As advanced modern systems like deep neural networks (DNNs) and generative AI
continue to enhance their capabilities in producing convincing and realistic
content, the need to distinguish between user-generated and machine generated
content is becoming increasingly evident. In this research, we undertake a
comparative evaluation of eight traditional machine-learning algorithms to
distinguish between machine-generated and human-generated data across three
diverse datasets: Poems, Abstracts, and Essays. Our results indicate that
traditional methods demonstrate a high level of accuracy in identifying
machine-generated data, reflecting the documented effectiveness of popular
pre-trained models like RoBERT. We note that machine-generated texts tend to be
shorter and exhibit less word variety compared to human-generated content.
While specific domain-related keywords commonly utilized by humans, albeit
disregarded by current LLMs (Large Language Models), may contribute to this
high detection accuracy, we show that deeper word representations like word2vec
can capture subtle semantic variances. Furthermore, readability, bias, moral,
and affect comparisons reveal a discernible contrast between machine-generated
and human generated content. There are variations in expression styles and
potentially underlying biases in the data sources (human and
machine-generated). This study provides valuable insights into the advancing
capacities and challenges associated with machine-generated content across
various domains.

比较了八种传统机器学习算法来区分机器生成数据和人类生成数据，结果表明传统方法在识别机器生成数据方面具有较高准确度。机器生成的文本相比人类生成的内容更短且词汇较少，而特定领域相关关键词可能导致这种高准确度，通过 word2vec 等深层次词表示可以捕捉微妙的语义差异。同时，可读性、偏见、道德和情感对比显示出机器生成内容与人类生成内容的明显差异，研究为机器生成内容在各个领域的进展能力和挑战提供了有价值的见解。