effective when using word-based syntactic features @xcite . word length: shorter word lengths in arabic (e.g., compared to english) reduces the effectiveness of lexical features @xcite . diglossia: may not provide significant features or an adequate ontology to provide proper mappings @xcite . grammatical structure: arabic dialect analysis creates more challenges. capitalisation and punctuation: identifying patterns is challenging @xcite . agglutinative constructs: difficult parts of speech tagging could degrade the stylometric features of av @xcite . in addition to the previous challenges, as av mostly depends on the style of writing, minimal data pre-processing is required. unlike most nlp tasks, such as sentiment analysis and text classification, av problems cannot undergo extensive data pre-processing, as stemming, normalization, diacritics removal, and other preprocessing techniques would eliminate the author's style of writing, and therefore make av more challenging. the challenge becomes even greater with authorship analysis tasks for very short texts @xcite , such as those on social media platforms. hence, a minimal number of arabic av studies have been conducted due to the inherent difficulty of such undertakings. consequently, there is a need to investigate different linguistic features that could help to improve performance of arabic av for arabic short texts (particularly twitter posts). this paper presents a novel method and a new presentation of data to verify the authorship of arabic texts specifically on twitter; however, the experiment in principle could be applicable to other social media platforms and any short arabic texts. the following section presents a brief overview of the recent work on arabic av, then section 3 explains the research methodology used in this work. section 4 presents the experimental setup and results, and section 5 discusses the results of the experiments. finally, section 6 summarizes the main conclusions and identifies areas for future investigation. ## related work as mentioned earlier, there are few studies of arabic av, and those which have been undertaken mainly analyzed very long texts, such as novels @xcite and other books @xcite . in the following we will review the ones conducted on medium to short texts, as they are more relevant to the current study (which pertains to tweets). an extensive set of documents was collected from dar al-ifta, 1 consisting of 3,000 balanced datasets and 4,686 documents from unbalanced datasets @xcite . the method is based on the 1 https://www.daralifta.org/foreign/default.aspx frequency-based features of unigrams, bigrams, and trigrams, and on style-based features (character, lexical, syntactic, semantic, contentspecific, structural, and language-specific). first, the data were filtered, and tfidf vectors were created. a bootstrap aggregating learner was then used to estimate the classification based on a maximum number of votes technique. several stylometric and frequency-based features were used, showing that combining the bigram model with style-based features achieved the highest accuracy. however, it was unclear whether authors' documents were used in training or chunking in such lengthy article datasets. two experiments by @xcite sought to find the best feature ensemble, using the features of tokens, stems, root, diacritics, and pos tags of n-grams (1 to 4) as features for arabic author verification. the author used a dataset consisting of 253 documents written by different authors from five domains. the average document sizes for the studied domains were 802 for columnists, 820 for economics, 1159 for fiction, 1108 for nonfiction, and 850 for politics. the accuracy for each domain varied from 80-84.53%. it is important to note that domains with the smallest sample size achieved the worst results. the second experiment was to find the effect of training or testing sample size, and it revealed that the training dataset size did not correlate with improved accuracy for the av method. in conclusion, the study found that a training set with a smaller number of documents outperformed one with a larger number of documents. arabic av using 125 documents from five common genres in modern standard arabic (msa), including opinion columns, economics, fiction, nonfiction, and politics, was undertaken by evaluating svm-calculated distance metrics of the canberra, manhattan, cosine, and jaccard measures using tokens, stems, and pos tags as features @xcite . it was found that the canberra distance measure was the bestperforming distance measure in most genres, with an accuracy rate as high as 97.8%. however, the method omits digits, punctuation marks, and special characters in pre-processing, which limits the applicability of these findings to short texts. in our recent work on arabic av @xcite , we collected a dataset consisting of 100 twitter users written in the arabic language, whereby each user had 1000 to 3000 tweets. firstly, we extracted a number of sylometric (content-free) features compatible with both the arabic language and with twitter posts. comparing different classifiers, we found that gradient boosting, with an average accuracy of 0.75, outperformed random forest, support vector machine, and k-nearest neighbor. in the second experiment, the effect of combining content-specific features (e.g., tf-idf) with the extracted stylometric features was tested, which improved the accuracy by almost 2%. the performance of using a combination of stylometric and tf-idf resulted in 0.77 average accuracy and f1-score. in general, it can be concluded that authorship analysis tasks depend on the feature set, the number of authors, and the dataset genres that reflect the arabic language type (classical, msa, or colloquial). in addition, the changing behavior of authors is an inherent problem that affects solving authorship verification problems. the root cause of the limited number of works on arabic-language authorship analysis is the inherent characteristics of the arabic language itself. compared to other languages, arabiclanguage structure increases the complexity of preprocessing, whereby unnecessary characters must be removed or carefully replaced. moreover, the complexity of arabic-language morphology tends to increase the set of features, syntax, and semantic structures, which is not germane to authorship analysis tasks. therefore, although the language provides the flexibility of numerous features, most of them are either not used or are not enough for related tasks. one implication of this is the limited number of arabic-language authorship identification studies and the minimal number of datasets that are available for the arabic language @xcite . ## methodology the most recent work on arabic av using linguistic features (stylometric features and tf-idf) gave promising results @xcite , and this study seeks to build on this by investigating the effect of using other features that help to extract more tweets, and understand the context of tweets without being word-dependent. in this work, we will continue the work on the same dataset and use the same stylometric features to find the effect of the investigated features on verifying authorship. the concept is creating a table of words (each in different rows) that carry specific values for each column/feature. the aim is to explain the words' meanings and identify their range of closeness or divergence from other words. we created an arabic knowledge-base in the form of a large table, whereby each word in a row is described with a set of features (columns), each of which carries a number between 0 (if the column is the complete opposite of the word) and 1 (if the column is the exact meaning of the word). other values are explained in more detail in section 3.2. as stylometric features are considered to be essential features for av tasks, we extend the work in @xcite in addition to using our novel arakb model. the results give an indication about the effect of using this technique to verify authorship with special relevance to very brief texts (specifically tweets). ## discussion looking at the results of using a combination of stylometric and arakb features shows a similarity in average results with the results of previous experiments that used a combination of stylometric and tf-idf features. this indicates that using arakb gives similar performance to using the tf-idf features. however, it is important to note that arakb features contained only 1,000 arabic words, due to the laborious and time-consuming individual efforts entailed. it is assumed that the outcomes would be substantially improved by adding many more words that an author would possibly write with. the purpose of this experiment is preliminary testing using arakb features, to determine if these features enhance the performance of verifying authorship in short texts like twitter posts. one could argue that using arakb is over-fitted to the dataset from which the list of words was derived. however, we have selected the thousand most prolifically used words of the whole dataset, which is actually far from being a reason of overfitting. this is because choosing the most repeated words entails that most of the users have used these words, therefore these words are not considered to be user-distinctive (i.e., they reflect ## conclusion the limited work on arabic av texts shows the need to investigate more features that could enhance the verification process. in this experiment, we prove that creating features that represent the word's meaning, as in arakb, does help to effectively verify the authorship, and might be helpful in other nlp tasks, such as sentiment analysis. future work on arakb might extend the number of arabic words, which will definitely improve its performance. in addition, further studies should investigate the influence of each of arakb features, as we could focus only on the most influential ones.