(untitled) (Contemporary Mathematics, 2024)
A person’s emotional state toward the past and future might be revealed by their "temporal distance" (TD), a psychological measure. There isn’t a lot of real-world research on how to measure attention span from human-written content to look at how people think about time. Self-report measures have been used a lot in studies of temporal attention. This article shares the results of a study that looked at how Twitter users’ attention changes over time. First, we use deep neural classifiers to figure out the temporal emphasis at the tweet level by using language data. The method sorts tweets into four groups based on when they were sent: recently, far away, likely to happen, and unlikely to happen. Then, each user sorts the classified tweets to get a focus on a certain time period. Lastly, Assemblies are drawn between the user’s attention directed towards temporal distance and data pertaining to their own history and disposition. Our real-world research shows that there is a stronger link between the age of the customer and their near-past concentration. Additionally, we can see that users who focus on the future feel good emotions, while users who focus on the past feel fear, anger, hopelessness, and disdain. A Multi Markov Model (MMM) is introduced in order to comprehend the characteristics of emotion dynamics inside Twitter tweets.