An empirical study of game reviews on the Steam platform

Lin, Dayi; Bezemer, Cor‐Paul; Zou, Ying; Hassan, Ahmed E.

doi:10.1007/s10664-018-9627-4

Cited by 79 publications

(45 citation statements)

References 39 publications

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“…However, sharing experiences and opinions did not remain within a circle of friends, but the information was also shared with a wider audience on the Internet, usually in the form of game reviews. Digital game reviews play a major role in the commercial success of digital games (Livingston et al, 2011) and are also feedback tools for game developers (Lin et al, 2019). According to Livingston et al (2011), peer and expert reviews equally influence gamers' perceptions of a game.…”

Section: Creating and Sharing Activitiesmentioning

confidence: 99%

Children’s gaming involves much more than the gaming itself: A study of the metagame among 12- to 15-year-old children

et al. 2020

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In addition to gaming, there are many other activities around digital games. These metagame activities have so far been studied from the perspective of single metagame phenomena and rarely from the perspective of the children who play digital games. This exploratory, qualitative study provides an overview of children’s metagame activities. A total of 142 children’s essays and lists of their metagame activities were analyzed using qualitative content analysis. The children’s metagame activities included game-enabling activities, strategizing activities, discussing activities, information-seeking activities, creating and sharing activities, and consuming activities. The results contribute to the body of literature on metagaming and provide an overview of children’s metagame activities around digital games, as well as new perspectives on digital games and learning.

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Section: Creating and Sharing Activitiesmentioning

confidence: 99%

Children’s gaming involves much more than the gaming itself: A study of the metagame among 12- to 15-year-old children

et al. 2020

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“…In particular, Johnson et al [28] call for a textual analysis of amateur reviews to explore further dimensions of player experience. This is a research gap which (i) Thominet [68] explores in a manual study of 180 amateur reviews on the online game shop Steam 2 , (ii) Lin et al [40] inspect in reviews of over 6224 games also on Steam, and (iii) we also address. Past correlational [57] and textual [31] studies of expert and amateur reviews on Metacritic cover the most similar kind of dataset to the one we handle, but those studies do not go beyond a static, small-scale analysis.…”

Section: Related Workmentioning

confidence: 98%

What's in a Review

Santos

Lemmerich

Strohmaier

et al. 2019

Proc. ACM Hum.-Comput. Interact.

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As video game press ("experts") and casual gamers ("amateurs") have different motivations when writing video game reviews, discrepancies in their reviews may arise. To study such potential discrepancies, we conduct a large-scale investigation of more than 1 million reviews on the Metacritic review platform. In particular, we assess the existence and nature of discrepancies in video game appraisal by experts and amateurs, and how they manifest in ratings, over time, and in review language. Leveraging these insights, we explore the predictive power of early expert vs. amateur reviews in forecasting video game reputation in the short-and long-term. We find that amateurs, in contrast to experts, give more polarized ratings of video games, rate games surprisingly long after game release, and are positively biased towards older games. On a textual level, we observe that experts write rather complex, less readable texts than amateurs, whose reviews are more emotionally charged. While in the short-term amateur reviews are remarkably predictive of game reputation among other amateurs (achieving 91% ROC AUC in a binary classification), both expert and amateur reviews are equally well suited for long-term predictions. Overall, our work is the first large-scale comparative study of video game reviewing behavior, with practical implications for amateurs when deciding which games to play, and for game developers when planning which games to design, develop, or continuously support. More broadly, our work contributes to the discussion of wisdom of the few vs. wisdom of the crowds, as we uncover the limits of experts in capturing the views of amateurs in the particular context of video game reviews.

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“…Formally, a policy is a probability distribution that maps actions over given states. State-action-reward-state-action (Sarsa) is an on-policy modelfree temporal difference learning algorithm which is shown in (1). Q(s, a) defines the Q-function which represents the expected total reward of taking action a in state s. R(s, a) represents the immediate reward of taking action a ∈ A in state s ∈ S, α is the learning rate, and δ is the temporal difference error.…”

Section: B Reinforcement Learningmentioning

confidence: 99%

Automated Video Game Testing Using Synthetic and Humanlike Agents

2021

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In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like-and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGP-IRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance.

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An empirical study of game reviews on the Steam platform

Cited by 79 publications

References 39 publications

Children’s gaming involves much more than the gaming itself: A study of the metagame among 12- to 15-year-old children

Children’s gaming involves much more than the gaming itself: A study of the metagame among 12- to 15-year-old children

What's in a Review

Automated Video Game Testing Using Synthetic and Humanlike Agents

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