It has long been theorized that the exchange of information in the aftermath of large-scale upheavals ensues dynamics that follow a stage model, which would be a societal equivalent of individuals’ psychological processing of traumatic events. Nowadays, a relevant portion of this informational exchange occurs on social media platforms. In this study, we use the digital footprint of three independent earthquakes to analyze their communication dynamics. We find empirical evidence of a stage model previously proposed by Pennebaker (Pennebaker in Handbook of mental control, Prentice-Hall Inc., Hoboken, 1993) in the aftermath of the earthquakes. In addition, we further explore the role of emotions within the model stages through time using natural language processing tools. Our results show that emotions with low activation levels, such as interest and sadness, are expressed in higher proportions and are the most useful for predicting the expression of emotions with higher activation levels. Employing newly available computational methods like digital trace data, natural language processing, clustering, and causal analysis, this study extends Pennebaker’s model from offline to online social communication.

One of the main drivers of social media's influence is the easy way of generating information cascades via forwarding messages. Two basic modes of forwarding information consist in sharing it without changes (lean-back) or adding or modifying the content of the original message (lean-forward). In this work, we study these two modes of online information sharing. Using data from six cases extracted from Twitter, in which retweets make up most of the content, we analyzed emotions in passive versus proactive information diffusion. Our findings show that emotional valence does not indicate significant differences between lean behaviors, while the activation level of emotions presents contrasts. For example, disgust is more intense in lean-back and anger in lean-forward behavior. We also find that in proactive lean-forward communication, disgust and joy synchronize with the emotions of lean-back messages. Finally, a causal analysis of lean-forward information sharing reveals that disgust provokes a consistent increment of itself while it also decreases the appearance of anger in political topics. We discuss the implications of our findings for the study of emotions in active versus passive forwarding of information in social media.

LLMs can interact as though they understand language, yet they remain algorithms and can be used as such. This study explores a novel guided interaction design for modeling users' information foraging behavior when navigating GPT-generated content and the role of Computational Thinking skills in shaping such behavior. Conducted with nine educational researchers in a doctoral-level AIEd course, our research used editable prompt templates and keywords to structure the prompt crafting process. We modeled and analyzed participants' behaviors in terms of \textit{exploration} (to generate and explore various information landscapes) and \textit{exploitation} (to delve deeper in a specific landscape). Our data, including responses from the Computational Thinking Scale, suggests that Algorithmic Thinking and Creativity might encourage exploitation behavior, leaning more on AI-generated information rather than pre-defined design elements.