## 1 Introduction  
Text messaging, a ubiquitous and essential means of connection in today's society, has become a cornerstone of modern communication. It enables rapid and efficient information exchange between individuals and businesses, spanning various contexts--from personal relationships to professional interactions. However, the rise of spam messages poses significant challenges beyond mere inconvenience. One of the primary concerns is the negative impact of spam on users' mental health. The increasing use of digital technologies, coupled with unsolicited messages, can trigger social anxiety and stress [1]. Dangerous spam, such as phishing attempts, manipulates users by inducing fear and anxiety, leading to psychological distress. Consequently, researchers advocate for a more detailed examination of the effects of digital threats on mental well-being [2, 3, 4]. Beyond their mental health implications, frequent spam messages disrupt daily life by inundating users with unnecessary alerts and constant phone vibrations. This constant bombardment annoys and poses severe threats to personal safety. Spam often lures individuals into clicking misleading information or divulging personal details, resulting in severe consequences like identity theft and financial losses. Recent studies underscore the severity of this issue. A security survey involving 7335 participants from seven countries, published by Bitdefender in April 2024, revealed that SMS fraud is the most prevalent security threat, with 45.4% of respondents reportingbeing targeted by fraud attempts via SMS [5]. Additionally, statistics from March 2024 indicate that 78 billion spam messages were sent in the US during the first half of 2023. Alarmingly, in 2020, 20% of Americans fell victim to mobile fraud, and by 2023, there were 2.6 million fraud reports totaling USD 10 billion in financial losses [6]. As researchers, policymakers, and individuals concerned about digital security and mental well-being, our role in combating this issue is crucial. The technical ramifications of spam messages are equally concerning. Excessive volumes overwhelm users and strain network resources by reducing bandwidth and increasing load [7]. Detecting spam messages and mitigating their impact has, therefore, become paramount. However, existing spam detection methods face significant challenges, including low detection rates and high false positive probabilities. These issues hinder effective filtering and can lead to negative user experiences, such as important emails being misclassified as spam [8]. To address these challenges, various spam detection methods have been developed. These include rule-based approaches, classical machine learning techniques, deep learning models, natural language processing (NLP) methods, and hybrid strategies. Rule-based methods were among the first employed in spam detection [9; 10], utilizing blacklists, whitelists, and conditional rules as primary mechanisms [11]. In such systems, the classification of a message often depends on whether the sender appears on a particular list [12]. Classical machine learning methods are also widely used in spam detection [13; 14; 15]. Algorithms like Naive Bayes, logistic regression, decision trees, and support vector machines (SVM) are commonly applied [16; 17; 18]. These techniques typically involve extracting features from text messages using NLP or statistical approaches, followed by classification based on the extracted features. Deep learning approaches have gained popularity in recent years due to their ability to enhance feature extraction and classification. These models effectively capture inherent data patterns, leading to higher classification success rates. Hybrid methods that combine rule-based, machine learning, and deep learning techniques have also shown promise in providing effective solutions for spam detection [19]. Spam detection remains a critical research area aimed at improving security and efficiency in digital communication by preventing unwanted or harmful content from reaching users.