Deep learning for video game genre classification

  • Published: 17 February 2023
  • Volume 82 , pages 21085–21099, ( 2023 )

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video games classification essay

  • Yuhang Jiang 1 &
  • Lukun Zheng   ORCID: 1  

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In this paper, we propose a new multi-modal deep learning framework with a visual modality and a textual modality for video game genre classification. The proposed framework consists of three parts: two deeep networks for textual data and imaginary data, a feature concatenation algorithm, and then a softmax classifier. Video game covers and textual descriptions are usually the very first impression to its consumers and they often convey important information about the video games. Video game genre classification based on its cover and textual description would be utterly beneficial to many modern identification, collocation, and retrieval systems. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of video game genres, many of which are not concretely defined. Second, video game covers vary in many different ways such as colors, styles, textual information, etc, even for games of the same genre. Third, cover designs and textual descriptions may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the video game industry, the cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The computer-based automatic video game genre classification systems become a particularly exciting research topic in recent years. The contribution of this paper is four-fold. First, we compiles a large dataset consisting of 50,000 video games from 21 genres made of cover images, description text, and title text and the genre information. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of genre classification for video games. Third, we developed an efficient and scalable multi-modal framework based on both images and texts. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework outperforms the current state-of-the-art image-based or text-based models. Several challenges are outlined for this task. More efforts and resources are needed for this classification task in order to reach a satisfactory level.

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Amiriparian S, Cummins N, Gerczuk M, Pugachevskiy S, Ottl S, Schuller B (2019) are you playing a shooter again?! deep representation learning for audio-based video game genre recognition. IEEE Transactions on Games

Barr M (2017) Video games can develop graduate skills in higher education students: a randomised trial. Comput Educ 113:86–97

Article   Google Scholar  

Bean AM, Nielsen RK, Van Rooij AJ, Ferguson CJ (2017) Video game addiction: the push to pathologize video games. Professional Psychology Research and Practice 48(5):378

Biradar GR, Raagini JM, Varier A, Sudhir M (2019) Classification of book genres using book cover and title. In: 2019 IEEE international conference on intelligent systems and green technology (ICISGT). IEEE, pp 72–723

Buczkowski P, Sobkowicz A, Kozlowski M (2018) Deep learning approaches towards book covers classification. In: ICPRAM, pp 309–316

Cao C, Liu F, Tan H, Song D, Shu W, Li W, Xie Z (2018) Deep learning and its applications in biomedicine. Genomics, proteomics & bioinformatics 16(1):17–32

Cer D, Yang Y, Kong SY, Hua N, Limtiaco N, John RS, Sung YH (2018) Universal sentence encoder. arXiv: 1803.11175

Chiang H, Ge Y, Wu C (2015) Classification of book genres by cover and title

Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

Clarke RI, Lee JH, Clark N (2017) Why video game genres fail: a classificatory analysis. Games and Culture 12(5):445–465

Costa YM, Oliveira LS, Silla Jr CN (2017) An evaluation of convolutional neural networks for music classification using spectrograms. Applied soft computing 52:28–38

Cui C, Yang H, Wang Y, Zhao S, Asad Z, Coburn LA, Huo Y (2022) Deep multi-modal fusion of image and non-image data in disease diagnosis and prognosis: a Review. arXiv: 2203.15588

Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. Ieee, pp 248– 255

Dieleman S, Brakel P, Schrauwen B (2011) Audio-based music classification with a pretrained convolutional network. In: 12th international society for music information retrieval conference (ISMIR-2011). University of Miami, pp 669–674

Dong M (2018) Convolutional neural network achieves human-level accuracy in music genre classification. arXiv: 1802.09697

Ebner M, Levine J, Lucas SM, Schaul T, Thompson T, Togelius J (2013) Towards a video game description language. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik

Fang J, Grunberg D, Litman DT, Wang Y (2017) Discourse analysis of lyric and Lyric-Based classification of music. In: ISMIR, pp 464–471

Feng D, Haase-Schütz C., Rosenbaum L, Hertlein H, Glaeser C, Timm F, Dietmayer K (2020) Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Trans Intell Transp Syst 22(3):1341–1360

Ferguson CJ (2018) Violent video games, sexist video games, and the law: why can’t we find effects?. Annual Review of Law and Social Science 14:411–426

Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. Journal of computational chemistry 38(16):1291–1307

Gouyon F, Dixon S, Pampalk E, Widmer G (2004, June) Evaluating rhythmic descriptors for musical genre classification. In: Proceedings of the AES 25th International Conference, pp 196–204

Guggisberg M (2020) Sexually explicit video games and online pornography-the promotion of sexual violence: a critical commentary. Aggress Violent Beh 53:e101432–e101432

Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2020) Deep learning for 3d point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv: 1704.04861

Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

Huang Y, Du C, Xue Z, Chen X, Zhao H, Huang L (2021) What makes multi-modal learning better than single (provably). Adv Neural Inf Process Syst 34:10944–10956

Google Scholar  

Iwana BK, Rizvi STR, Ahmed S, Dengel A, Uchida S (2016) Judging a book by its cover. arXiv: 1610.09204

Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90

Keeler KR (2020) Video Games in Music Education: the Impact of Video Games on Rhythmic Performance. Visions of Research in Music Education, (37)

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

Kundu C, Zheng L (2020) Deep multi-modal networks for book genre classification based on its cover. arXiv: 2011.07658

Laurier C, Grivolla J, Herrera P (2008) Multimodal music mood classification using audio and lyrics. In: 2008 Seventh international conference on machine learning and applications. IEEE, pp 688–693

Logan B (2000) Mel frequency cepstral coefficients for music modeling. In: Ismir, vol 270, pp 1–11

Lougheed T (2019) Video games bring new aspects to medical education and training

Lucieri A, Sabir H, Siddiqui SA, Rizvi STR, Iwana BK, Uchida S, Ahmed S (2020) Benchmarking deep learning models for classification of book covers. SN Computer Science 1:1–16

Mater AC, Coote ML (2019) Deep learning in chemistry. Journal of chemical information and modeling 59(6):2545–2559

Mayo MJ (2009) Video games: a route to large-scale STEM education? Science 323(5910):79–82

Newman JA (2013) Videogames. Routledge

Nguyen ND, Huang J, Wang D (2022) A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data. Nature Computational Science 2(1):38–46

Oramas S, Barbieri F, Nieto O, Serra X (2018) Multimodal deep learning for music genre classification. Transactions of the international society for music information retrieval 2018 1(1):4–21

Oramas S, Espinosa-Anke L, Lawlor A (2016) Exploring Customer reviews for music genre classification and evolutionary studies. In: The 17th international society for music information retrieval conference (ISMIR 2016), New York City, United States of America, 7–11 August 2016

Oramas S, Nieto O, Barbieri F, Serra X (2017) Multi-label music genre classification from audio, text, and images using deep features. arXiv: 1707.04916

Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2016) Deep learning for health informatics. IEEE J Biomed Health Inform 21(1):4–21

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

Sahu S, Mitra V, Seneviratne N, Espy-Wilson CY (2019, September) Multi-Modal Learning for speech emotion recognition: an analysis and comparison of ASR outputs with ground truth transcription. In: Interspeech, pp 3302–3306

Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

Squire K (2003) Video games in education. Int J Intell Games Simulation 2(1):49–62

Strubell E, Ganesh A, Mccallum A (2019) Energy and policy considerations for deep learning in NLP. arXiv: 1906.02243

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence

Tang L, Yang ZX, Jia K (2018) Canonical correlation analysis regularization: an effective deep multiview learning baseline for RGB-d object recognition. IEEE Transactions on Cognitive and Developmental Systems 11(1):107–118

Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5):293–302

Zhang W, Lei W, Xu X, Xing X (2016, September) Improved music genre classification with convolutional neural networks. In: Interspeech, pp 3304–3308

Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Transactions on Knowledge and Data Engineering

Zhou DX (2020) Universality of deep convolutional neural networks. Appl Comput Harmon Anal 48(2):787–794

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Jiang, Y., Zheng, L. Deep learning for video game genre classification. Multimed Tools Appl 82 , 21085–21099 (2023).

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Video game genre classification based on deep learning.

Yuhang Jiang , Western Kentucky University Follow

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Lukun Zheng (Director), Zhonghang Xia, Lan Nguyen and Melanie Autin

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Video games have played a more and more important role in our life. While the genre classification is a deeply explored research subject by leveraging the strength of deep learning, the automatic video game genre classification has drawn little attention in academia. In this study, we compiled a large dataset of 50,000 video games, consisting of the video game covers, game descriptions and the genre information. We explored three approaches for genre classification using deep learning techniques. First, we developed five image-based models utilizing pre-trained computer vision models such as MobileNet, ResNet50 and Inception, based on the game covers. Second, we developed two text-based models, using Long-short Term Memory (LSTM) model and the Universal Sentence Encoder model, based on the game descriptions. For the third approach, we constructed a multi-modal fusion model, which concatenates extracted features from one image-based model and one text-based model. We analysed our results and revealed some challenges that exist in the task of genre classification for video games. Some future works are also proposed.

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Classification of Video Games 3 Pages 752 Words

             Video games can be classified into several distinct categories. Though video games are all basically the same thing, a computer program that is made to entertain people. Arguably one of the first video games was Pong on Atari, but since then video games have changed so radically that you can almost forget they are only games when you are playing them. In Today's world Video games range from various console gaming systems like Nintendo 64 and Microsoft's Xbox hooked to televisions to handheld gaming systems that can be carried in your pocket like Nintendo's Game boy. Video games are most often classified into groups that represent what kind of system they are played on, like games that are made for computer systems or games that are made for consoles.              Video games can be put into many different categories based on the way they are played. First person shooter games like Quake are much different then puzzle games like Tetris. The main categories of video games consist of Adventure games, puzzle games, role-playing games, first person shooter games, simulation games, sports games, and strategy games.              Strategy games most closely resemble the game chess where you must manage a small army consisting of different sorts of soldiers (i.e. Pawns and knights). You the player can command a space colony on another planet or Manage a theme park. In strategy games you use your mouse or game pad to manipulate the simulated world the video game creates. For example, in one of the latest strategy games Roller coaster tycoon you can build a theme park and manage every aspect of it from engineering and building of roller coasters and other attractions, to the cleaning of the restrooms with janitors. This is usually done from a third person view meaning you are looking from above.              Adventure games are more like a book, you follow a story in a movie type format. You can follow a prewritten story much like              ...

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Research Article

Evidence that digital game players neglect age classification systems when deciding which games to play

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, School of Arts and the Humanities, Edith Cowan University, Perth, Western Australia, Australia

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Roles Conceptualization, Investigation, Writing – original draft, Writing – review & editing

Affiliation Media and Communication, School of Social Sciences, Faculty of Arts, Business, Law and Education, The University of Western Australia, Perth, Western Australia, Australia

Roles Conceptualization, Formal analysis, Investigation, Project administration, Writing – original draft, Writing – review & editing

Affiliation Department of Learning and Teaching Enhancement, Edinburgh Napier University, Scotland, United Kingdom

Roles Conceptualization, Writing – review & editing

Affiliation College of Arts, Business, Law and Social Science, Murdoch University, Perth, Western Australia, Australia

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

  • Ross Hollett, 
  • Sian Tomkinson, 
  • Sam Illingworth, 
  • Brad Power, 
  • Tauel Harper


  • Published: February 22, 2022
  • Reader Comments

Fig 1

This article considers players’ experiences seeking out new games to play, and their use of the Australian National Classification Scheme in doing so. The global video game industry is booming, with hundreds of games being released each month across numerous platforms. As a result, players have an unprecedented number of games available when choosing what games to purchase. However, a number of confounding issues around the emergent content of games and the subjective nature of game reviewing makes it difficult to relate what kinds of experiences a given game will facilitate. In this study, we surveyed game players in order to find their game platform and acquisition preferences; strategies and experiences when choosing games; and attitudes towards classification systems. Our findings suggest that players find it difficult to choose what games to purchase, and that existing classification systems are mostly only beneficial when choosing games for minors.

Citation: Hollett R, Tomkinson S, Illingworth S, Power B, Harper T (2022) Evidence that digital game players neglect age classification systems when deciding which games to play. PLoS ONE 17(2): e0263560.

Editor: Stefano Triberti, University of Milan, ITALY

Received: February 7, 2021; Accepted: January 22, 2022; Published: February 22, 2022

Copyright: © 2022 Hollett et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This research was partly supported by funding from UWA’s Office of Research and former Faculty of Arts, Business, Law and Education awarded to TH. No additional external funding was received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


It is well-known that the video game industry is growing rapidly and has surpassed the film industry on numerous fronts. By the end of 2020 there were 2.7 billion players worldwide [ 1 ], and the global games market generated $174.9 billion USD, about half of this being generated from mobile games [ 2 ]. There are huge numbers of games being published with over ten thousand released on the popular video game digital distribution service ‘Steam’ alone in 2020, or around 600 to a thousand per month [ 3 ]. With such a saturated and competitive market, it is often difficult and expensive for game developers to reach the right market of players, and for players to easily find games which suit their needs [ 4 ]. Given the sheer number of games available, paired with the subjective nature of game experiences [ 5 ] and misleading advertising practices common within the industry, the complex task of choosing a game to play has become inherently challenging. Consequently, the aim of this study was to better understand the consumer experience of choosing digital games, with a particular focus on evaluating the utility of National Media Classification Systems, such as Australia’s National Classification Scheme.

The pressure for success has led to some misleading marketing tactics in the game industry. There have been several high-profile cases of deceptive marketing practices where game companies have failed to deliver on content and features (e.g., smooth performance) promised during game development. In some cases, these high-profile games are rushed into market with considerable flaws (i.e., “bugs”) which further undermines consumer trust, such as No Man’s Sky [ 6 ], and Cyperpunk 2077 . Further, the need for paid sponsorships have resulted in a lack of trust in games journalism, influencer reviews, and even game developers themselves [ 7 ]. For example, in 2007 GameSpot’s advertising deal with Eidos Interactive led to a journalist being fired for providing a poor review of their game, Kane & Lynch [ 8 ]. Similarly, while rooted in toxic gamer culture, the Gamergate incident of 2014 actually congealed around a common belief in unethical reviewing practices within the industry [ 9 ]. In 2016, Warner Bros. settled charges from the Federal Trade Commission, which asserted that they had failed “to adequately disclose that it paid online ‘influencers,’…thousands of dollars to post positive gameplay videos [of Shadow of Mordor ] on YouTube and social media” [ 10 ]. Because of these issues, players often lack trust in reviews as a guide for game choice, particularly when it comes to big budget AAA releases. In this case National Classification Systems may help players understand the content of games because they are free from commercial bias.

Benefits and limitations of national classification systems

Media classification systems, such as the IARC (International Age Rating Coalition), PEGI (Pan European Game Information) and ESRB (Entertainment Software Ratings Board) operate as a form of media content regulation in various jurisdictions worldwide [ 11 ]. Classification systems have been developed as governments have, in general, progressed from a censorship model to one where all material are provided with classifications and only exceptional material is censored [ 12 ]. Classification systems contain age and/or maturity-based levels, indicating the minimum recommended age one should be to engage with a piece of media or advising the parent/guardian that they should provide guidance to their child depending on their maturity level [ 13 ]. They take elements such as violence, sex, and drug use into account when determining the appropriate audience age range for any given piece of media. They vary country-by-country according to political, cultural, and religious influences [ 13 – 16 ], and there have been calls to design a worldwide system [ 14 ], although it would be significantly challenging to establish a common understanding of what age-appropriate means [ 14 ]. While classification systems’ primary role is to govern “what pleasures, knowledge and experiences are deemed appropriate for minors” [ 17 ] and, more broadly, protect individuals from material they find offensive [ 18 ], these classifications also provide commercially unbiased summaries of game content.

Australia’s ‘National Classification Scheme’, is overseen by the Commonwealth (federal) Government as well as state and territory governments. It involves the ‘National Classification Code’, which was established in May 2005 and was approved by all Commonwealth, State and Territory Censorship ministers [ 18 ]. This document outlines the purpose of the code and the way that media are to be classified. In this code, publications, films, and computer games each have a different system. Computer games can be rated the following way [ 18 ]:

  • RC: Refused Classification.
  • R 18+: “unsuitable for viewing or playing by a minor” (introduced in 2013)
  • MA 15+: “Computer games … that depict, express or otherwise deal with sex, violence or coarse language in such a manner as to be unsuitable for viewing or playing by persons under 15”.
  • M: “Computer games…that cannot be recommended for viewing or playing by persons who are under 15”.
  • PG: “Computer games…that cannot be recommended for viewing or playing by persons who are under 15 without the guidance of their parents or guardians”.
  • G: “All other computer games”.

“The national scheme is implemented through the Commonwealth Classification (Publications, Films and Computer Games) Act 1995”, which is in turn “supplemented by a number of regulations, determinations and legislative instruments” [ 19 ]. The Commonwealth manages the Classification Board and Classification Review Board [ 19 – 21 ], which decide what classification a given piece of media should receive, and manages appeals to that decision. There are also two classification tools available which produce classification decisions via a questionnaire or computer program. These are “The Global Rating Tool for the classification of mobile and online games on participating storefronts”, and “The Netflix Classification Tool” [ 19 ]. States and territories “make laws about how films, computer games, and publications can be distributed, shown and advertised” [ 19 ].

The role of classification systems as cultural arbiter is often problematic. For instance the Australian Classification Board is viewed as quite harsh towards video games [ 22 ], and there is some confusion regarding the M and MA15+ ratings [ 13 ]. Some games that were refused classification in Australia prior to the introduction of the R 18+ rating were available in other countries according to their classification systems, or were reevaluated as MA15+, some with adjustments, others without [ 13 ]. Even after the introduction of the R 18+ rating in 2013, numerous games are still refused classification, typically due to violence or drug use [ 23 ]. Indeed, Australia and Singapore are the only countries where a game can be banned if its content cannot be accommodated by the rating system [ 15 ]. Such concern is evident in other nations’ systems, with the US’s ESRB focusing on protecting young children from violent and sexual content, and the German Unterhaltungssoftware Selbstkontrolle regulating violent shooter games more strictly than others [ 16 , 24 ]. In 2020 the Australian Government announced a review of the National Classification Scheme, and many of the invited submissions sent from public bodies, corporations, and game developers, among others, alluded to the complex and at times opaque nature of the process [ 25 ].

Alternatives to national classification systems

Our research indicates that players engage in numerous alternative methods to choose games. It is understood that players find new games through recommendations from family and friends; YouTubers (or influencers); social media; gaming websites; gaming magazines; TV advertisements; game developer websites; and expos, all carrying varying levels of trust (as noted above in the case of journalism) [ 26 ]. Other approaches include recommendation and tagging systems (such as that used on Steam) [ 27 ], and browsing forums such as reddit.

There is much research on the reasons for why players choose certain games in regard to personality traits and game genres [ 28 – 30 ], but little on the actual seeking out, decision-making, and purchasing process.

Considering the many channels through which players find games to play, the time required to explore these channels, and the inherent difficulty in describing and differentiating game experiences as described above, it is important to understand attitudes towards the game selection process. Specifically, we feel this understanding will benefit not only players, but also game creators/publishers, by justifying the development of more informative systems for describing game content.

The present study

Given the wide variety of strategies available to players to evaluate game suitability and the inherent difficulty of selecting games from the immense volume of available content, we sought to better understand how players make game purchasing decisions and their attitudes towards the experience. We used both quantitative and qualitative methods to explore player experiences so that fixed response categories could be further contextualised with more detailed descriptions. We chose to address the following research questions:

  • What are the leading strategies used by players to choose games to play?
  • Do players positively or negatively appraise the game selection process?
  • Do players regard the game selection process as difficult?
  • What are players’ attitudes towards the Australian National Classification Scheme?
  • Would players be supportive of a more informative classification system?


Participants were 210 digital game players (59% female) who reported playing games for an average of 5.70 hours per week ( SD = 5.60). Players were aged between 17 and 70 years ( M = 31.45, SD = 12.22) and mostly identified as Australian (68%).

Game platform and acquisition preferences.

Participants were requested to select which platform(s) they use to play digital games from a list of popular options (PC, mobile/tablet, PlayStation, etc.). Participants were also requested to select how they access (by purchase or for free) digital games from a list of popular options (online retailers, app stores, bricks-and-mortar stores, etc.). Finally, participants were requested to estimate how much money they spend (in Australian dollars) on digital games (including subscriptions) each year by selecting from several categories which ranged from $0 to More than $1000 .

Game decision strategies and experiences.

Participants were requested to estimate on a 5-point scale (anchored with Never to Always ) how often they make use of popular game decision strategies (e.g., Australian National Classification Scheme, game reviews, etc.). Participants were also given the option to select “other” and enter a text response to capture any other strategies not offered by the provided list. Participants were also requested to rate their experience of choosing a digital game on a 5-point scale (anchored with Strongly disagree to Strongly agree ) for six different positive and negative descriptors (enjoyable, time consuming, frustrating, confusing, boring, satisfying). A further item was included using the same 5-point scale to measure whether participants found it difficult to determine whether a digital game would meet their needs before playing it.

Attitudes towards classification systems.

Participants were requested to estimate on a 5-point scale (anchored with Never to Always ) how often they pay attention to classifications when choosing a game for themselves and (if applicable) for a child (under 16). Participants were also asked two questions on 5-point scale (anchored with Strongly disagree to Strongly agree ) regarding their agreement that the Australian Government classification system already adequately (1) assists them in deciding which games to play and (2) describes the content of digital games in Australia. A further question was asked on the same 5-point scale which sought to measure whether participants would be supportive of a more informative classification system that would assist them in choosing a game that meets their needs. Participants were also given a text option to add further detail explaining their attitudes towards classification systems.

The survey link was distributed via a snowballing method through professional and social networks, with an open invitation to pass the survey along to other interested parties. The survey was at various points also hosted on our research lab’s website and on our Facebook page. A randomly drawn prize of two AU$50 gift vouchers was also used in messaging to incentivise survey completion. All participants provided informed consent for their anonymous responses to be included in this study by completing a consent form before beginning the questionnaire. These procedures were approved by the lead and co-author’s university ethics committees.

Game platform and acquisition preferences

Most players reported using a personal computer/laptop to play digital games (68%), followed by mobile/tablet (59%), PlayStation (33%), Nintendo Switch (27%), Xbox (18%), Nintendo Wii (8%), and Nintendo DS (8%). Most players reported downloading purchased games from an online retailer (e.g., Steam) to a PC or console (58%), followed by downloading free games to a phone or tablet from an app store (57%), downloading free games from an online retailer (e.g., Steam) to a PC or console (43%), purchasing a hardcopy from a bricks-and-mortar retail outlet (34%), downloading purchased games to a phone or tablet (22%), sharing with a friend (19%), purchasing a hardcopy from an online retailer (13%), peer to peer sharing/torrenting (8%), online streaming (7%), and purchasing a digital copy from a bricks-and-mortar retail outlet (4%). We have also reported the response distribution for annual spending of players on digital games (including subscriptions) in Fig 1 , with around half of players spending less than $100 per annum on digital games.


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Game decision strategies and experiences

Fig 2 illustrates the response distributions for the strategies that players reported using to make decisions when seeking a game which meets their needs. Note that most distributions showed reasonable spread except for the use of the Australian Classification System of which more than 70% participants reported never using. Specifically, the most popular strategy was recommendations from people participants knew, followed by game reviews, game trailers, social media, game cover art, and the Australian Classification System.


As 14% of participants selected “other”, their text responses were analysed and were found to suggest a number of novel game decision strategies. For instance, 24% of these text responses identified communities of play as influencing their decisions—these communities included personal networks, community media (such as reddit, Twitch and YouTube), or Steam community ratings. A further 20% listed the game description, and factors such as the games developer, the game’s franchise and the price each accounted for 12% of written responses. Other written explanations for factors that influenced game choice included “game mechanics” (8%) and “time”, “past experience” and trying a torrented version of the game before buying (4% each). Numerous written responses alluded to issues of trust, noting a difference between critic and audience reviews. Respondents were conscious of whether a review is paid, credible, of quality, and is trustworthy.

Fig 3 illustrates the response distributions from participants regarding their experience of the game decision process when choosing a game that meets their needs (note that these data only included participants who purchase games for themselves, N = 172).


Overall, the game decision process appeared to be positive with most participants agreeing that the experience is enjoyable and satisfying. Furthermore, large proportions of participants disagreed that the process is frustrating, confusing, or boring. However, a large proportion (48%) of participants agreed that the process is time consuming.

We also found that around half of participants agreed that it is often difficult to determine whether a game will meet their needs before playing it (see Fig 4 ).


Attitudes towards classification systems

Fig 5 illustrates the attention participants estimated giving to classifications when choosing a game for themselves or a child. Note that the response data here is separated based on whether people buy for themselves ( N = 170) or/and buy for children ( N = 62). Importantly, it is clear that classifications are largely disregarded by participants who were choosing a game for themselves but is useful for participants who are choosing games for children.


Attitudes towards the current classification system and potential new classification systems have been reported in Fig 6 . Importantly, almost half the participants disagreed that the current classification system already adequately assists them when choosing a game. Furthermore, around half of participants agreed that they would be supportive of a more informative system.


Qualitative analysis of attitudes toward classification systems

Participants were asked to write a free text response to the following question:

“Can you explain your attitude toward classification systems when purchasing games?”

The responses to this question were analysed using a conventional approach to qualitative content analysis, with preconceived categories being avoided, and instead being determined by the implementation of the coding process [see e.g. 31 ]. As a result of this analysis, four categories emerged from the data (two of which represented positive attitudes towards the current Australian National Classification Scheme for Computer Games and two of which represented negative attitudes), and we now discuss each of these emergent categories in the context of the participants’ responses.

1. They are needed for children.

This category relates to those participants who noted that the classification system was necessary in order to assist adults when buying video games for under 18s. This was by far the most common category to emerge in terms of why participants chose to engage with the current Australian video game classification system. Typical comments included:

“I only look at classifications when buying/looking for a gift for a child.”
“I follow it very strictly for my children.”

For the most part, participants in this category agreed that the Australian National Classification Scheme was a good idea, and that it served its purpose, but several participants did not think that it was entirely suitable, either because it was over restrictive, or else did not go far enough. The gaming literacy of the participants themselves likely played an important role in their use of the classification system, with regular gamers more likely to use the classifications as the start of a dialogue, rather than as a strict barrier for exclusion, for example:

“If my children want to play a game I’m unfamiliar with, I will use the rating and watch YouTube videos to help make a decision.”
“My eldest son is a gamer with more experience with games than myself. For him I allow him to make his own choices rather and just keep communication open.”

These findings are consistent with the work of Nikken, Jansz [ 32 ], who found that parents’ attitudes towards video game classification systems are associated with the parents’ own gaming and views on positive game effects.

2. They help to identify adult themes.

Of the participants who found aspects of the current Australian National Classification Scheme to be useful, a second (weaker) category to emerge was that they helped participants to identify “adult themes” that they found to be distasteful in video games. These were largely those games that involved violence, drugs, and scenes of a sexual nature, for example:

“They’re useful in deciding if it’s the right game for me. I don’t like to see a lot of violence or nudity in my games so it can be helpful that way.”

Again, in many of these instances the participants found the classifications to be a useful starting point for them to determine if they wanted to play a game or not, rather than a hard rule to stick by:

“If R or MA, I might try and analyse them to determine if this game is right for me, (e.g., if the violence is gratuitous or not).”

3. They are irrelevant for adults.

This category is the most strongly emergent in terms of the partial redundancy of the current Australian National Classification Scheme, with participants noting that once they reached adulthood, they paid very little attention to the classification system. For example:

“Irrelevant. I am mature enough to make my own decisions.”
“I’m over 18 so don’t care about ratings. Don’t influence my decision.”

These responses highlight the need for a classification system that goes beyond simply stating the extent to which games are or are not age appropriate. In some instances, the current classification system even acted to put off mature gamers from games that received a lower ager ranking, for example:

“Games with a lower classification aren’t necessarily bad, I certainly play games designed for younger people, but I know that the people I may purchase for would prefer something they would consider more age appropriate’”.

This category is in opposition to the second category to emerge from the participants’ responses, indicating that whilst some adults would value a classification system to help them identify adult themes, others find such a system to be redundant. These results would point to the need for a classification system that went beyond simply identifying if a video game was “age appropriate” or not, as for many individuals this is not simply a question of age.

4. They are arbitrary and inaccurate.

The second main category to emerge from the data in terms of why the participants found the current Australian National Classification Scheme to be redundant is because they found it to be either arbitrary or inaccurate. The following two comments are representative of this category, and highlight why participants found the current classification system to be ill-serving:

“Violence and implied sexual relationships are rated the same where they aren’t equally ‘difficult’ for younger people to understand.”
“The Australian system is outdated and flawed. It harshly penalises video games that contain drug use but accepts rape and torture.”

This lack of nuance regarding the classification of media texts has long been argued by media scholars [ 33 ] and has been noted as being particularly problematic because of the subjective and affective nature of game play [ 34 ].

The purpose of the present study was to collect data to better understand the digital game selection process and determine what role a current national classification system plays in this process. In addressing our first research question (what are the leading strategies used by players to make game decisions?), we can surmise that players largely turn to communities of play (from friends to Twitch streams) and reviewers for guidance on whether a game will suit them. In regard to our second research question (do players positively or negatively appraise the game selection process?), while we find evidence that the game selection process is largely a positive experience, the adoption of varied selection strategies can be onerous and complicated to navigate, as supported by the 40% of participants who agreed that choosing a game is time consuming. Third, (do players regard the game selection process as difficult?), 50% of our respondents agreed that it was difficult to find a game to suit their needs. Fourth, (what are players’ attitudes towards the Australian National Classification Scheme?), the Australian National Classification Scheme is largely disregarded in the game selection process. Finally and importantly, for our fifth research question (would players be supportive of a more informative classification system?) there was general support for a more informative classification system to help consumers understand game content and make appropriate game selection decisions.

As can be seen from the emergent categories in the qualitative analysis, the responses largely aligned with those of the quantitative responses shown in Fig 5 ; that is, it appears that the current Australian video game classifications are largely disregarded by participants who were choosing a game for themselves but useful for those who were choosing a game for a child or someone that they did not considered to be a “reasonable adult”. These qualitative responses reveal some further nuances into this delineation, and in particular for the participants in this study it would appear that those who choose not to use the classification system do so either because they do not think it is appropriate for them as adults or because they believe it to be inadequate. Similarly, for those who do use the classification system (i.e., mainly those people buying video games for the under 18s), it is clear that the current classification system falls well short of providing all of the information that is needed to help encourage dialogue about the suitability of games for the intended audience.

The lack of perceived utility in game ratings systems can be explained by a number of confounding factors in terms of their use. First, as Flew explains, the rise of digital media has brought many complications to the way that classification systems are developed and operate [ 12 ]. The Internet facilitates fast, decentralised content production, distribution, and consumption through many content producers [ 12 ]. As a result it is recognised that the vast majority of online content will never be formally classified, with the responsibility for censorship essentially falling to service providers (such as Apple in the case of the App Store, and Google in the case of YouTube) [ 11 ]. This means that ratings, classification and censorship often take place without any public oversight or awareness. This lack of transparency is a product of both “platformisation” and consumer expectations. While the Australian Classification Board does have a publicly accessible database of information that provides more explanation of their ratings decisions, presenting this information as part of packaging is not industry practice and so there is very little consumer knowledge about its existence.

Second, the definition of “community expectations” used for ratings purposes is contested and not necessarily respected by game players [ 12 ]. Classification systems are subject to review to align with such expectations and the Australian Classification website contains numerous community research reports that demonstrate engagement with community expectations [ 35 , 36 ]. However the difficulty in defining what an adult finds reasonable may result in the perception that certain media content is unfairly classified [ 17 ], and at times governments may fail to quickly align with shifting public interests [ 37 ].

Third, as digital platforms such as YouTube are global, “there is a non-correspondence between their geographical space of activity and national territorial jurisdictions”. To ease the classification process for developers seeking to release content worldwide, the Australian board is encouraged to match up with other nations, and work with content and service providers [ 11 , 14 ]. In 2017, the Australian Government approved the use of the International Age Rating Coalition tool, an international classification system, for mobile games. To use the tool, a game developer answers a questionnaire that generates an Australian rating [ 38 ]. Fourth, it is now less clear what counts as “media content”, and what counts as “personal communication”, and how those two types of media experience can be regulated simultaneously [ 12 ]. In light of these issues it is clear that the National Classification Scheme must be continually developed to be useful in the digital age to remain relevant to the public.

One of the key implications of this research is that national classification systems only service a fraction of consumers (parents and children), with the remaining market needing to adopt varied strategies to make game selection decisions. Indeed, a recent report by Bond University as part of their ongoing “Digital Australia” research indicates that 78% of Australian players are aged 18 years or older with an average age of 34 years [ 39 ]. Accordingly, there is currently unprecedented demand for commercially unbiased classifications of games which extends beyond “what pleasures, knowledge and experiences are deemed appropriate for minors” [ 17 ], and protecting individuals from material they find offensive [ 18 ]. Because National Classification Systems are essentially funded by the tax payer, they offer a unique opportunity to provide unbiased and standardised evaluations of games which could be broadly valuable to the digital game consumer. That is, they could represent an attractive alternative to more complex, time consuming, and untrustworthy sources. However, our results show that, in their current form, National Classification Systems are not currently attractive to adult players when choosing games for themselves.

We have already articulated several reasons why National Classification Systems are limited in their scope for addressing the needs of players more broadly, including the subcontracting of censorship to producers and distributors (e.g., Apple, Google) [ 11 , 12 ], ambiguity regarding the definition of a “reasonable adult” [ 12 , 15 ], increasing need for globalised and transparent classification standards [ 11 , 14 ], and ambiguity regarding what counts as media content [ 12 ]. The resolution of these issues represents a complex task which would involve considerable resources. However, given the size of the video game market, the revenue it produces, and the number of stakeholders involved, there is a strong impetus to further research and address these issues. A well-defined, transparent and internationalised classification scheme would allow developers to design their games more strategically, distributors to promote games more accurately, and players to find suitable games more easily.


While we believe our study makes a useful contribution towards justifying the development of more informative digital game classification systems, we acknowledge that our methodology had several limitations. Firstly, we relied on a self-selected sample recruited via online survey methods. As a result the quality of the responses may have been compromised by the anonymity and uncontrolled nature of the data collection. Similarly because our survey was initially distributed through Australian networks, our data is likely to be skewed by a predominantly Australian selection of respondents, who were asked to answer questions about the Australian Classification System. We can therefore only make conclusions regarding attitudes towards the Australian Classification System and recommend that similar research be conducted in other regions to understand whether similar attitudes exist for National Classification Systems broadly, or if they are unique to the Australian System. Finally, we only secured a relatively small subsample of adults who buy for children and did not seek responses from those under the age of 18. Given that the utility of the Australian Classification system appears to largely relevant to those with age considerations when choosing digital games, further research is needed to determine the value of such systems in younger age groups and for parents/guardians.

However, we believe that the mixed method approach to our research has enabled us to negotiate these limitations and produce results that we suspect are replicable in studies of the utility of other classification systems, and therefore have enhanced the understanding of the benefits and limits of a National Classification System in general. We encourage future researchers to utilize similar approaches when exploring the role of National Classification Systems on consumer decisions.


Overall, these results would seem to suggest a more informative classification system would improve the process of choosing games for the majority of consumers. While there seems to be good awareness of the value of the existing classification system for parents (and others) who are choosing games for children, it is equally clear that the existing classification system is not used to help guide game choice for most adults. Given the subjective but powerful nature of gameplay experiences, we would suggest any future classification system for games should not only highlight the severity and occurrence of adult themes in video games but should also use data from reviews and communities of play to help adults more readily identify the right game for them. As video games become increasingly prevalent and internationalised there is a clear imperative to create an improved system for relating the affective content of games in a sophisticated, nuanced and universally respected format. Ultimately the challenge lies in developing a classification system which adequately describes game content, whilst efficiently delivering this information to players in a standardised and unbiased format.

Supporting information

S1 file. why don’t we play games..

S1 Data. Why don’t we play games?.


The authors would like to thank all of the people who participated in this study, and the several colleagues who helped to pilot earlier versions of the survey.

  • 1. Newzoo. 2020 Global Players Per Region . 2020 [cited 2021 January 18]; .
  • 2. Newzoo. 2020 Global Games Market Per Device & Segment . 2020 [cited 2021 January 18]; .
  • 3. SteamSpy. Games released in previous months . 2021 [cited 2021 January 18]; .
  • 4. Ruggill J.E., et al., Inside The Video Game Industry . 2017, New York: Routledge.
  • View Article
  • Google Scholar
  • 7. Prax, P. and A. Soler. Critical Alternative Journalism from the Perspective of Game Journalists . 2016 [cited 2021 January 22]; .
  • 8. Plunkett, L. Yes , a Games Writer was Fired Over Review Scores . 2012 Mar 15 [cited 2021 January 22]; .
  • 10. Federal Trade Commission. Warner Bros . Settles FTC Charges It Failed to Adequately Disclose It Paid Online Influencers to Post Gameplay Videos . 2016 July 11 [cited 2021 January 21]; .
  • 12. Flew, T. Assessing the Knowledge Ecologies of Media Policy : the Case of Content Classification . 2014 [cited 2021 January 27]; .
  • 14. Marston, H. and S. Smith. Understanding the Digital Game Classification System : A Review of the Current Classification System and Its Implications for Use within Games for Health . in International Conference on Human Factors in Computing and Informatics . 2013.
  • 15. Hamid, R.S. and N. Shiratuddin. Age Classification of the Existing Digital Game Content Rating System Across the World : A Comparative Analysis in Knowledge Management International Conference (KMICe) 2018 . 2018. Miri Sarawak, Malaysia.
  • 17. Grealy L. and Driscoll C., The plastic adolescent: Classification and minority, in Cultural Pedagogies and Human Conduct, Watkins M., Noble G., and Driscoll C., Editors. 2015, Routledge. p. 63–77.
  • 18. Australian Government. National Classification Code (May 2005) . F2013C00006 2005 [cited 2021 January 27]; .
  • 19. Australian Government. National Classification Scheme . [cited 2021 3 November]; .
  • 20. Australian Government. Classification Board . [cited 2021 3 November]; .
  • 21. Australian Government. Classification Review Board . [cited 2021 3 November]; .
  • 22. Interactive Games & Entertainment Association. A modern classification scheme for video games . 2020 [cited 2021 January 18]; .
  • 23. Kerr, C. DayZ has been refused classification by the Australian ratings board . 2019 Aug 7 [cited 2021 January 18]; .
  • 24. Walker, I. After 11 Years , Left 4 Dead 2 Is Finally Uncensored In Germany . 2021 Jan 30 [cited 2021 February 01]; .
  • 25. Walker, A. The Most Important Submissions Into Australia’s Classification Review . 2020 Mar 3 [cited 2021 January 18]; .
  • 29. Yee, N. Gaming Motivations Align with Personality Traits . 2016 [cited 2017 February 2]; .
  • 30. Vandenberghe, J. The 5 Domains of Play : Applying Psychology’s Big 5 Motivation Domains to Games . 2012 [cited 2017 February 1]; .
  • 33. Group G.M., Getting the message . 1993, London: Routledge.
  • 34. Bogost I., Persuasive Games : The expressive power of videogames . 2007, Canbridge MA: The MIT Press.
  • 35. Australian Government: Department of Infrastructure, Transport, Regional Development and Comunications. Research and publications . 2020 [cited 2021 January 18]; .
  • 36. Commonwealth of Australia. Classification : usage and attitudes study . 2016 [cited 2021 January 18]; .
  • 38. Australian Government: Department of Communications and the Arts. International Age Rating Coalition (IARC) . 2016 [cited 2021 January 27]; .
  • 39. Brand, J.E., et al., Digital Australia 2020 . 2019, IGEA: Eveleigh, NSW.

Shariff`s Passion Blog

Just another weblog, classification of video games.

There are many classifications of video games. These classifications are also known as genres. Video games can be classified into several distinct genres. Video games are most often classified into groups that represent what kind of system they are played on, like games that are made for computer systems or games that are made for consoles.

These genre include action, shooter, action-adventure, adventure, role-playing, simulation, strategy, puzzles, cards, and race. Video games can be put into many different categories based on the way they are played. Shooter games like Call of Duty are much different then puzzle games like Tetris.

Action video games are games that usually emphasize physical challenges, including hand-eye coordination and reaction time. In most action games, the player typically controls the character of a protagonist. The character must navigate a level, collecting objects, avoiding obstacles, and battling enemies with various attacks. The player wins the game by finishing a sequence of levels.

Shooter video games also test a players’s speed and reaction time. Shooter games tend to focus on a character using some sort of weapon, usually a gun. The purpose of shooter games is to shoot opponents and proceed through missions.

Action-adventure games are video games that combines elements of the adventure game genre with various action game elements. It is perhaps the broadest and most diverse genre in gaming.

An adventure game is a video game in which the player assumes the role of protagonist in an interactive story driven by exploration and puzzle-solving. Nearly all adventure games are designed for a single player.

A role-playing game is a game in which players assume the roles of characters in fictional setting.  Role-playing video games typically rely on a highly developed story and setting which is divided into a number of quests.

Simulation video games are games that are designed to closely simulate aspects of a real or fictional reality. Majority of all games fits into this category.

Strategy video games focus on careful planning and skillful resource management in order to achieve a  goal. These are also know as the thinking games.

Puzzle video games require the player to solve logic puzzles or even navigate complex locations.

The last to genres are cards and race which are self explanatory.

In addition, a video game can fit into many different genres. The more genres a game includes, the better the player enjoys the game. For example, a video game called Grand Theft Auto has included basically all the genres within their video games. Because of this, Grand Theft Auto has become the top selling franchise of a video game.

3 thoughts on “ Classification of Video Games ”

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I had no clue that there were so many genres in the video game industry. You definitely gave great descriptions of all the possible genres. I liked when you threw in some examples because it helped in understanding the genres and the differences between them.

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To someone who has no familiarity to video-games, this post offers a very clear and concise summary of some of the genres, so good job piecing together such a nice overview! Unfortunately, being the video-game nerd that I am, I’m all too familiar with these genres already.

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I like how you give a description of all possible kinds of video games instead of talking about just one! You could go into more detail about which one is the most popular on the market right now in each category or talk about new and upcoming ones ready to be released. This topic has a lot more potential to be more in depth especially if you could relate it to something outside the world of video games like how people attack such entertainment as the source of violence like school shootings.

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Electric Vehicle Charging Infrastructure in the U.S.

64% of americans live within 2 miles of a public charging station, and those who live closest to chargers view evs more positively, table of contents.

  • Distribution of EV charging stations in the U.S.
  • Who lives closest to EV charging stations?
  • Attitudes toward EVs vary based on proximity to chargers
  • Acknowledgments
  • Appendix A: Regression analyses
  • Appendix B: Vehicle-to-charger ratios for each state
  • American Trends Panel survey methodology
  • Additional survey questions
  • Sources for geographic data

video games classification essay

Pew Research Center conducted this study to understand Americans’ views on electric vehicles. We surveyed 10,329 U.S. adults from May 30 to June 4, 2023.

Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

We supplemented the data from the survey with data on EVs and charging stations from the U.S. Energy Department, specifically the Office of Energy Efficiency & Renewable Energy and its Alternative Fuels Data Center . This dataset is updated frequently; we accessed it for this study on Feb. 27, 2024.

The analysis in this report relies on two different measures of community type, one based on what ATP panelists self-reported when asked “How would you describe the community where you currently live?” This measure is used when discussing differences in public opinion towards EV charging infrastructure or related issues and distinguishes between urban, suburban and rural areas. The other measure is based on the U.S. Census Bureau’s urban-rural classification , which identifies urban and rural areas based on minimum housing unit density and/or population density thresholds.

Here are the questions used for this analysis, along with responses, and the survey methodology .

Several recent laws, including the 2021 Infrastructure Investment and Jobs Act and the 2022 Inflation Reduction Act, have sought to encourage the development of electric vehicle infrastructure and increase the adoption of electric vehicles (EVs). And a Pew Research Center survey paired with an analysis of U.S. Department of Energy data finds that roughly six-in-ten Americans now live within 2 miles of a public charger . There were over 61,000 publicly accessible electric vehicle charging stations in the United States as of February 2024.  

A chart showing that About 6 in 10 Americans live within 2 miles of a public EV charger

The vast majority of EV charging occurs at home , but access to public infrastructure is tightly linked with Americans’ opinions of electric vehicles themselves. Our analysis finds that Americans who live close to public chargers view EVs more positively than those who are farther away .

Even when accounting for factors like partisan identification and community type, Americans who live close to EV chargers are more likely to say they:

  • Already own an electric or hybrid vehicle
  • Would consider buying an EV for their next vehicle
  • Favor phasing out production of new gasoline cars and trucks by 2035
  • Are confident that the U.S. will build the necessary infrastructure to support large numbers of EVs on the roads

Here are some other key takeaways from our geographic analysis of EV chargers:

The number of EV charging stations has more than doubled since 2020. In December 2020, the Department of Energy reported that there were nearly 29,000 public charging stations nationwide. By February 2024, that number had increased to more than 61,000 stations. Over 95% of the American public now lives in a county that has at least one public EV charging station.

EV charging stations are most accessible to residents of urban areas: 60% of urban residents live less than a mile from the nearest public EV charger , compared with 41% of those in the suburbs and just 17% of rural Americans.

How Americans view electric vehicles

  • Today’s electric vehicle market: Slow growth in U.S., faster in China, Europe

As of Feb. 27, 2024, there are more than 61,000 publicly accessible electric vehicle charging stations with Level 2 or DC Fast chargers in the U.S. 1 That is a more than twofold increase from roughly 29,000 stations in 2020 . For reference, there are an estimated 145,000 gasoline fueling stations in the country.

EV charging stations can be found in two-thirds of all U.S. counties, which collectively include 95% of the country’s population.

A map showing that Electric vehicle charging stations exist across the country, but most are concentrated in and around urban areas

Distribution by state

As has been the case in the past, California has the most EV charging infrastructure of any state. The state is home to a quarter of all public EV charging stations in the U.S., though this represents a slight decrease from the last time we analyzed this data source in May 2021. At that time, California contained 31% of all public EV charging stations in the U.S.

Californians with an EV might also have a harder time than residents of many states when it comes to the actual experience of finding and using a charger. Despite having the most charging stations of any state, California’s 43,780 individual public charging ports must provide service for the more than 1.2 million electric vehicles registered to its residents. That works out to one public port for every 29 EVs, a ratio that ranks California 49th across all 50 states and the District of Columbia.

At the other end of the spectrum, Wyoming (one-to-six), North Dakota (one-to-six) and West Virginia (one-to-eight) have the most ports relative to the much smaller number of EVs registered in their respective states.

Infrastructure growth in rural areas

Historically, rural parts of the country have had substantially less access to EV charging stations . Addressing that issue has been a focus of recent legislation passed into law. For instance, the 2022 Inflation Reduction Act (IRA) contains tax credits designed to incentivize the installation of EV charging stations outside urban areas.

Since the IRA’s tax credits became active , the number of EV charging stations nationwide has increased 29%. But rural parts of the U.S. have a slightly faster growth rate in their total number of charging stations when compared with urban areas (34% vs. 29%). 2 Even so, access to public EV charging remains heavily concentrated in urban areas, which account for nearly 90% of all stations in the U.S. as of Feb. 27, 2024.

The vast majority of Americans now live in a county with at least one public EV charging station, but some live closer to this infrastructure than others: 39% of Americans live within a mile of a public charging station, and 64% have a charging station within 2 miles of home.

A bar chart showing that City dwellers, Democrats and younger adults are more likely to live near a public EV charger

Americans who live in cities are especially likely to have a public charging station very close to their home. Six-in-ten urban residents live within a mile of a public charger, compared with 41% of suburbanites and just 17% of rural Americans.

Because of this distribution, those who live closest to EV charging infrastructure tend to share the demographic characteristics of urban residents more broadly. For instance, they tend to be relatively young and are more likely to have a college degree than those in other community types.

Looking at political affiliation, 48% of Democrats and Democratic-leaning independents live within a mile of a public charger, compared with 31% of Republicans and Republican leaners.

However, there are no substantial differences in distance to the nearest charger by income. Similar shares of Americans with lower, middle and upper incomes live within a mile of public charging stations.

On the whole, the American public is fairly skeptical that the U.S. will be able to build the infrastructure necessary to support large numbers of EVs on the roads.

A chart showing that Those who live closest to existing charging stations are more confident that the U.S. will build necessary EV infrastructure

Just 17% of U.S. adults say they are extremely or very confident in the country’s ability to develop this infrastructure. But 20% of those who live within a mile of a public charger say they’re extremely or very confident that the U.S. will build the infrastructure necessary to support EVs, almost twice the share (11%) among those who live more than 2 miles from a charging station.

Likewise, those who live closer to public chargers are more likely to favor phasing out production of new gasoline cars and trucks by 2035. This view is held by 49% of those who live within a mile of a public charger, but just 30% of those who live more than 2 miles from one.

Owning – or considering – an electric vehicle

Americans who live near a public charger are a bit more likely to say they currently own an electric vehicle or hybrid. As of June 2023, 11% of those who live within a mile of a public charger said they owned an EV or hybrid; that figure is 7% for those who live more than 2 miles from a charging station.

Those who live close to public charging infrastructure are also much more likely to consider purchasing an EV in the future. Around half of those within a mile of a public charger say they are very or somewhat likely to consider purchasing an EV, compared with just 27% of those for whom the nearest charger is more than 2 miles away.

A dot plot showing that Those who live closest to charging infrastructure are more likely to consider purchasing an EV

These trends persist if we look at urban, suburban and rural areas separately. 3 For instance, just 17% of rural Americans live within a mile of an EV charger, but those who do live close to one are substantially more likely to consider buying an EV in the future (33%) when compared with those who live more than 2 miles from the nearest charging station (21%).

Likewise, Democrats are much more likely than Republicans to say they’d consider buying an EV, but members of both parties are more willing to consider an EV when they live near charging infrastructure.

Just 15% of Republicans who live more than 2 miles from a charger say they are very or somewhat likely to consider an EV for their next vehicle purchase. But among Republicans who live within a mile of a charger, that share is 26%. And although 60% of Democrats living in close proximity to chargers say they’d consider buying an EV, that share drops to 50% among those whose nearest public charger is over 2 miles away.

Does road tripping experience affect attitudes toward EVs?

A dot plot showing that Those who frequently take long road trips and those who don’t have similar attitudes toward EVs

Some transportation experts have suggested that “range anxiety” associated with the need to charge EVs partway through longer road trips is a stumbling block to widespread EV adoption . But our data finds that attitudes toward EVs don’t differ that much based on how often people take long car trips.

In fact, those who regularly drive more than 100 miles are slightly more likely to say they currently own an electric vehicle or hybrid – and also to say they’d consider purchasing an EV in the future – when compared with those who make these trips less often.

  • These charging stations collectively contain more than 164,000 individual ports. ↩
  • The 2022 Inflation Reduction Act uses the Census Bureau’s definition of urban versus rural areas, which defines an urban area as a census block that encompasses at least 2,000 housing units or has a population of at least 5,000. ↩
  • In addition to the results reported here, we used binary logistic regression to explore these (and other) relationships while accounting for other attributes (in regression parlance, while “controlling” for other factors). For more about this methodology and to see the results of that analysis in more detail, refer to Appendix A . ↩

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A Day of Negative Headlines for Trump Points to a Cure for Voter ‘Amnesia’

In roughly 24 hours on Tuesday, former President Donald J. Trump reposted a video with an echo of Nazi Germany, hinted at restricting contraception and made news in two of the criminal cases against him.

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Donald Trump, wearing a suit and a gold tie, stands in a hallway surrounded by people.

By Lisa Lerer and Michael Gold

President Biden has been battling a new phenomenon in American politics: what Democrats and pollsters have taken to calling “Trump amnesia,” a softening of feeling about his predecessor’s tumultuous term as president.

But over roughly 24 hours on Tuesday, Donald J. Trump provided what looked like at least a temporary cure. He reposted a video containing the words “unified Reich,” reviving accusations that he flirts with Nazism. He hinted at the idea of restricting contraception, and rehired as a campaign aide a political operative with a record of accusations of sexual harassment .

A court ruling unsealed later in the day in one of the federal criminal cases against Mr. Trump showed that a judge had questioned how documents with classification markings could have been overlooked repeatedly before they turned up in Mr. Trump’s own bedroom .

And by midmorning, Mr. Trump’s defense rested in a criminal case that threatens to forever affix a label to him that no presidential candidate has yet survived: convicted felon.

Mr. Trump has built a political career on surviving the unsurvivable: No matter how much chaos he creates or how many political norms he shatters, his Republican base stands by him. But now, he is leaving the all-forgiving conservative cocoon to enter the crucible of a general election. Much of his electoral success may depend on whether voters who are not yet irrevocably in his corner recall, are repelled by and reject the turbulence, divisiveness and inflammatory rhetoric that cost him re-election four years ago.

So far, that has not happened. Even as Mr. Trump spends weeks sitting in court as a criminal defendant, he leads in many polls of swing states. Surveys show that views of his administration have improved with distance, with voters remembering those years as a time of economic prosperity and strong national security. While Americans still remember Mr. Trump as a divisive and polarizing figure, a larger share of voters now see his term as better for the country than President Biden’s.

There is plenty of material with which Mr. Biden and his Democratic allies can try to shake voters free of any Trump amnesia. The former president faces four criminal indictments in four different courts. He is implicated in helping foment a siege of the Capitol, and is the first president to have been impeached twice. And under Mr. Trump, Republicans have lost or underperformed in every election since his 2016 victory.

The Biden campaign has focused on directing voters’ attention to issues that it believes will work most in its favor: abortion bans, threats to democracy and the sense of chaos that often consumed the Trump administration.

Which is why Tuesday’s headlines seemed to unspool like a series of gifts from Mr. Trump to his successor.

By Tuesday night, the Biden campaign unwrapped their presents, blasting out a news release highlighting Mr. Trump’s “Awful 24 Hours.”

“Posting Nazi imagery and promising to rip away more freedoms from women is not what we would call a winning campaign strategy,” said Ammar Moussa, a campaign spokesman. “Every day, Donald Trump reminds voters just how extreme and dangerous Donald Trump is.”

At a fund-raiser, Mr. Biden himself scolded Mr. Trump for reposting the “unified Reich” video, attacking him for embracing fascism.

“A unified Reich: That’s not the language of American presidents,” he told hundreds of donors gathered in Boston. “That’s not the language of any Americans. It’s the language of Hitler’s Germany.”

It’s unclear, of course, how many voters can be swayed in their views of Mr. Trump at this point. “People have already come to judgment on the guy,” said Neil Newhouse, a Republican pollster. “There’s not much new they are going to learn that will change their impressions of him.”

Mr. Trump’s campaign believes voters are focused more on broader issues affecting their lives — the economy and affordability, above all — than on any one flurry of controversies. Brian Hughes, a Trump spokesman, pointed to recent polls and fund-raising numbers as signs that the campaign was doing just fine.

Mr. Hughes argued that the Biden campaign had seized on the “unified Reich” video and Mr. Trump’s comments on contraception — and had taken them both out of context — in an effort to distract voters from what he called “the core of what’s at stake” in November.

Still, the Trump campaign’s actions on Tuesday suggested that it recognized some of Mr. Trump’s statements over the past 24 hours as potentially damaging.

After suggesting in an interview with a Pittsburgh television station that he might be open to limits on birth control, or allow states to impose such limits, Mr. Trump quickly slammed the door on the idea.

“I have never, and will never advocate imposing restrictions on birth control, or other contraceptives,” he wrote Tuesday afternoon, almost entirely in capital letters, on Truth Social, his social media site. “I do not support a ban on birth control, and neither will the Republican Party!”

After a wave of criticism, Mr. Trump’s campaign also disavowed the “unified Reich” video and removed it from his social media account. The video had used images of mocked-up newspaper articles as it conjured a vision of America’s future should Mr. Trump win in November. The term “Reich” is often associated with Nazi Germany, though the text in the video, which came from a template, appeared to refer to the decades before World War I.

“This was not a campaign video. It was created by a random account online and reposted by a staffer who clearly did not see the word, while the president was in court,” Karoline Leavitt, a campaign spokeswoman, said in a statement.

Mr. Trump’s response on Tuesday to news about the classified-documents case, however, wound up landing like yet another unforced error. After the four documents in Mr. Trump’s bedroom came to light in a court filing, the former president took to his social media network Tuesday night to accuse Mr. Biden’s administration of authorizing “the F.B.I. to use deadly (lethal) force” against him. “They were authorized to shoot me!” he said in the subject line of a fund-raising email.

Even that, though, set off another round of recrimination. The F.B.I. took the unusual step of disputing Mr. Trump’s claim .

The bureau, it said, had “followed standard protocol in this search as we do for all search warrants, which includes a standard policy statement limiting the use of deadly force.”

“No one ordered additional steps to be taken, and there was no departure from the norm in this matter,” the F.B.I. said.

Lisa Lerer is a national political reporter for The Times, based in New York. She has covered American politics for nearly two decades. More about Lisa Lerer

Michael Gold is a political correspondent for The Times covering the campaigns of Donald J. Trump and other candidates in the 2024 presidential elections. More about Michael Gold

Our Coverage of the 2024 Election

Presidential Race: News and Analysis

President Biden’s campaign released a new advertisement aimed at Black voters . It comes as Donald Trump railed against Biden and the migrant crisis at a rally in the Bronx , the latest in a series of stops campaigning in New York City  in a push to win his home state.

Trump has baselessly and publicly cast doubt about the fairness  of the 2024 election about once a day, on average, a significant escalation since he announced his candidacy for president.

A state dinner held in honor of Kenya, with Barack Obama as a surprise guest , was more about keeping Democratic allies close as campaign season intensifies. Here is the full guest list .

Trump praised Nikki Haley , once his bitter rival for the Republican nomination, a day after she said that she would vote for him , opening the door to bringing Haley into his circle.

Special Legislative Session:  Gov. Mike DeWine of Ohio has called a special session to resolve an issue  that would prevent Biden from being placed on the November ballot there.

Protest Zone Clash:  The Republican National Committee, alarmed by what it sees as a significantly worsening security threat, asked that the director of the Secret Service intervene  and move a designated protest zone farther away at an upcoming convention.

A.I.’s Role:  The era of A.I. has officially arrived on the campaign trail. But so far, the political uses of the much-anticipated, and feared, technology are more theoretical than transformational .

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Narrative About Video Games

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Published: Mar 20, 2024

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