To what extent has a lack of diversity influenced the development of the internet? How does this lack of diversity harm societies and individuals?

Figure 1. “Future of the Internet” by Free Press/ Free Press Action Fund is licensed under CC BY-NC 2.0.


Just as it has been a guiding policy principle in the world of traditional media, diversity has become one of the fundamental elements of Internet governance. These concepts assist in creating the political and legal contexts that shape cultural law and policies throughout the development of the Internet and the changes in the digital networked environment (Burri, 2016). Therefore, to fully understand the effects of diversity in the digital age, it is also crucial for researchers to understand how these factors influence both individual Internet users and entire societies. In the context of the Internet’s evolution from Web 2.0 platforms to the emergence of artificial intelligence in Web 3.0, this article will analyse gender discrimination as a component of the lack of diversity and how it negatively impacts society. Then it will analyse these processes using real-world examples and theories from media academics, as well as discuss possible consequences of a lack of diversity in networked society.

Background and context 

First and foremost, it is crucial to understand how the development of the Internet influences social conventions and individual perceptions; as a result, a lack of demographic diversity in this development could have several negative outcomes. From a cultural perspective, the Internet is as much a collection of communities as it is a collection of technology, and its success is mainly attributable to both satisfying community needs and utilising the community to make developments. As the technological systems are socially produced and social production is culturally informed, the Internet is no exception (Castells, 2002). The Internet provides people with a platform to express their identity, connect with the community, as well as interact and collaborate with other individuals without any geographic constraint. As a result of this close relationship between the network and its users, any inequality in the network can have far-reaching effects on the public infrastructure. In the early days of the Internet, some academics hypothesised that the development of virtual environments would result in a freedom from gender norms and the experience of cultural diversity (Daniels, 2012). However, this is not true: the Internet did not escape its commitment to diversity, and its lack of diversity in many aspects has had serious effects on political and economic movements in contemporary communities, given its connection to the public. These points will be explored further in the next paragraphs, along with an evaluation of the Internet’s two stages of development: Web 2.0 and Web 3.0.

Web 2.0 and gender discrimination on media platforms 

The effects of gender discrimination have always been a “complex and on-going field of research” (Matamoros-Fernández, 2017, p. 935). This absence of diversity in Web 2.0 development and social media platforms will be examined in this section. Darcy DiNucci first used the term “Web 2.0” to describe the new ways people would utilise the internet at the beginning of the twenty-first century (1999). User-generated content, participatory culture, interaction, and collaboration are just a few of the new public engagement methods that Web 2.0 provided to its users. As a result, communities that have historically been marginalised in the public domain, such as gender and racial minorities, have been disproportionately affected by new types of gatekeeping in public spaces (Vorsino, 2021). This disparity exists because historically, public spaces have been perceived as masculine and unwelcoming to female participation, and this ideology remains when applied to the architecture of the internet as the new public sphere. In particular, internet use and culture are perceived as being dominated by men, both in terms of general user engagement and the tech industry where the majority of engineers, programmers, and other professionals are men. The gender gap in political engagement first came to the public’s notice in the 1970s thanks to feminist theory and research (Vochocová, 2018). Soon after political actors began to use social media platforms as a tool for political communication in the Web 2.0 era, these inequalities were noticed and studied by contemporary media researchers. 

Figure 2. “Internet users” by Multnomah County Library is licensed under CC BY-NC 2.0.

According to a study of Facebook users, only 27% of women participated in political conversation on Facebook, compared to 70% of men (Vochocová , 2018). This can be explained by a Forbes report stating that men had 35% more active accounts than women do, when compared on a relative basis (2018). According to another study, only 36% of political candidates with Facebook pages at the time were women, with 64% of them being men (Tsichla et al., 2021). Additionally, Facebook’s lack of gender diversity also extends to its mistreatment of individuals who identify as non-binary. When creating a new account, users had to specify whether they were male or female, or the site would reject their submission. Even after Facebook updated in 2014 to allow users to choose a third option (“custom”) that, if selected, provided 56 additional options, users could still only wish their friends a happy birthday by using the pronouns him, her, or they. This draws attention to the lack of gender diversity in contributions on Facebook as a typical example of media platforms in Web 2.0. This has a number of negative effects, such as a male-dominated public sphere, online harassment, or even coordinated attacks from a particularly more dominant gender (Vorsino, 2021). Overall, it was clear from the investigation into the gender diversity gap in Web 2.0, using Facebook as a representative platform, that the deficiency has a number of consequences for both the advancement of the Internet and the general public. 

Web 3.0, artificial intelligence and gender bias in employment 

Figure 3. “Future Insights: Artificial Intelligence” by Ars Electronica is licensed under CC BY-NC 2.0.

The goal of the Web 3.0, which is an evolution of Web 2.0, is to reduce the amount of work and decision-making done by humans by handing those tasks over to computers (Vieira & Isaas, 2016, p. 214). Web 3.0, in contrast to Web 2.0, relies on user cooperation and allows machines to undertake some of the thinking that was previously only performed by humans (Ghelani & Hua, 2022). This current Internet development, however, cannot escape the lack of cultural and sexual diversity, as well as the consequences in the general population.

Artificial intelligence (AI), the most remarkable development from Web 3.0, was developed with the objective of assisting users with different tasks, and in particular aiming to simplify them (Ghelani & Hua, 2022). Despite the fact that the innovation has had a significant positive impact on society, there is substantial evidence that artificial intelligence is not the unbiased technology that has been claimed by many researchers. For instance, IBM explained that the absence of diverse data to train systems on is what mostly contributes to the gender bias in their AI-led biometrical technology (Biometric Technology Today, 2018). Accordingly, a 2017 study found that IBM’s success rate for applying AI to scan data was just 87% when compared to Microsoft, which has a success rate of 97%. (Lunter, 2020). The use of AI and its deep learning algorithms also promises to help businesses with profile scanning and other employment-related decisions as human resources deals with modern concerns like the ageing workforce and universal basic income (Tétrault, 2022). Therefore, the tool’s lack of gender diversity, like in IBM’s example, could have negative effects on the workforce. Due to the long-standing issue of gender diversity in the IT industry, prejudice in employment-related judgments made by AI may result in greater gaps and barriers to gender equality and will also make it more difficult for women to find jobs in the STEM fields (Ly-Le, 2022). Women will also be offered positions that require fewer technical skills as gender bias filters into AI algorithms, with men being pitched lucrative careers in machine learning and women being considered for roles in data analytics (Domini et al., 2022). To sum up, while AI is useful in helping businesses make employment-related decisions, it can also pose a number of recruitment issues in the workforce and the general public due of the lack of gender diversity in the data input.


In conclusion, a lack of diversity can contribute to a number of issues with the growth of the Internet, such as gender inequity in Web 2.0 platforms and biased artificial intelligence in Web 3.0. Additionally, because they promote online abuse, unequal employment opportunities, and single gender dominance in networked conversation, these problems have the potential to harm both society and individuals. As the most pressing question for the network’s future is not how technology will change, but how the process of change and evolution itself will be managed (Leiner et al., 2009), it is critical for media researchers and stakeholders to come up with solutions to these emerging problems in the Internet’s development. Platforms can improve by incorporating more diverse customer data in their sign-up processes to reduce gender prejudice, and businesses should also be more cautious when indicating multi-gender training data sets. However, the success of these solutions depends on individuals, especially Internet users and network providers, being more considerate when interacting with a diverse and multicultural public when utilising network tools and the Internet.





Angelovska, N. (2018, December 20). Men Dominate Facebook With 35% More Active Accounts Than Women. Forbes.


Burri, M. (2016). Cultural diversity in the internet age: in search of new tools that work. DigiWorld Economic Journal Communications & Strategies, 101, 63—.


Castells, M. (2002). The Culture of the Internet. In The Internet galaxy reflections on the Internet, business, and society (pp. 36—63). Oxford University Press.


Daniels, J. (2012). Race and racism in Internet Studies: A review and critique. New Media & Society, 15(5), 695—719.


DiNucci, D. (1999). Fragmented Future. Print, 53(4).


Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2022). For whom the bell tolls: The firm-level effects of automation on wage and gender inequality. Research Policy, 51(7), 104533—.


Ghelani, D., & Hua, T. K. (2022). Conceptual Framework of Web 3.0 and Impact on Marketing, Artificial Intelligence, and Blockchain. International Journal of Information and Communication Sciences, 7(1), 10—17. 10.11648/j.ijics.20220701.12


IBM and Microsoft tackle FRT race and gender bias. (2018). Biometric Technology Today, 2018(7).


Leiner, B. M., Cerf, V. G., Clark, D. D., Kahn, R. E., Kleinrock, L., Lynch, D. C., Postel, J., Roberts, L. G., & Wolff, S. (2009). A brief history of the internet. Computer Communication Review, 39(5), 22—31.


Lunter, J. (2020). Beating the bias in facial recognition technology. Biometric Technology Today, 2020(9), 5—7.


Ly-Le, T. M. (2022). Hiring for gender diversity in tech. The Journal of Management Development, 41(6), 393—403.


Matamoros-Fernández, A. (2017). Platformed racism: the mediation and circulation of an Australian race-based controversy on Twitter, Facebook and YouTube. Information, Communication & Society, 20(6), 930–946.


Tsichla, E., Lappas, G., Triantafillidou, A., & Kleftodimos, A. (2021). Gender differences in politicians’ Facebook campaigns: Campaign practices, campaign issues and voter engagement. New Media & Society, 146144482110364—.


Tétrault, M. (2022). Considerations and Risks of AI in Employment Decisions. In McCarthy Tétrault Blog: Employer Advisor. Newstex.


Vieira, J., & Isaías, P. (2016). Web 3.0 in Web Development. In Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications (pp. 209—228). IGI Global.


Vochocová, L. (2018). Witty divas, nice mothers and tough girls in a sexist world: experiences and strategies of female influencers in online political debates. Media, Culture & Society, 40(4), 535—550.


Vorsino, Z. (2021). Chatbots, Gender, and Race on Web 2.0 Platforms: Tay.AI as Monstrous Femininity and Abject Whiteness. Signs: Journal of Women in Culture and Society, 47(1), 105—127.



Hypertext references


Ars Electronica. (2021, March 9). Future Insights: Artificial Intelligence, [Photograph].


Defining Web 3.0: opportunities and challenges. Electronic Library, 34(1), 132–154.


DeRosa, S. (2014). ‘Like’ new Facebook gender options. University Wire.


Free Press/ Free Press Action Fund. (2012, January 5). Future of the Internet, [Photograph].


Gillespie, T. (2015). Platforms Intervene. Social Media + Society, 1(1), 205630511558047–.


Habermas, J., Burger, T., & Lawrence, F. (1992). The structural transformation of the public sphere: an inquiry into a category of bourgeois society. Polity Press.


Han, S. (2011). Web 2.0. Routledge.


Heiss, R., Schmuck, D., & Matthes, J. (2019). What drives interaction in political actors’ Facebook posts? Profile and content predictors of user engagement and political actors’ reactions. Information, Communication & Society, 22(10), 1497–1513.


Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar, P. N. (2020). Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Current Reviews in Musculoskeletal Medicine, 13(1), 69–76.


Multnomah County Library.  (2011, March 16). Internet users, [Photograph].


VanHeuvelen, T., & Quadlin, N. (2021). Gender Inequality in STEM Employment and Earnings at Career Entry: Evidence from Millennial Birth Cohorts. Socius: Sociological Research for a Dynamic World, 7, 237802312110643–.