A Pilot Study on the Collection and Computational Analysis of Linguistic Differences Amongst Men and Women in a Kuwaiti A rabic W hats A pp Dataset (2022)

Abstract: This study focuses on the collection and computational analysis of Kuwaiti Arabic (KA), which is considered a low resource dialect, to test different sociolinguistic hypotheses related to gendered language use. In this paper, we describe the collection and analysis of a corpus of WhatsApp Group chats with mixed gender Kuwaiti participants. This corpus, which we are making publicly available, is the first corpus of KA conversational data. We analyse different interactional and linguistic features to get insights about features that may be indicative of gender to inform the development of a gender classification system for KA in an upcoming study. Statistical analysis of our data shows that there is insufficient evidence to claim that there are significant differences amongst men and women with respect to number of turns, length of turns and number of emojis. However, qualitative analysis shows that men and women differ substantially in the types of emojis they use and in their use of lengthened words.

## introduction a wide range of sociolinguistic gender studies have been carried out in english speaking cultures and in the arab world too. however, there is a lack of research on gulf arabic (ga) dialects, and especially the kuwaiti dialect, from a sociolinguistic perspective. the ga dialects vary tremendously with regards to morpho-phonological features, lexical structures and the effect of language borrowing from different languages @xcite . there are some interesting linguistic phenomena in the kuwaiti dialect. the way men and women speak is different and this can be noticed in their choice of words when communicating or expressing feelings or reacting to situations. it can be noticed that there are some words which men would refrain from using because they represent femininity. for example, the word ‫ا‬ ‫ي‬ ‫ن‬ ‫ن‬ "eyanen", which means "amazing" is a word used to convey a positive sentiment towards an entity and is usually only used by women. this word can for example be used to describe a movie by kuwaiti women, whereas men might use the word ‫ج‬ ‫ب‬ ‫ا‬ ‫ر‬ "jbar" which is a polysemous adjective that in this context means "amazing", to describe the movie. moreover, ‫ي‬ ‫ا‬ ‫ح‬ ‫ا‬ ‫ف‬ ‫ظ‬ "ya hafeth" is a phrase that is only used by women. it can be translated into "oh saviour (god)" to convey dissatisfaction or disappointment. if a man uses this expression, he would be described as someone who is feminine in the way he speaks. advances in the field of arabic natural language processing (anlp) have made it possible to study such variation in lexical usage between genders as well to explore other features that are indicative of gender. however, the lack of ka textual resources and preprocessing tools make it a challenging task. this study contributes to the field of anlp in two ways. first, we have compiled and made publicly available a new, gender-labelled ka dataset, which can be used by researchers interested in the kuwaiti dialect or gender studies. this dataset consists of textual book club conversations conducted on the whatsapp online instant messaging mobile application. to the best of our knowledge this is the first published dataset of mixed gender ka conversational data. second, we have carried out an analysis of interactional and linguistic features that may inform the development of a gender classification system for ka. this paper is structured as follows. in the next section we review related work. in section 3 we first discuss how we have collected the raw data, then describe how this raw data has been preprocessed to prepare the dataset for analysis and finally discuss the features that will be explored and analysed. in section 4 we present our results and analysis. finally, we conclude and discuss future work, as well as pointing out some of the limitations of our work. ## related work language is a rich source for analysis and many studies have been conducted to infer the relationship between different social variables and the language they construct @xcite . one of the social variables that is studied in relation to language is gender. traditional studies of language and gender that have been conducted in the humanities and social sciences have had inconsistent findings and have received some criticism. for example, @xcite criticised conclusions drawn about the relationship between language and gender that are dependent on small samples of data. the implication of this criticism is that gender and language studies should be improved by using larger samples of data and different contexts @xcite . however, now that we are in the era of 'big data', extracting large amounts of data for gender analysis has become possible. moreover, sociolinguistic studies of gender have mostly been explored using qualitative methods such as interviews, surveys, recordings and manual observations. @xcite argue that qualitative and quantitative analysis of sociolinguistic gender studies are complementary as qualitative analysis may shed light on phenomena and quantitative analysis provides the opportunity to explore phenomena through large scale studies and also identify cases that can be analysed qualitatively. @xcite explain: language and gender may, then, legitimately be viewed from different perspectives: a pragmatic combination of methods and approaches, along with an acknowledgment of their possibilities and limitations, might allow us to focus on different aspects of the relationship between language and gender, or have a wider range of things to say about this. in the context of studies that have explored gender differences in language use, @xcite looked into gender differences in language usage of whatsapp groups. they analysed over 4 million whatsapp messages from more than 100 users to find and understand differences between different age and gender demographic groups. in analysing the data, they relied on metadata only such as message lengths, size of the whatsapp groups, time, average number of sentences sent per day, time between messages. in relation to gender, analysing the length of messages sent by both genders showed that women send and receive more messages than men. they also concluded that women are more active in small whatsapp groups, whereas men are more active in larger whatsapp groups. these differences were then employed in building age and gender prediction models. they performed a 10-fold cross validation for these tasks using decision trees and a bayesian network. for the gender prediction task, using users' metadata with decision trees achieved 70.27% accuracy and 73.87% accuracy when used with a bayesian network. other studies have looked into differences amongst genders in the use of emojis. @xcite compiled a large dataset of 401 million smartphone messages in 58 different languages and labelled them according to the gender of users. they used emojis from the dataset to study how they are used by males and females in terms of emoji frequency, emoji preference and sentiment conveyed by the emojis. they also studied the extent in which emojis are indicative of gender when used in a gender classification system. the results obtained from this study showed that not only are there considerable differences in the use of emojis between males and females, but also that a gender classification system that uses emojis alone as features can achieve an accuracy of 81%. shared nlp tasks that are organized for the research community have started off by tackling problems with the english language and in recent years have added arabic datasets, reflecting the increasing interest in arabic nlp. for example, the pan 2017 author profiling shared task included two tasks: gender identification and language variety identification of twitter users. arabic, english, portuguese, and spanish datasets consisting of tweets were provided for training and testing. the system that achieved the highest accuracy result on gender identification in the arabic dataset was the system developed by @xcite . they used an svm classifier in combination with word unigrams and character 3-to 5-grams and achieved an accuracy of 0.80. as for studies that have targeted the arabic language, @xcite studied gender text classification of arabic articles using the bag-of-words (bow) approach. they collected and manually labelled 500 arabic articles from different arabic news websites. the number of articles was distributed equally across both genders. they wanted to explore the result of performing feature reduction techniques such as pca and correlation analysis on the high-dimensional data in combination with different machine learning algorithms for the gender classification task. results showed that stochastic gradient descent (sgd), naive bayes multinomial (nbm) and support vector machines (svm) were the classifiers that performed best on the original dataset where the accuracy results surpassed 90%. furthermore, @xcite compiled a dataset of 166k arabic tweets and labelled them with gender and geo location labels. they used this dataset for gender analysis and to build a gender classification system using svms that was tested on different features such as usernames of the twitter users, the profile pictures of the users, tweets and gender distribution of users' friends. their study showed that using usernames alone as features for gender prediction achieved the highest f1 score of 82.1 %. in addition, @xcite attempted to build a gender classification system for egyptian arabic. they created a dataset of 140k tweets that were retrieved from famous egyptian influencers and active egyptian users of twitter. they labelled the dataset according to the gender of the twitter users by referring to the users' profile image and names. they experimented with different features such as gender discriminative emojis, female suffixes, manually created dictionaries of swear words, emotion words, political words, flirting words, technological words and word embeddings. they used ensemble weighted average on a mixed feature vector fed into a random forest classifier and an n-gram feature vector fed into a logistic regression classifier. they achieved an accuracy score of 87.6%. not many gender studies in nlp have provided much insight into linguistic characteristics of gendered language, especially those related to dialectal arabic. furthermore, the field of anlp still lacks enough dialectal arabic datasets to help inform the development of arabic natural language processing tools. @xcite compiled gumar corpus which consists of 100 million ga words from 1200 forum novels annotated according to the dialect, novel name and writer name. the corpus was also used to develop dialectal arabic orthography. however, although gumar corpus contains some ka text, the text is not naturally occuring conversational ka. therefore, there is still a need to compile conversational ka resources. we aim to address this gap by contributing towards providing resources for the ka dialect and analysing sociolinguistic features of that dialect that can be used to inform nlp applications, such as gender classification systems. ## results and analysis to analyse the results of this study, two approaches were taken: a quantitative statistical approach and a qualitative linguistic approach. as for the statistical approach, the mann whitney u test was used for analysis due to it being suitable for data, like ours, which is not normally distributed. it was done using spss foot_1 . one limitation of using a statistical approach in analysing the data is that it does not take into account the contextual information and meanings embedded within the text. therefore, it was important to perform an in-depth manual analysis of the data to be able to describe the patterns found and provide interpretations for points that the statistical analysis could not capture.