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In early 2021, the stock prices of GameStop, AMC, Nokia, and BlackBerry experienced dramatic increases, triggered by short squeeze operations that have been largely attributed to Reddit's retail investors. These events showcased, for the first time, the potential of online social networks to catalyze financial collective action. How, when and to what extent Reddit users played a role in driving up these prices, however, remains unclear. We address these questions by statistical analysis of time series with high temporal resolution, about social activity on Reddit and Twitter as well as stock trading volumes. We find that increasing Reddit discussions anticipated high trading volume before the GameStop short squeeze, with their predictive power being particularly strong on hourly time scales. This effect emerged abruptly a few weeks before the event, but waned once the community of investors gained widespread visibility through Twitter. Meanwhile, the collective investment of the Reddit community, quantified through each user's financial position on GameStop, closely mirrored the market capitalization of the stock. These evidences suggest a coordinated action by Reddit users in developing a shared financial strategy through social media. Towards the end of January, users talking about GameStop contributed to raise the popularity of BlackBerry, AMC and Nokia, which emerged as the most popular stocks as the community gained global recognition. Overall, our results shed light on the dynamics behind the first large-scale financial collective action driven by social media users.

The cognitive process of opinion formation is often characterized by stubbornness or resistance of agents to changes of opinion. To capture such a feature we introduce a constant latency time in the standard voter model of opinion dynamics: after switching opinion, an agent must keep it for a while. This seemingly simple modification drastically changes the stochastic diffusive behavior of the original model, leading to deterministic dynamical oscillations in the average opinion of the agents. We explain the origin of the oscillations and develop a mathematical formulation of the dynamics that is confirmed by extensive numerical simulations. We further characterize the rich phase space of the model and its asymptotic behavior. Our work offers insights into understanding and modeling opinion swings in diverse social contexts.

People nowadays express their opinions in online spaces, using different forms of interactions such as posting, sharing and discussing with one another. How do these digital traces change in response to events happening in the real world? We leverage Reddit conversation data, exploiting its community-based structure, to elucidate how offline events influence online user interactions and behavior. Online conversations, as posts and comments, are analysed along their temporal and semantic dimensions. Conversations tend to become repetitive with a more limited vocabulary, develop at a faster pace and feature heightened emotions. As the event approaches, the shifts occurring in conversations are reflected in the users' dynamics. Users become more active and they exchange information with a growing audience, despite using a less rich vocabulary and repetitive messages. The recurring patterns we discovered are persistent across a wide range of events and several contexts, representing a fingerprint of how online dynamics change in response to real-world occurrences.

The patterns of human mobility play a key role in the spreading of infectious diseases and thus represent a key ingredient of epidemic modeling and forecasting. Unfortunately, as the Covid-19 pandemic has dramatically highlighted, for the vast majority of countries there is no availability of granular mobility data. This hinders the possibility of developing computational frameworks to monitor the evolution of the disease and to adopt timely and adequate prevention policies. Here we show how this problem can be addressed in the case study of Italy. We build a multiplex mobility network based solely on open data, and implement a SIR metapopulation model that allows scenario analysis through data-driven stochastic simulations. The mobility flows that we estimate are in agreement with real-time proprietary data from smartphones. Our modeling approach can thus be useful in contexts where high-resolution mobility data is not available., Comment: 10 pages, 3 figures

The short squeeze of GameStop (GME) shares in mid-January 2021 has been primarily orchestrated by retail investors of the Reddit r/wallstreetbets community. As such, it represents a paramount example of collective coordination action on social media, resulting in large-scale consensus formation and significant market impact. In this work we characterise the structure and time evolution of Reddit conversation data, showing that the occurrence and sentiment of GME-related comments (representing how much users are engaged with GME) increased significantly much before the short squeeze actually took place. Taking inspiration from these early warnings as well as evidence from previous literature, we introduce a model of opinion dynamics where user engagement can trigger a self-reinforcing mechanism leading to the emergence of consensus, which in this particular case is associated to the success of the short squeeze operation. Analytical solutions and model simulations on interaction networks of Reddit users feature a phase transition from heterogeneous to homogeneous opinions as engagement grows, which we qualitatively compare to the sudden hike of GME stock price. Although the model cannot be validated with available data, it offers a possible and minimal interpretation for the increasingly important phenomenon of self-organized collective actions taking place on social networks.