This paper examines how player flexibility – a player’s willingness to engage in a breadth of options or specialize – manifests across two gaming environments: League of Legends (League) and Teamfight Tactics (TFT). We analyze the gameplay decisions of 4,830 players who have played at least 50 competitive games in both titles and explore cross-game dynamics of behavior retention and consistency. Our work introduces a novel cross-game analysis that tracks the same players’ behavior across two different environments, reducing self-selection bias. Our findings reveal that while games incentivize different behaviors (specialization in League versus flexibility in TFT) for performance-based success, players exhibit consistent behavior across platforms. This study contributes to long-standing debate about agency versus structure, showing individual agency may be more predictive of cross-platform behavior than game-imposed structure in competitive settings. These insights offer implications for game developers, designers and researchers interested in building systems to promote behavior change.

STRIVE is a taxonomy for multiplayer games organized around player behavior features such as sociality, communication, and experience. We extend STRIVE from Sky: Children of the Light, a relationship-focused social game, to World of Tanks (WoT), a game centered on team combat, player clans, and competitive performance. Using three player data snapshots from 2020 and the original STRIVE framework, we recover four stable player types in WoT: Veterans, Socialites, Squad Players, and Newbies. These segments persist across time and align with the original STRIVE dimensions, while adding a WoT-specific performance dimension. Prediction experiments illustrate that future battle participation varies in predictability across player types: Veterans and Squad Players are consistently more predictable than Socialites and Newbies. Together, these findings support STRIVE as a generalizable framework for comparing social play across multiple games.

We introduce STRIVE, a framework to build socio-behavioral taxonomies in multiplayer online games using unsupervised learning on common features across many social games.This work demonstrates this framework on "Sky: Children of the Light," a social adventure game by Thatgamecompany, using features such as cooperative play, social bonding, and in-game communication.After performing descriptive statistics, clustering and dimensionality reduction, we assign semantic categories to these behavior clusters.Next we perform a behavior prediction experiment where the most social cluster's behavior has a higher correlation with future play time and chats sent than predicting on the full dataset.These results suggest the importance of customized, or personalized, player behavior prediction models.Moreover, this framework could be easily extended to other games and further augment our understanding of human behavior in virtual worlds, ultimately aiding social scientists and game designers to better match players together for healthier online interactions.

The present research on team collaboration is typically performed through qualitative interview based studies or social network measurements of connectedness through co-play. In this study, we take the unique approach to build networks from direct messages between players in the massive online game World of Tanks where players self-organize into clans with specific roles assigned from military rankings (from Private to Commander). We explore the relationship between team communication volume and skill level, the impact of communication features on clan rating, and the differences in communication hierarchy between high and low-rated clans. Our findings reveal that higher-rated clans send more pre-battle chat messages, suggesting that effective communication and strategic planning are key to team performance. Evidence shows teams who use voice chat during battle are significantly higher ranked. Finally, we reveal that the highest rated clans have more connected lower-ranked members emphasizing that these teams are ''only as strong as their weakest link.'' This research is guided by the Transactive Memory Systems and Collective Intelligence theories which serve to expand the contribution of this research outside of games to other forms of virtual collaboration.

The use of social robots in service tasks is spreading, showcasing advantages for both consumers and service providers. However, their widespread adoption is hindered by a notable lack of trust. Our study aims to uncover insights into the factors influencing the adoption of social robots in home settings, exploring the factors that lead users to trust and eventually adopt robots. We designed two experimental conditions, presenting the Amazon Astro robot from different perspectives (high-angle and eye-level) and demonstrating its different abilities to 198 people recruited from MTurk. We employed both quantitative (trust, first impressions of warmth and competence as well as usability, familiarity, and attitudes) questionnaires and qualitative (word analysis) assessments, and results showed that participants had higher trust scores when seeing the robot from an eye-level perspective. In addition, usability, familiarity and competence were shown to explain a significant amount of variance in trust. While existing negative attitudes towards robots and the participants’ age were shown to be the strongest predictors for participants’ willingness to purchase a robot, trust was able to significantly affect use intention. We contribute to the broader understanding of the challenges and opportunities in integrating social robots into daily life, shedding light on the dynamics between technological innovation and consumer adoption.

This article studies the effects and implications of sybils (secondary accounts created by a person in an online platform) through the game World of Tanks from interdisciplinary, mixed-methods perspectives. Considering sybils allows us to access a "person based'' network, instead of an "account based" network, revealing formerly undetected patterns. We move on to behavioral differences between the "parent" (the initial account) and "child" (those created afterwards) accounts in a sybil relationship. We explore the behavioral patterns of sybils using network, chat, and gameplay data. We find that sybils represent players experimenting with new roles or features without damaging their play record. We find that there are significant behavioral differences between different sybil accounts, and we leverage them to build a machine learning classifier to differentiate sybils. This classifier is able to identify sybil accounts with over 95% accuracy for sybil/non-sybil, 61% for parent/child. This study demonstrates the underexplored but rich potential for sybils to improve research and industry practitioners' understandings of user practices and experiences.

This study employed an experiment to test participants’ perceptions of an artificial intelligence (AI) recruiter. It used a 2 (Specialist AI/Generalist AI) × 2 (Sexist/nonsexist) design to test the relationship between these labels and the perception of moral violations. The theoretical framework was an integration of the Computers Are Social Actors (CASA) and Elaboration Likelihood Model (ELM) approaches. Participants (n = 233) responded to an online questionnaire after reading one of four scenarios involving an AI recruiter’s evaluation of job candidates. Results found that the concept of “mindlessness” in CASA is situational, based on whether the issue is processed with the central route or the peripheral route. Moreover, this study shows that CASA can explain the evaluation of machines with the third-person point of view. Also, there was a distinction between the perception of the AI and its decisions. Furthermore, participants were found to be more sensitive about the AI agent’s sexism – which was more anthropomorphic and emotionally engaging – than about the AI agent’s status as a specialist.

How do social ties in online worlds evolve over time? This research examined the dynamic processes of relationship formation, maintenance, and demise in a massively multiplayer online game. Drawing from evolutionary and ecological theories of social networks, this study focuses on the impact of three sets of evolutionary factors in the context of social relationships in the online game EverQuest II (EQII): the aging and maturation processes, social architecture of the game, and homophily and proximity. A longitudinal analysis of tie persistence and decay demonstrated the transient nature of social relationships in EQII, but ties became considerably more durable over time. Also, character level similarity, shared guild membership, and geographic proximity were powerful mechanisms in preserving social relationships.

The term 'Gold Farmer' refers to a class of players in massive online games (MOGs) involved in a set of interrelated activities which are considered to be deviant activities. Consequently these gold farmers are actively banned by game administrators. The task of gold farmer detection is to identify gold farmers in a population of players but just like other clandestine actors they not labeled as such. In this paper the problem of extending the label of gold farmers to players which are not labeled as such is considered. Two main classes of techniques are described and evaluated: Network-based approaches and similarity based approaches. It is also explored how dividing the problem further by relabeling the data based on behavioral patterns can further improve the results