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The present study provides a qualitative-quantitative linguistic analysis of AI-generated versus human-generated short story adaptations with the aim of detecting the stylometric features that distinguish each style. The author analyzed 15 classic short story adaptations written by nonnative ESL students in an Egyptian university and compared them to 15 adaptations of the same short stories generated by the AI bot, ChatGPT, in terms of content as well as the stylometric language features of length, lexical choices and word frequencies. The results show that the AI-generated text was more loyal to the main theme, plot line and character description of the original story but used language that was more complex, descriptive, unique and bias-free, whereas the student-generated text deviated from the original story in terms of theme, plot, characters and context and used language structures that were simpler and more repetitive. The non-native features characterizing student-generated text included long sentences with excessive use of coordinators, basic vocabulary, frequent intensifiers and L1-induced structures and sociocultural stereotypes. The implications of the study can be used to inform the ESL community of ways to distinguish ChatGPT-generated from nonnative human-generated linguistic features with the aim of attributing texts to their rightful authors as well as finding out the linguistic and stylistic features that characterize non-native English text.

This paper conducts a sentiment analysis comparing human and automated sentiment annotation of Facebook comments associated with news articles likely to evoke the emotions of anger, fear, sadness, and happiness. The study finds that both human and automated methods assigned mostly similar sentiment polarities- negative for comments on the articles triggering anger, fear, and sadness, and positive for comments on the article evoking happiness. However, human annotators detected a wider range of emotional words, while the automated tool missed many of them and, at times, provided inaccurate descriptions of emotions. The study also employs Martin and White's (2005) appraisal theory to examine the emotion-related language structures in these comments. It reveals that the affect dimension predominated in discussions of the sadness-related article, the judgment dimension was more prominent in discussions of the anger- and happiness-related articles, and the appreciation dimension featured more in discussions of the fear-related article.

The present paper adopts a psycholinguistic perspective in an attempt to study the impact of aging on the use of affective language. The paper provides a quantitative-qualitative analysis of a study conducted with 90 Egyptian adults of different age groups in order to investigate their emotional perception of words related to personal experiences and social relationships as well as their use of affective language to describe a real-life situation, adopting the framework of the Linguistic Inquiry and Word Count (LIWC) model proposed by Penne baker, Booth and Francis (2001). The findings of the present study partially agree with the findings of previous research in regard to the inclination of individuals above 40 to use more positive affective language. Yet, contrary to previous research which found that the increase in positive affect continues well into old age, the present study found that positive affective language use starts to decline after the age of sixty.

The present paper presents a psycholinguistic and pragmatic analysis of a study conducted on 90 Egyptian adults of different age groups in order to investigate the politeness strategies they preferred in situations arousing different emotions. The findings analyzed quantitatively and qualitatively indicate that the older adults show tendency to use bald on record strategies in situations arousing fear signaling their vulnerability and need for assistance and prefer off-record strategies in situations arousing anger in order to avoid direct conflict with others. The middle-aged group choose more positive politeness strategies, in general, indicating their tendency to maintain strong social relationships and to control negative emotions. The younger adults, on the other hand, choose more negative politeness strategies indicating their inclination to enhance social distancing and avoid threatening the face of others. The present study attempts to link research on aging, emotion processing and politeness with the aim of providing a complete picture of linguistic social interactions across different age groups.