A rab G end: Gender Analysis and Inference on A rabic T witter (2022)

Abstract: Gender analysis of Twitter can reveal important socio-cultural differences between male and female users. There has been a significant effort to analyze and automatically infer gender in the past for most widely spoken languages’ content, however, to our knowledge very limited work has been done for Arabic. In this paper, we perform an extensive analysis of differences between male and female users on the Arabic Twitter-sphere. We study differences in user engagement, topics of interest, and the gender gap in professions. Along with gender analysis, we also propose a method to infer gender by utilizing usernames, profile pictures, tweets, and networks of friends. In order to do so, we manually annotated gender and locations for ~166K Twitter accounts associated with ~92K user location, which we plan to make publicly available. Our proposed gender inference method achieve an F1 score of 82.1% (47.3% higher than majority baseline). We also developed a demo and made it publicly available.

## introduction demographic information (e.g., age, gender) has proven to be useful in many different decisionmaking processes such as, from business decisions (e.g., personalized online advertising), forensic investigation to policy-making purposes @xcite @xcite . for example, social media platforms and e-commerce sites are using customers' gender and other demographic attributes for targeted advertising @xcite . in the past decade, there have been extensive research efforts to automatically infer demographic attributes of the social media users using their social media footprints (e.g., users' posts, names, and other attributes) @xcite . in addition, to evaluate the performance of the model's fairness for different tasks it is important to have such attributes @xcite . given that such attributes are often removed from the original source for privacy and ethical reasons, however, having such attributes through inference is a possible way to evaluate the model's fairness. major research efforts for such attributes inference are mostly done for english, and very little effort has been given to non-english languages @xcite . the research for arabic demographic inference such as gender is relatively rare for social media users, specifically on twitter's content. with approximately 164 million monthly active users, twitter is one of the most popular social media platforms in the arab region @xcite . the large volume of tweets produced represents the social and cultural characteristics of the region. even though there is a large number of twitter users, however, usage of twitter differs in volume, topics, and engagement depending on the users' gender role. another important factor is that social media users often provide misleading demographic information (e.g., name, age, location and marital status), which is highlighted in a survey conducted in the arab region @xcite . hence, self-declared information might not be always reliable. though some studies argue that the proportion of such misleading self-reported information is relatively lower @xcite . while the availability of twitter data and its large user base provides opportunities to understand such information, however, for privacy reasons, twitter does not share users' gender information @xcite . such factors stress the need to have automatic methods for gender inference, and here our focus is twitter-sphere for the arabic region. in addition, there is a gap in the literature in a thorough analysis of arabic twitter (e.g., linguistic content) for gender, even though arabic is a morphologically rich language where linguistic markers are present to distinguish genders in many cases (see section 3.1). to address the gap of gender analysis and automatic inference, in this paper, we perform an extensive analysis of arabic twitter data where we identify key distinguishing properties of male/female authorship. we experiment with different features to identify the gender of twitter users. we examine the usage of friendship networks, profile pictures, and textual information such as usernames, user descriptions, and tweets to classify gender. the contributions of our work are as follows: ## related work gender inference is a well-studied problem in english. @xcite present a dataset of 13k gender-labeled twitter users and propose the use of first names as features for gender inference. screen_names, full names, user descriptions, and tweets have also been used as features for gender inference @xcite . @xcite use stacked svms for identifying gender and other latent attributes of twitter users. semi-supervised methods that exploit social networks have also been used for gender classification @xcite . gender inference has also received attention for a few other languages. @xcite combine the output of text processor and image processor to infer the gender of japanese twitter users. @xcite propose a hybrid method that uses logistic regression to combine text and image features. @xcite label 1000 users for gender in each of the following languages: japanese, indonesian, turkish, and french. the authors use support vector machines (svms) for classification. @xcite present a dataset consisting of 5.5k twitter users labeled for their gender. tuan et al. (2019) proposes clusteringbased approaches for demographic analysis to support advertising campaigns. very recently liu et al. (2021) provided a large-scale study that investigate different inference techniques (e.g., classic machine learning to deep learning models) using twitter data. the authors highlight that a simpler model performs well to infer age, however, sophisticated models (e.g., sentence embeddings) are important for gender. for arabic, on the other hand, work is relatively less explored. malmasi (2014) use first names to classify the gender of arabic, german, iranian and japanese names. elsayed and farouk (2020) uses neural networks to differentiate male and female authors of tweets in egyptian dialect. hussein et al. (2019) use classic machine learning classifiers such as logistic regression and random forest classifiers to identify gender in egyptian tweets. habash et al. (2019) use deep learning for gender identification and uses machine translation for reinflection. bsir and zrigui (2018) use the gated recurrent unit (gru) for gender identification in facebook and twitter posts. zaghouani and charfi (2018) collect a corpus of 2.4m multi-dialectal tweets from 1600 accounts that are tagged for gender, age, and language. wang et al. (2019) propose a new multilingual (32 different languages), multimodal, multiattribute deep learning system for inferring different demographic attributes. our work differs from previous work on gender analysis and inference for arabic in a number of ways (i) it uses a much bigger dataset for male and female users; (ii) it has no bias towards a specific country as it covers users from all arab countries; ## conclusion in this paper, we have presented arabgend, a new dataset of twitter users labeled for their gender and location. to the best of our knowledge, this is the largest arabic dataset for gender based analysis. we analyzed the characteristics of the users from a gender perspective. we identified key differences between male and female accounts on arabic twitter such as user connections, topics of interest, etc. we also studied the gender gap in professions and argued that results obtained from our dataset are aligned with recent reports from the world bank and twitter ads information. we also showed that our dataset yields the best inference results on a publicly available test set. in the future, we plan to enhance our data collection method by considering gender markers in the whole user description and other profile fields. english and predicts a gender label with probabilities. the demo can be accessed using the link: https://asad.qcri.org/demo (part of asad tools @xcite ). a screenshot of the demo is presented in figure 11 .