Steam Platform Research Articles

Digital entertainment in the face of COVID-19

The work analyzed the impact of the Covid-19 pandemic on digital entertainment. The focus is on three types of digital entertainment: computer games, virtual reality and streaming, respectively. The first two types of entertainment were analyzed based on data from the Steam platform, while streaming was analyzed based on Twitch. Data were collected from 2019, i.e. the period before the pandemic, and 2020, i.e. the time of the pandemic. Then the data from both of these years were compiled for analysis. The study particularly focused on events related to the pandemic, such as the March declaration of the Covid-19 virus as a pandemic by the WHO or the holiday period in which the restrictions were reduced.

A large-scale study of changes to the quantity, quality, and distribution of video game play during a global health pandemic.

Video game play has been framed as both protective factor and risk to mental health during the Coronavirus disease (COVID-19) pandemic. We conducted a statistical analysis of changes to video game play during the pandemic to better understand gaming behavior and in doing so provide an empirical foundation to the fractured discourse surrounding play and mental health. Analyses of millions of players’ engagement with the 500 globally most popular games on the Steam platform indicated that the quantity of play had dramatically increased during key points of the pandemic; that those increases were more prominent for multiplayer games, suggesting that gamers were seeking out the social affordances of video game play; and that play had become more equally distributed across days of the week, suggesting increased merging of leisure activities with work and school activities. These results provide a starting point for empirically grounded discussions on video games during the pandemic, their uses, and potential effects.

Supplemental Material for A large-scale study of changes to the quantity, quality, and distribution of video game play during a global health pandemic.

Video game play has been framed as both protective factor and risk to mental health during the Coronavirus disease (COVID-19) pandemic. We conducted a statistical analysis of changes to video game play during the pandemic to better understand gaming behavior and in doing so provide an empirical foundation to the fractured discourse surrounding play and mental health. Analyses of millions of players’ engagement with the 500 globally most popular games on the Steam platform indicated that the quantity of play had dramatically increased during key points of the pandemic; that those increases were more prominent for multiplayer games, suggesting that gamers were seeking out the social affordances of video game play; and that play had become more equally distributed across days of the week, suggesting increased merging of leisure activities with work and school activities. These results provide a starting point for empirically grounded discussions on video games during the pandemic, their uses, and potential effects.

Explaining and Predicting Helpfulness & Funniness of Online Reviews on the Steam Platform

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

The Design of Intelligence Analysis Platform Based on Big Data

There are many problems in the current intelligence analysis work. Data islands and data transmission monitoring problems have severely restricted the further development of intelligence analysis work. To promote and improve the level of intelligence analysis work, this article mainly introduces how to apply big data to intelligence analysis. It enters into the analysis of the problems faced by the current intelligence analysis platform, focusing on the importance of big data technology for intelligence analysis. The structure and key technologies of big data can effectively solve various problems existing in intelligence analysis work. The structure of the intelligence analysis platform based on big data and the structure of the Hadoop+Spark+Steam platform are designed, including intelligence analysis and intelligence analysis. Functional modules such as data management and intelligence storage. This platform design of this article can effectively improve the effect and efficiency of intelligence analysis, and has very high practical value.

Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

Large-Scale Cross-Game Player Behavior Analysis on Steam

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.

MyBotS Prototype on Social Media Discord with NLP

The continuous growth in technology and technological devices has led to the development of machines to help ease various human-related activities. For instance, irrespective of the importance of information on the Steam platform, buyers or players still get little information related to the application. This is not encouraging despite the importance of information in this current globalization era. Therefore, it is necessary to develop an attractive and interactive application that allows users to ask questions and get answers, such as a chatbot, which can be implemented on Discord social media. Artificial Intelligence is a technique that allows machines to think and be able to make their own decisions. This research showed that the discord chatbot prototype provides various services based on the results of classification testing using the SVM method with three kernels, namely Linear, Polynomial, and RBF. The test data and accuracy values prediction are the largest Liniear Kernel SVM with accuracy and error prediction values of 94% and 6%.

A Data-Driven Approach for Video Game Playability Analysis Based on Players’ Reviews

Playability is a key concept in game studies defining the overall quality of video games. Although its definition and frameworks are widely studied, methods to analyze and evaluate the playability of video games are still limited. Using heuristics for playability evaluation has long been the mainstream with its usefulness in detecting playability issues during game development well acknowledged. However, such a method falls short in evaluating the overall playability of video games as published software products and understanding the genuine needs of players. Thus, this paper proposes an approach to analyze the playability of video games by mining a large number of players’ opinions from their reviews. Guided by the game-as-system definition of playability, the approach is a data mining pipeline where sentiment analysis, binary classification, multi-label text classification, and topic modeling are sequentially performed. We also conducted a case study on a particular video game product with its 99,993 player reviews on the Steam platform. The results show that such a review-data-driven method can effectively evaluate the perceived quality of video games and enumerate their merits and defects in terms of playability.

Epic, Steam, and the role of skin-betting in game (platform) economies

In this article, we discuss how and why virtual items known as “skins” travel beyond games and into wider online ecosystems where they become tokens in gambling games. We argue that betting with skins purchased on the Steam platform contributes to the wider platform economy. We do this on the basis of a comparative analysis of Counter-Strike: Global Offensive and Fortnite: Battle Royale as well as the two platforms on which these games exist. First, we discuss the notion of platform economies in relation to the two cases in question and how this positions the consumer as “prosumer” and “micro-entrepreneur.” Second, we introduce our analytical framework related to game economies and skins as commodities and currencies. On the basis of this, we compare the ways skins are acquired in the two games and the degree to which they extend exist beyond the game. We argue that Epic retains a “skin-monopoly,” within the game, whereas Steam features a free “skin market” that transgresses the platform and turns the “prosumer” into a “micro-entrepreneur” and the “modder” into a “speculator.”

Positioning game review as a crucial element of game user feedback in the ongoing development of independent video games

The research is designed to determine the extent to which game reviews on the Steam platform can be used as a means of discerning the common needs of players and any flaws in existing games. In this study, the independent game No Man’s Sky is adopted as the subject of the research. Results indicated that the majority of reviews reflect shortcomings of No Man’s Sky and that the developer has acknowledged these shortcomings. Statistical research and the analytic hierarchy process supported that variables on the Steam platform reflect players’ common needs and constitute a model by means of which developers can assess constructive data. Based on these results, the researcher argues that it provides clarification for indie game developers who rely on online reviews on Steam for ideas which can assist them in effecting improvements in-game quality on a constant basis.

COMPARISON OF DURATION PLAYING DEFENSE OF THE ANCIENTS-2 VIDEO GAME WITH REACTION TIME

Background: DotA-2 is the most popular MOBA video game, which ranks the second most sold on the steam platform. Reaction time as a physiological parameter to find out how quickly to respond to a stimulus. Some research states that video games can increase reaction time. Objective: To know the correlation of playing video game Defense of the Ancients-2 with the duration of reaction time. Method: The study used cross-sectional design. This study was conducted in the area of the Faculty of Medicine, Diponegoro University. In this study found 42 respondents participated. Score of reaction time is measured using a ruler dropped from the respondent's fingertips. Analysis using Shapiro-Wilk test and Pearson test. Results: There is significant relationship between playing a DotA-2 status and reaction time. The duration of playing DotA-2 video games to the reaction time there were significant differences between the 3 groups. Conclusion: There is a significant relationship between the status of playing video game DotA-2 with reaction time.

Virtual place during quarantine – a curious case of VRChat

During the first months of the COVID-19 pandemic many governments imposed forced lockdowns and implemented social distancing measures. At the same time there was also a large increase in gaming sales, which was particularly pronounced in the Virtual Reality (VR) sector of the market. We hypothesize that this is no coincidence since VR immersion and the capability of inducing embodiment and a feeling of presence can mitigate the loss of contact with outside world. VR has social and spatial potential to provide space and place for human interactions in time when physical contracts are restricted. To investigate this, we analyse reviews of VRChat (a social VR game) posted on the Steam platform, both before and during the pandemic. Among several themes that were identified, we found indications that spatiality plays an important role in the players’ experience. Users describe virtual worlds of the game using emotional language that suggest bonding and presence of place attachment. In the reviews made during the pandemic there is a strong theme of safety associated with virtual places of VRChat – a replacement of physical space that is no longer accessible or is perceived as unsafe. At least for some users, VRChat has provided a sympathetic and comfortable environment during the pandemic to act as a surrogate for social interaction during social distancing and isolation. Future interviews with users are needed to extend and validate this preliminary research.

Strategies to Map Play Time in Steam Platform Dataset into 5-Star Ratings

Strategies to Map Play Time in Steam Platform Dataset into 5-Star Ratings

Identifying gameplay videos that exhibit bugs in computer games

With the rapid growing market and competition in the gaming industry, it is challenging to develop a successful game, making the quality of games very important. To improve the quality of games, developers commonly use gamer-submitted bug reports to locate bugs in games. Recently, gameplay videos have become popular in the gaming community. A few of these videos showcase a bug, offering developers a new opportunity to collect context-rich bug information. In this paper, we investigate whether videos that showcase a bug can automatically be identified from the metadata of gameplay videos that are readily available online. Such bug videos could then be used as a supplemental source of bug information for game developers. We studied the number of gameplay videos on the Steam platform, one of the most popular digital game distribution platforms, and the difficulty of identifying bug videos from these gameplay videos. We show that naive approaches such as using keywords to search for bug videos are time-consuming and imprecise. We propose an approach which uses a random forest classifier to rank gameplay videos based on their likelihood of being a bug video. Our proposed approach achieves a precision that is 43% higher than that of the naive keyword searching approach on a manually labelled dataset of 96 videos. In addition, by evaluating 1,400 videos that are identified by our approach as bug videos, we calculated that our approach has both a mean average precision at 10 and a mean average precision at 100 of 0.91. Our study demonstrates that it is feasible to automatically identify gameplay videos that showcase a bug.

Avatar economies: affective investment from game to platform

ABSTRACTVideo game avatars have been understood as a key site of players’ “affective investment” in play and games. In this article, we extend this conversation to explore the avatar’s role in engaging players with gaming platforms. Through a case study of Team Fortress 2 (Valve Software, 2007) and the Steam platform, we demonstrate the avatar’s function beyond gameworlds as a tool for encouraging certain kinds of play. Team Fortress 2, we argue, is a crucial testing ground for Valve’s experiments with gaming economies via the Steam platform. By extension, we show the importance of video game avatars for encouraging affective investment in platforms more broadly, including Microsoft’s Xbox Live, PlayStation Network and even workplace dashboards.

Swinging into art with pendulum paintings

Children are captivated with how things work and they like to build things and in many ways, engineering comes naturally for them. Progress does not come from technology alone but from the melding of technology and creative thinking through art and design. There has been a push for STEAM-based curricula to be included in science classrooms and the Next Generation Science Standards (NGSS) provides the framework for integrating engineering design into the structure of science education. The push for the STEAM platform is derived from the lack of creativity and innovation in recent college graduates in the United States. This STEAM-based unit meshes engineering design, representing and interpreting data, visual arts, and motion/stability. As students investigated and analyzed pendulum motion, they also created unique pendulum paintings. Throughout this unit our students applied their content knowledge across several disciplines and in turn allowed them to gain a better understanding and retention of these concepts. Through creating their own pendulum paintings, the students learned about pendulums and how they work, designed and constructed their own pendulums, and applied prior knowledge of forces and motion in a controlled experiment.

An empirical study of game reviews on the Steam platform

The steadily increasing popularity of computer games has led to the rise of a multi-billion dollar industry. Due to the scale of the computer game industry, developing a successful game is challeng...

Hardcore Gamer Profiling: Results from an unsupervised learning approach to playing behavior on the Steam platform

Based on a very large dataset of over 100 million Steam platform users we present the first comprehensive analysis of hardcore gamer profiles by over 700,000 hardcore players (users playing more than 20 hours per week) covering more than 3,300 games. Using an unsupervised machine learning approach we reveal the specific behavioral categories of hardcore players, i.e. First Person Shooter, Team Fortress 2 player, Action game player, Dota 2 player, Strategy and action combiner, Genre-switching player. Subsequently we derive individual patterns of hardcore gamers in the categories found, such as strategy-action games combiner or game switching players. Our results are useful for computer science and information systems scholars interested in individual differences in user behavior as well as practitioners interested in game-designing.