Toxicity is a widespread phenomenon in competitive online video games. In addition to its direct undesirable effects, there is a concern that toxicity can spread to others, amplifying the harm caused by a single player's misbehavior. In this study, we estimate whether and to what extent a player's toxic speech spreads, causing their teammates to behave similarly. To this end, we analyze proprietary data from the free-to-play first-person action gameCall of Duty®: Warzone™. We formulate and implement an instrumental variable identification strategy that leverages the network of interactions among players across matches. Our analysis reveals that all else equal, all of a player's teammates engaging in toxic speech increases their probability of engaging in similar behavior by 26.1 to 30.3 times the average player's likelihood of engaging in toxic speech. These findings confirm the viral nature of toxicity, especially toxic speech, in competitive online video games.

Predictive models are often deployed for decision-making tasks for which they were not explicitly trained. When only partial observations of the relevant state are available, as in most real-world applications, there is a strong possibility of hidden confounding. Therefore, partial observability often makes the outcome of an action unidentifiable, and could render a model’s predictions unreliable for action planning. We present an identification bound and propose an algorithm to account for hidden confounding during model-predictive control. To that end, we introduce a generalized causal sensitivity model for action-state dynamics. We place a constraint on the hidden confounding between trajectories of future actions and states, enabling sharp bounds on interventional outcomes. Unlike previous sensitivity models, ours accommodates hidden confounding with memory, while maintaining computational and statistical tractability. We benchmark on a wide variety of multivariate stochastic differential equations with arbitrary confounding. The results suggest that a calibrated sensitivity model helps controllers achieve higher rewards.

Online competitive action games have flourished as a space for entertainment and social connections, yet they face challenges from a small percentage of players engaging in disruptive behaviors. This study delves into the under-explored realm of understanding the effects of moderation on player behavior within online competitive action games on an example of a popular title - Call of Duty®:Modern Warfare®II. We employ a quasi-experimental design and causal inference techniques to examine the impact of moderation in a real-world industry-scale moderation system. We further delve into novel aspects around the impact of delayed moderation, as well as the severity of applied punishment. We examine these effects on a set of four disruptive behaviors including cheating, offensive username, chat, and voice. Our findings uncover the dual impact moderation has on reducing disruptive behavior and discouraging disruptive players from participating. We further uncover differences in the effectiveness of quick and delayed moderation and the varying severity of punishment. Our examination of real-world gaming interactions sets a precedent in understanding the effectiveness of moderation and its impact on player behavior. Our insights offer actionable suggestions for the most promising avenues for improving real-world moderation practices, as well as the heterogeneous impact moderation has on different players.

Tracking and collecting fast-evolving online discussions provides vast data for studying social media usage and its role in people's public lives. However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics. We propose a dynamic keyword search method to maximize the coverage of relevant information in fast-evolving online discussions. The method uses word embedding models to represent the semantic relations between keywords and predictive models to forecast the future trajectory of keywords. We also implement a visual user interface to aid in the decision making process in each round of keyword updates. This allows for both human-assisted tracking and fully-automated data collection. In simulations using historical #MeToo data in 2017, our human-assisted tracking method outperforms the traditional static baseline method significantly, achieving 37.1% improvement in F-1 score in the task of tracking the top trending keywords. We conduct a contemporary case study to cover dynamic conversations about the recent Presidential Inauguration and to test the dynamic data collection system. Our case studies reflect the effectiveness of our process and also points to the potential challenges in future deployment.