## 2 Materials and Methods
### Sentiment Scoring and Statistical Analysis  
Narratives communicating important information for an agent, such as an associated birth or death, should follow a realistic flavor for the given environment while maintaining a flavor that is unique both to the agent and the agent's current state of mind. Therefore, the sentiment conveyed in a narrative should vary depending on the type of life event. A birth announcement may be themed in a very joyous manner while a narrative pertaining to a death may be much more melancholy and reminiscent. We aim to assess whether statistically significant differences exist in feelings and emotions between narratives generated by LLM platforms and real tweets with respect to PANAS groups. We generate narratives framed around Birth, Death, Hired, and Fired events for comparative evaluation between the sentiment of real tweets and the sentiment of the generated narratives. An archive of tweets generated by psychology students [43] is used as a corollary to the LLM narratives to establish a baseline sentiment value for comparing against the generated narratives. Note that the ChatGPT-generated narratives are based on four specified event types while the real tweets covered a wide range of unrelated situations over a one-year period. All of the tweets are compared to each set of generated narratives for each event type, thereby providing a static sentiment level for comparison across each event type. Our evaluation relies on the large, unbiased vocabulary present within the tweet set for enabling the generalized assessment of sentiment scores across the 20 PANAS categories and facilitating assessments for statistical significance. The statistical tests selected include the chi-squared test and [PERSON]'s exact test. For both tests, it is assumed that the data can be divided into a two-by-two contingency table with two groups (i.e., LLM-generated narrative dataset versus real-tweets dataset) and two categories (e.g., narratives demonstrating any amount of positive sentiment versus narratives demonstrating zero positive sentiment). We then apply [PERSON]'s exact test or the chi-squared test to look at whether significant differences in sentiment values exist between the PANAS groups for the given comparison categories. [PERSON]'s exact test [63] is appropriate when very small sample sizes exist within any of the cells of the contingency table [64]. To determine which test to apply in each case, we assess whether any cell of the two-by-two contingency table has fewer than five samples. If any cell has fewer than five samples, then we apply [PERSON]'s exact test; otherwise, we apply the chi-squared test. For the chi-squared test, we provide the \(\chi^{2}\) test statistic, the degrees of freedom (df), and the \(p\)-value. For [PERSON]'s exact test, we only provide the \(p\)-value. [PERSON]'s exact test has been used in recent works to identify urban functions [65]; to isolate differences between pregnant women with and without pregnancy-induced hypertension [66]; to find differences in crocodile lizard populations regarding parasitic infection [67]; to identify associations between single-nucleotide polymorphisms with autism spectrum disorder patients [68]; and to perform analyses of the development of lower-back pain in school-age children [69]. Figure 2 provides a high-resolution depiction of our sentiment scoring and statistical analysis algorithms. The color scheme in Figure 2 mirrors the color scheme used in Figure 1.  
To score the sentiment of each message and allow for comparisons between narrative sources, we utilize the PANAS lexicon. The PANAS lexicon reflects words associated with the well-validated Positive and Negative Affect Schedule (PANAS) [44; 45]. PANAS consists of 20 traits, with 10 traits measuring positive affect (e.g., excited, inspired) polarity and 10 traits measuring negative affect (e.g., upset, afraid) polarity. These 20 traits are categorizedinto nine groups.