Abstract
Social media has become an essential part of the digital age, serving as a platform for communication, interaction, and information sharing. Celebrities are among the most active users and often reveal aspects of their personal and professional lives through online posts. Platforms such as Twitter provide an opportunity to analyze language and behavior for understanding demographic and social patterns. Since followers frequently share linguistic traits and interests with the celebrities they follow, textual data from followers can be used to predict celebrity demographics. However, most existing research in this field has focused on English and other high-resource languages, leaving Urdu largely unexplored. This study applies modern machine learning and deep learning techniques to the problem of celebrity profiling in Urdu. A dataset of short Urdu tweets from followers of subcontinent celebrities was collected and preprocessed. Multiple algorithms were trained and compared, including Logistic Regression, Support Vector Machines, Random Forests, Convolutional Neural Networks, and Long Short-Term Memory networks. The models were evaluated using accuracy, precision, recall, F1-score, and cumulative rank (cRank). The best performance was achieved for gender prediction with a cRank of 0.65 and an accuracy of 0.65, followed by moderate results for age, profession, and fame prediction. These results demonstrate that follower-based linguistic features can be effectively leveraged using machine learning and neural approaches for demographic prediction in Urdu, a low-resource language.
Abstract (translated)
社交媒体已成为数字时代不可或缺的一部分，它作为沟通、互动和信息分享的平台发挥着重要作用。名人是其中最活跃的一类用户群体，他们经常通过在线帖子展示个人生活和职业生活的各个方面。例如，Twitter这样的平台为分析语言和行为提供了机会，以理解人口和社会模式。由于追随者通常与他们关注的名人的语言特征和兴趣有相似之处，因此可以从这些追随者的文本数据中预测名人的特征。 然而，在这一领域的大多数现有研究主要集中在英语和其他资源丰富的语言上，乌尔都语则相对较少被探索。本研究将现代机器学习和深度学习技术应用于乌尔都语的名人画像问题。我们收集并预处理了一个由次大陆名人的追随者发布的短微博数据集。训练了包括逻辑回归、支持向量机、随机森林、卷积神经网络和长短期记忆网络在内的多种算法，并通过准确率、精确度、召回率、F1分数以及累积排名（cRank）对模型进行了评估。 性别预测方面取得了最佳性能，其cRank为0.65，准确率为0.65；年龄、职业和知名度预测则表现较为一般。这些结果表明，在乌尔都语这一资源较少的语言中，可以通过机器学习和神经网络方法有效地利用追随者的语言特征来进行人口统计学预测。
URL
https://arxiv.org/abs/2510.11739
