Samer Al-Khateeb

As social media has made us more connected, it has also increased our ability to mobilize groups of people and coordinate events, such as mobs. Understanding the motivation of individuals to join such events and the ability to predict the outcome of these events (success or failure) is complex and requires a robust understanding of social science theories. In this research, we aim to analyze the applicability of five social science theories to explain and model mobs, namely: collective action theory, diffusion of innovation theory, lifestyle-routine activity (L-RAT) theory, low self-control theory, and environmental design theory (as developed through Crime Prevention Through Environmental Design (CPTED). By mapping the concepts/factors from these theories and providing potential operationalization methods accordingly, we hope to gain a deeper understanding of the mob phenomenon. The five social science theories comprise shared and unique concepts/factors related to the individuals (i.e., potential mobbers) and the event (i.e., the mob). We identify areas of theoretical overlap, creating a network of connected social science theories to model this phenomenon. Additionally, we discuss methods in which these concepts/factors might be estimated using social media sites such as event-based social media (EBSM) platforms. Finally, we derive a set of 24 sentences (expressed in English and using first-order logic) concerning the factors and rules that contribute to the mobber’s decision and 15 first-order logic sentences that govern the mob outcome.

Recently, there has been a growing trend in using large language models (LLMs) to develop diverse applications suitable for a wide range of tasks. These tasks range from solving programming bugs to helping teach elementary school students how to enhance their writing. Even with all these beneficial use cases, researchers worry about the potential bias these tools could produce and their effect on society. In this research, we compared responses that resulted from prompting two chatbots, namely OpenAI ChatGPT and Google Bard, about the issue of gene editing. Twelve prompts that are part of two subgroups were used to generate responses (text) about the issue of gene editing when the political affiliation (Democrat, Republican, and Communist) or geographical areas (United States, China, and Europe) of the prompter is provided. The Twelve responses were then analyzed semantically using three dictionary-based tools, i.e., Linguistic Inquiry and Word Count, the Moral Foundation Theory and Biblical Ethics dictionary, and Google’s Perspective API, to test and analyze the semantic and linguistic differences (measured via the Mann–Whitney U test) in the responses returned from the two chatbots. The results suggest that there are semantic and linguistic differences in responses per chatbots and prompts.

A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then disperse (quickly or over a long period). In recent years, these events are increasingly happening worldwide due to the anonymity of the internet, affordability of social media, boredom, etc. Studying such a phenomenon is difficult due to a lack of data, theoretical underpinning, and resources. In this research, we use the Agent-Based Modeling (ABM) technique to model the mobbers and the Monte Carlo method to assign random values to the factors extracted from the theory of Collective Action and conduct many simulations. We also leverage our previous research on Deviant Cyber Flash Mobs to implement various scenarios the mobber could face when they decide to act in a mob or not. This resulted in a model that can simulate mobs, estimate the mob success rate, and the needed powerful actors (e.g., mob organizers) for a mob to succeed. We finally evaluate our model using real-world mob data collected from the Meetup social media platform. This research is one step toward fully understanding mob formation and the motivations of its participants and organizers.

Since the year 2003, the term “flash mob” has been studied in various disciplines and was referred to by various media outlets to describe various events. The term "flash mob" originally meant to describe a phenomenon in which a group of people assembles in a public space, perform a seemingly random act, then quickly disperse. This phenomenon was expected to die just like other types of public performances, e.g., the eighties' raves. However, this phenomenon is still alive and became widespread in various forms. Since this phenomenon was new, mentioned in the media, and depended on technology, it is unwise to assume that everyone knows its exact meaning. Many traditionalist flash mobbers become concerned that the global diffusion of flash mobs has diluted their original meaning. So, in this summative, chronological, and by topic literature review article, we explore the disciplines in which this term was used; then investigate its various forms and propose the term “Mob” to be the root term so we can categorize its various forms (branches) and eliminate ambiguities; and prove that this phenomenon still exists. Finally, we try to shed light on what is missing from the literature and what needs to be studied in the future with regard to this topic.