# The New Paradigm of Deepfake Detection at the Text Level
## 3 Materials and Methods  
Deepfake texts are defined as sequences of text generated by AI models. The primary objective of this research is to identify texts that mimic the style and structure of human text when generated by AI. AI tools can improve user experiences. However, their use must adhere to clear ethical standards. There are situations in which text generation using AI tools respects these standards, but there are also situations in which they should not be used. AI tools allow for the development of human capabilities when specific ethical criteria are respected. AI could generate better texts, but not all texts should be entirely generated by AI. Correcting a text is an example of how AI can be used without violating ethical criteria. Generating a text that has no connection to reality violates all ethical principles. The present article aims to identify situations in which AI is used without violating moral norms. For a concise distinction, the authors, in the following section, highlight the fundamental differences between synthetic data and deepfake data:  
* Synthetic data are used to improve user experience. They allow the expansion of certain datasets that are used in various scenarios. The most well-known scenario is the one associated with training AI models. The augmentation of existing data respects similarities with reality. * Deepfake data are generated to imitate a person's writing style for manipulative purposes. This type of data is used to create misleading messages and spread fake news. From this perspective, it does not respect ethical standards. Also, there are differences between deepfake texts and fake news:
* Fake news is distorted information that is presented as accurate to manipulate public opinion or spread misinformation. AI can generate these, but the general objective is to produce fabricated information. * The deepfake text uses advanced NLP algorithms and AI models (such as GPT). The result generates content that realistically imitates a person's writing style. These can seem very authentic and can be hard to distinguish from a text written by a real person. Their objective is not misinformation but, more specifically, the simulation of original content. From their teaching experience and the previously presented literature, the authors of this work have identified four differences between human-generated texts and AI-generated texts:
* Paragraph length within documents: The authors observed that NLP models tend to generate paragraphs with sentences containing a larger number of words. Human texts have an average length of 17 words, as presented in [48]. This hypothesis will be validated through the tests outlined in this research. * Word length: Words have an average length of approximately seven characters. Through this research, the authors aim to validate the hypothesis that NLP models tend to use long words. Conversely, human-generated words are simpler, even in academic, technical, or everyday content. * Word distribution: Another criterion indicating that a text might be deepfake is the distribution of words. The authors observed from the analysis of texts that NLP models exhibit a high frequency of certain words in AI-generated texts. * Sentiments expressed in sentences: The authors noted that texts generated by NLP tend to be neutral without strongly expressing an opinion. One example is \"something, but also something else\". * AI-generated text changes the dominant discussion category with a new paragraph. When the text contains multiple paragraphs, the analysis is more developed. These evaluation criteria will later be analyzed using CDFTM. It will determine whether a text is human-generated or deepfake. The proposed model aims to achieve an accuracy that surpasses the values mentioned in the literature review section. When the model identifies the text as belonging to the Human category, a second layer of checks is performed using a text anomaly detector.