Spam has been increasingly used for malware distribution. This paper analyzed modern spam from an age-comparative perspective to (i) discover the extent to which psychological weapons of influence and life domains were represented in today’s spam emails and (ii) clarify variations in the use of these weapons and life domains by user demographics. Thirty five young and 32 older participants forwarded 18,605 emails from their spam folder to our study email account. A random set of 961 emails were submitted to qualitative content coding and quantitative statistical analysis. Reciprocation was the most prevalent weapon; financial, leisure, and independence the most prevalent life domains. Older adults received health and independence-related spam emails more frequently, while young adults received leisure and occupation-related spam emails more often. These age differences show a level of targeting by user demographics in current spam campaigns. This targeting shows the need for age-tailored demographic warnings highlighting the presence of influence and pretexting (life domains) for suspicious emails for improved response to cyber-attacks that could result from spam distribution. The insights from this study and the produced labeled dataset of spam messages can inform the development of the next generation of such solutions, especially those based on machine learning.

中文翻译：

---

现代垃圾邮件中影响力武器，生命域和人口统计目标的实证分析：年龄比较观点

垃圾邮件已越来越多地用于恶意软件分发。本文从年龄比较的角度分析了现代垃圾邮件，以（i）发现影响力和生活领域的心理武器在当今垃圾邮件中所占的比例，并且（ii）阐明用户使用这些武器和生活领域的变化人口统计资料。35位年轻参与者和32位年龄较大的参与者将18,605封电子邮件从其垃圾邮件文件夹转发到我们的研究电子邮件帐户。随机收集了961封电子邮件，以进行定性内容编码和定量统计分析。往复运动是最流行的武器。财务，休闲和独立是最普遍的生活领域。老年人收到健康和独立性相关的垃圾邮件的频率更高，而年轻人收到休闲和职业相关的垃圾邮件的频率更高。这些年龄差异显示了当前垃圾邮件广告系列中按用户受众特征定位的级别。此目标表明需要针对年龄的人口统计警告，突出显示对可疑电子邮件的影响和借口（生命域）的存在，以改善对垃圾邮件分发可能导致的网络攻击的响应。这项研究的见识以及所产生的带有标签的垃圾邮件数据集可以为下一代此类解决方案的开发提供信息，尤其是那些基于机器学习的解决方案。此目标表明需要针对年龄的人口统计警告，突出显示对可疑电子邮件的影响和借口（生命域）的存在，以改善对垃圾邮件分发可能导致的网络攻击的响应。这项研究的见识以及所产生的带有标签的垃圾邮件数据集可以为下一代此类解决方案的开发提供信息，尤其是那些基于机器学习的解决方案。此目标表明需要针对年龄的人口统计警告，突出显示对可疑电子邮件的影响和借口（生命域）的存在，以改善对垃圾邮件分发可能导致的网络攻击的响应。这项研究的见识以及生成的带有标签的垃圾邮件数据集可以为下一代此类解决方案的开发提供信息，尤其是基于机器学习的解决方案。