The strongest storm in Philippines history, super-typhoon Haiyan, barreled through central Philippines in 2013 and left a high death toll and extensive destruction in its wake. Past studies have investigated Fading Affect Bias (FAB) in extremely negative situations like the death of a loved one and found that the FAB generally occurs in those extreme situations, but this study is the first to assess FAB in first-hand memories for a natural disaster survival situation. The FAB phenomenon is the tendency for emotional intensity associated with negative memories for events to fade over time and emotional intensity for positive events stays relatively stable over time. Researchers collected memories for the super-typhoon from survivors three years after the event. Results showed that negative emotional intensity for the event faded after the event. Emotion in comparison positive memories for non-typhoon events did not fade, and emotion in comparison negative memories faded, following results in several other FAB studies. The Positive and Negative Affect Scale (PANAS) was used as an initial assessment of mood before the study began, and PANAS scores reliably predicted current emotional intensity scores. Memory vividness and emotional intensity in first-hand accounts of a natural disaster experience behave like vividness and intensity in flashbulb memories, but details in first-hand accounts are similar to the amount of details in memories of near-death experiences. How memory rehearsal behaves in relation to time elapsed since event has yet to be captured for first-hand survival experiences.

The 2020 U.S. Presidential election was a campaign that could be characterized as ‘one of the nastiest presidential campaigns in recent memory,’ partly because the general election debates were highly contentious and featured frequent interruptions and several insults and invectives between candidates. This research compared the language used in the debates to fact-checked truths and lies using a Reality Monitoring (RM) deception detection algorithm in Linguistic Inquiry and Word Count (LIWC) to investigate the veracity of real-life high-stakes verbal messages in the political context. We found that overall RM scores were lower and not significantly different between debate language and fact-checked lies, and RM scores were significantly higher in fact-checked truth statements, indicating that most debate language uttered was deceptive. This result supports the finding that the RM algorithm in LIWC distinguishes truth from lies and debate language in the context of politics. The 60.7% classification rate in this study may reflect a problem with the relatively short word counts of fact-checked lie and truth statements, but most probably reflects individual candidates’ deviations in RM features used in their statements. Each individual has a style that they use in communication—‘the way people talk and write have been recognized as stamps of individual identity.’ Even with a corpus of many statements from the same individual candidates, they probably regularly amplify certain features of RM and diminish other features of RM in their truthful and deceptive messages. This is a fruitful area of research that could be explored in future studies.