In Massively Multiplayer Online Role-Playing Games (MMORPGs), auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions require explainable justification to avoid legal and user experience issues. In this paper, we present a novel framework for detecting auto-leveling bots by leveraging contrastive representation learning and clustering techniques in a fully unsupervised manner to identify groups of characters with similar level-up patterns. To ensure reliable decisions, we incorporate a Large Language Model (LLM) as an auxiliary reviewer to validate the clustered groups, effectively mimicking a secondary human judgment. We also introduce a growth curve-based visualization to assist both the LLM and human moderators in assessing leveling behavior. This collaborative approach improves the efficiency of bot detection workflows while maintaining explainability, thereby supporting scalable and accountable bot regulation in MMORPGs.

In MMORPGs (Massively Multiplayer Online Role-Playing Games), abnormal players (bots) using unauthorized automated programs to carry out pre-defined behaviors systematically and repeatedly are commonly observed. Bots usually engage in these activities to gain in-game money, which they eventually trade for real money outside the game. Such abusive activities negatively impact the in-game experiences of legitimate users since bots monopolize specific hunting areas and obtain valuable items. Thus, detecting abnormal players is a significant task for game companies. Motivated by the fact that bots tend to behave collectively with similar in-game trajectories due to the auto-programs, we developed BotTRep, a framework that comprises trajectory representation learning followed by clustering using a completely unlabeled in-game trajectory dataset. Our model aims to learn representations for in-game trajectory sequences so that players with contextually similar trajectories have closer embeddings. Then, by applying DBSCAN to these representations and visualizing the corresponding moving patterns, our framework ultimately assists game masters in identifying and banning bots.

Another week, another catastrophic failure of policy at Twitter that's being eagerly exploited by its myriad competitors--and they truly are myriad. And yet, in spite of the momentary success of some of these platforms--Threads has gotten over 70 million signups as of this writing--none has quite ascended to the lofty heights of Twitter's influence at its height, where it seemed, for good or for ill (let's be honest, mostly ill), to be at the heart of every conversation among our world's epistemic elites. To understand why, we have to go to Azeroth. Black Mirror creator Charlie Brooker once, tongue-in-cheek, called Twitter the best video game of all time, likening it to the then-still-popular wave of Massively Multiplayer Online Roleplaying Games, or MMORPGs, that were led by titles like World of Warcraft. Aside from the obvious connections--adopting an online persona in a gamified system entirely governed by earned metrics--we can also look at the fact that Twitter, like World of Warcraft, is surrounded by failed imitators.

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.

We have developed a method for detecting real money traders (RMTers) to support the operators of massively multiplayer online role-playing games (MMORPGs). RMTers, who earn currency in the real world by selling properties in the virtual world, tend to form alliances and frequently exchange a huge volume of virtual currency within such a community. The proposed method exploits (1) the trading network, to identify the communities of characters, and (2) the volume of trades, to estimate the likelihood of communities and characters becoming engaged in real money trading. The results of an experiment using actual log data from a commercial MMORPG showed that using the trading network is more effective in detecting RMTers than conventional machine learning methods that assess individual character without referring to the trading network.

In this paper, we discuss motivations for studying interactive narrative in shared, persistent worlds using the established conventions of quest-based MMORPGs. We present a framework for categorizing the various techniques used in these games according to the interaction between the world model and the quest model. Using this framework we generalize recent games to present a more dynamic world model, and investigate extensions to the quest model to support storytelling through adaptive quest narratives.

After four delays spanning nearly a year and a half of missed release dates, New World is finally here. You can download the MMORPG from Steam and Amazon's own marketplace. At launch, the title is available through a $40 Standard Edition or $50 Deluxe Edition. The latter comes with the game, a digital artbook and a collection of bonus items. Beyond the cost of entry, you don't need to pay a subscription fee to play New World. If you buy the game and you're an Amazon Prime subscriber, you can claim the Pirate Pack for free until November 1st.

The sky in Final Fantasy XIV is full of catgirls on broomsticks and elves on dragonback. In World of Warcraft, orcs glide along in giant metal rockets and humans steer horse-sized birds across miles of desert. In the decade-plus since flying first came to massively multiplayer online role-playing games, digital airspace has become as populated as the ground, maybe even more so. When game developers introduced mounted flying to online superhero game City of Heroes and World of Warcraft in the mid-aughts, it changed the MMORPG genre forever--both for better and for worse. One of humanity's greatest wishes, it turns out, has sparked major controversy in the world of video games.

To stay away from Azeroth--which is to remain unsubscribed from Blizzard Entertainment's enduring MMORPG, World of Warcraft--is no simple task. In fact, the gaming community has long (and only half-jokingly) referred to the orc- and elf-filled game as "World of Warcrack." As somebody who, over the last 14 years, has racked up over 600 days played, the pull of WoW's constant new dungeons, raids, and battlegrounds is something I can attest to. When I'm at a loose end, the first thing that comes to mind is logging on my level 60 rogue. And if I don't play for an extended period of time, I'll, quite literally, see WoW in my dreams.

Amazon told Bloomberg that it has canned its Lord of the Rings, developed with the help of China's Leyou, after being "unable to secure terms" to continue its development. However, sources claim that Tencent's recent acquisition of Leyou led to a contract dispute that ultimately killed the project. Amazon Game Studios is struggling to get games out there and keep them on sale. It canceled Breakaway in 2018 after lack of progress, and it dropped Crucible after poor feedback during testing. Another MMO, New World, has faced multiple delays and isn't expected to launch until the end of August. While Amazon remains committed to its Game Studios efforts -- its incoming CEO has stated his support -- they clearly haven't delivered results quickly.