## 3 Results
### Statistically Significant and Not Statistically Significant Differences  
The comparison of tweets and ChatGPT-generated narratives used the 11 PANAS traits and four life event types combined to produce a total of 44 tested comparisons for significant differences. As outlined in Section 2.1, [PERSON]'s exact test is applied in cases where two-by-two contingency tables contain any cells with fewer than five samples. Chi-squared tests are applied in all other cases. The two-by-two contingency tables are constructed with tweets and ChatGPT-generated narratives as columns and the counts of  
Figure 5: Final-design structured prompt input and ChatGPT output. (**a**) Final-prompt-design set of instructions tested with ChatGPT for generating narratives based on ABM agents’ life events and information. (**b**) Sample output set based on initial instructions presented in (**a**). 0s and 1s with respect to a PANAS trait as the two rows. As such, 11 contingency tables are constructed with one specific to each PANAS trait. For all of our tested cases, all cells within the contingency tables resulted in greater than five samples; therefore, the chi-squared test was applied for all significance tests. Statistical significance tests are applied using the mean sentiment scores for a PANAS trait between tweets and ChatGPT-generated narratives. For each narrative, as well as for each tweet, the PANAS trait assessments are binary. A score of 1 is assigned for a trait if any corresponding words within the lexicon are contained within the text and a score of 0 is assigned if no terms are contained. As a result, the mean value for each assessment reflects the prevalence of the corresponding sentiment within each dataset. Of the 44 tests for statistically significant differences between the tweets and the ChatGPT-generated narratives for the Birth, Death, Hired, and Fired event types, four comparisons provided evidence in support of rejecting the null hypothesis in favor of the alternative hypothesis and 40 comparisons provided evidence in support of failing to reject the null hypothesis. Table 4 provides the aggregate number of comparisons for rejecting or failing to reject the null hypothesis for each life event type. We are particularly interested in instances where no statistically significant evidence is found in support of rejecting the null hypotheses outlined in Hypothesis 0. These instances represent cases where the sentiment prevalence for a given PANAS group was not discernibly different from the language used within real tweets and represent a step forward in the generation of realistic narratives from simulated agents. For the ChatGPT-generated narratives, four of the forty-four comparisons (9.09%) were not statistically significantly different from the sentiment prevalence within the tweets. For Birth events, this included _all negative_ (ChatGPT-generated narratives mean = 0.410, tweets mean = 0.438, \(p\)-value = 0.128). For Death events, this included _nervous_jittery_ (ChatGPT-generated narratives mean = 0.052, tweets mean = 0.074, \(p\)-value = 0.052). For Fired events, this included _all positive_ (ChatGPT-generated narratives mean = 0.506, tweets mean = 0.514, \(p\)-value = 0.691) and _strong_active_ (ChatGPT-generated narratives mean = 0.322, tweets mean = 0.319, \(p\)-value = 0.859). No PANAS group comparisons were found to be not statistically significantly different for Hired comparisons. Mean binary PANAS scores for all sentiment keyword categories and all life event types are plotted in Figure 6. For _Death_ and _Fired_ narratives, it was expected that sentiments would skew towards the use of negative terms. These narratives did generally attain higher mean sentiment scores compared to the mean scores for _Birth_ and _Hired_ narratives; however, _Death_ narratives still achieved higher mean sentiment in the positive polarity categories of _excited_enthususistic_inspired_, _proud_determined_, and _strong_active_.