  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.
