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Firms’ public communication on social media during disasters can benefit both disaster response efficiency and the perception of the corporate image. Despite its importance, limited guidelines are available to inform firms’ disaster communication strategies. The current study examines firms’ communication on social media in various disasters and how it impacts public engagement. We employ a novel natural language processing (NLP) approach, Semantic Projection with Active Retrieval (SPAR), to analyze Facebook posts made by Russell 3000 firms between 2009 and 2022 concerning various disasters. We show that firm communication can be measured based on two dimensions derived from the Competing Values Framework (CVF): internal versus external and stable versus flexible. We find that social media messages that emphasize operational continuity (internal/stable-oriented) are more popular during biological disasters. By contrast, messages that stress innovations and adaptations to disasters (external/flexible-oriented) elicit more engagement in weather-related disasters. The study offers a framework to characterize and guide firms’ design of disaster communication on social media in different disaster contexts. Our SPAR method is also available to firms to analyze their social media data and uncover the underlying patterns in communication across different contexts. 

The collective intelligence of online communities often depends on implicit forms of coordination, given the fluidity of membership and the lack of traditional hierarchies and associated incentive structures. This coordination drives knowledge production. Studying temporal dynamics may help elucidate how coordination happens. Specifically, the rate of interaction with an artifact such as a Wikipedia page can function as a signal that affects future interactions. Many activities can be characterized as bursty, meaning activity is not evenly spread or random, but is instead concentrated. This study analyzes 3,260 Wikipedia articles and shows that the coordination pattern in the Wikipedia community is mostly bursty. More importantly, the extent of burstiness affects article quality. This work highlights the important role temporal dynamics can play in the coordination process in online communities, and how it can affect the quality of knowledge production. 

Bots are playing an increasingly important role in the creation of knowledge in Wikipedia. In many cases, editors and bots form tightly knit teams. Humans develop bots, argue for their approval, and maintain them, performing tasks such as monitoring activity, merging similar bots, splitting complex bots, and turning off malfunctioning bots. Yet this is not the entire picture. Bots are designed to perform certain functions and can acquire new functionality over time. They play particular roles in the editing process. Understanding these roles is an important step towards understanding the ecosystem, and designing better bots and interfaces between bots and humans. This is important for understanding Wikipedia along with other kinds of work in which autonomous machines affect tasks performed by humans. In this study, we use unsupervised learning to build a nine category taxonomy of bots based on their functions in English Wikipedia. We then build a multi-class classifier to classify 1,601 bots based on labeled data. We discuss different bot activities, including their edit frequency, their working spaces, and their software evolution. We use a model to investigate how bots playing certain roles will have differential effects on human editors. In particular, we build on previous research on newcomers by studying the relationship between the roles bots play, the interactions they have with newcomers, and the ensuing survival rate of the newcomers.