Empowering Palestinian Voices Using Text Mining Techniques to Overcome Social Media Expression Restrictions

Abstract

In an era where digital communication shapes political discourse and collective memory, social media platforms have become both spaces of empowerment and instruments of control. This thesis investigates the algorithmic suppression of Palestinian digital expression on major platforms—Facebook, Instagram, and X (formerly Twitter)—through the integration of Natural Language Processing (NLP) and text mining techniques. The study situates itself within the interdisciplinary domains of artificial intelligence, digital rights, and computational social science, aiming to uncover how algorithmic bias operates within automated moderation systems. Data were collected using Apify-based scrapers and analysed in Kaggle through multiple preprocessing and modelling stages. Techniques such as TF-IDF vectorization, sentiment and emotion analysis, and Named Entity Recognition (NER) were employed to identify linguistic and affective patterns correlated with content suppression. Comparative machine-learning experiments—including Logistic Regression, Naïve Bayes, Linear SVC, SGD, and transformer-based models (BERT and XLM-R)—revealed consistent evidence of algorithmic bias. Facebook demonstrated structural filtering and downranking of politically sensitive posts, Instagram exhibited emotional suppression of solidarity content, and X retained partial transparency but reflected selective engagement constraints. The results confirm that algorithmic repression is not incidental but systematically embedded within platform architectures and moderation logic. Beyond quantitative findings, the research advances an Integrated Research Framework that combines computational rigor with ethical reflection, positioning data science as a form of digital resistance. This thesis contributes to the emerging field of algorithmic justice by presenting empirical evidence of digital repression and proposing a context-aware, ethically grounded approach to AI design. It concludes that reclaiming visibility in the algorithmic age is not merely a technical challenge but a moral and political act—one that defines the future of digital freedom and equity.