Unmasking the Perils of Spam: Safeguarding User Privacy, Security, and Trust Online
Spam refers to unsolicited bulk email messages, often sent indiscriminately for commercial purposes without the recipient’s consent. Spam is considered a nuisance as it wastes time and clogs inboxes, and has been a growing problem since the widespread adoption of email in the 1990s, though anti-spam efforts have helped reduce its prevalence over time.
Spam poses a range of threats to online users, from violating their privacy by exploiting personal information without consent, to enabling security risks through malicious links and attachments that can lead to malware infections and cyber attacks. Spam also carries the potential for financial losses when users fall victim to fraudulent schemes and bogus offers, while the constant deluge of unwanted messages can significantly impact workplace productivity and erode overall trust in digital communication platforms. Beyond these direct harms, spam can also inflict reputational damage on individuals and organizations whose accounts are hijacked to send bulk unsolicited emails. Addressing the diverse challenges posed by spam requires a comprehensive strategy involving technological, legal, and educational measures to safeguard users and maintain the integrity of the online environment.
Studying spam is crucial due to its significant negative impacts — it clogs inboxes, wastes resources, enables other online threats like malware and scams, violates user privacy and trust, has tangible economic costs, and continually evolves to adapt to new technologies, requiring ongoing research to develop effective countermeasures and regulations. As an endemic nuisance in the digital landscape, understanding the nature and dynamics of spam is essential for combating this persistent challenge, protecting the integrity of communication systems, and fostering a safer and more efficient online environment.
While spam, fake content, and scams are related online threats, they differ in key ways — spam refers specifically to unsolicited bulk commercial emails, fake content is false or misleading information presented as fact, and scams are fraudulent schemes designed to deceive and exploit victims, often using deceptive tactics. These issues can overlap, such as a spam email containing fake claims as part of a scam, however, they are distinct concepts that require tailored strategies to address, as spam is defined by the bulk and unsolicited nature of the communication, fake content by its factual inaccuracy, and scams by the intent to defraud.
[1] Machine-Learning-Based Spam Mail Detector
[2] Spam-Based Scams
[3] Spam detection in social media using convolutional and long short term memory neural network
[4] Think Before RT: An Experimental Study of Abusing Twitter Trends
[5] Adaptive Learning Ant Colony Optimization for Web Spam Detection
[6] A Voice Spam Filter to Clean Subscribers’ Mailbox
[7] Taxonomy and Control Measures of SPAM and SPIM
[8] Spam Control by Source Throttling Using Integer Factorization
[9] Spam Filtering in Twitter Using Sender-Receiver Relationship
[10] Spam Detection on Twitter Using Traditional Classifiers
[11] Machine Learning for the Detection of Spam in Twitter Networks
[12] Fighting Spam on the Sender Side: A Lightweight Approach
[13] Web Spam, Social Propaganda and the Evolution of Search Engine Rankings
[14] Fighting Link Spam with a Two-Stage Ranking Strategy
[15] Y.O.U MayHAVE Alredy I!: Spam
[16] Fighting Spam: The Science
