Unravelling the gender politics of young men [Pt 2]
This is the second of three pieces on gender, politics, and young people. Part 1 is here.
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There is some unfinished business from the previous piece on the gendered of young people. ‘Young’ is 18-29.
So let’s start, briefly, with the politics of young women, radicalised in the last decade (in the UK, Germany, and the US), and the last half decade in South Korea.
In his FT piece…
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The Algorithm's Effect on Users
How Does The Algorithm Engage with Users?
In the Gendered Lives reading “Media Representations and the Creation of Knowledge,” Kirk and Okazawa-Rey write briefly about the role of social media in news circulation and how it works. They state, “As a condition of using such sites, these companies have unprecedented access to our personal information to direct specific advertising our way. Moreover, online business models that seek to maximize ‘clicks’ to earn advertising revenue encourage posting information that is ‘click-worthy’ regardless of whether it is accurate” (Kirk and Okazawa-Rey, 2020). In other words, a common method used by advertisers and social media users to gain more interaction with their content is to appeal to the need for belief affirmation and curiosity.
Algorithms used by TikTok and other social media platforms filter the content presented to each user so that they are not presented with views or news that challenge their personal beliefs, essentially submerging them into an online sphere of constant positive feedback for every opinion or experience they have. This presents a degrading effect on media literacy. If one is presented with a post that agrees with their values or beliefs, they are less likely to question it. If you like pineapple on pizza, and you see someone else on TikTok who claims that pineapple is the best possible topping for pizza, then you aren’t going to question that post because you already believe it to be true.
This effect is helped by the fact that people often get their news from accounts that post about the news, and these accounts are more prone to posting with biases since they do not operate the same way as newspapers or newscasters. The same level of fact-checking or extensive research simply doesn’t exist with a platform where it’s so easy to post and repost, which leads to the rapid spread of misinformation and biases. TikTok is also somewhat infamous for its young user base, consisting largely of teens and adults under 30, even children younger than their teens if given access to a device. Young and impressionable minds will be the most affected by biases they take in from TikTok because they don’t have the maturity, critical thought, or media literacy to see that the content they are fed might be subverting their thinking. Instead of learning how to think about real-world events and other people from their education and community, they’re learning through an algorithm. So, when presented with information or ideas that may be false or negatively reflected on a group of people, users will absorb it without question. That absorption may come in reposting, liking, or scrolling through and ignoring the post.
How Does The Algorithm Warp Users’ Sense of Self & Others?
A small social science review paper about how the algorithm of TikTok functions in shaping users’ identities discusses in more detail how people find themselves trapped in a cycle of repeatedly feeding about, by, and for the same groups. The authors write, “Due to the opaqueness or “black box-ed” nature of algorithms, users experience them just through their perceptions. Because what people see on social media is largely personalized, it shapes how people see themselves and others (Bhandari and Bimo 2022) but also impacts their behavior on social media platforms (DeVito 2021)” (Ionescu & Licu, 2023). This means that not thinking critically about the information they are seeing means that users are taking in opinions without considering alternate perspectives. Learning hatred without empathy means these users, in their filter bubbles, will continue to perpetuate hatred.
In their research, Ionescu and Licu also talk about how these “filter bubbles” create what’s called a crystal framework, where “The main characteristics of the “crystal” were: reflective (parts of their self-concept were reflected back to them in the feed); multifaceted; has a refinement strategy; is diffractive” (Ionescu & Licu, 2023). When placed into filter bubbles, users are put into the net of fellow users who share the ideologies and opinions of others, meaning that they are constantly surrounded by others who think, act, and look the same as them. This can snowball into a sort of ‘us vs.. them’ mentality on social media platforms, as users engage with others of a like mind (a like mind which the platform’s algorithm has also curated), they will gradually take in opinions and potential hatred of other groups who hold differing opinions, without taking the chance to think critically about what they are seeing of those groups.
When young users are put into one of these filter bubbles where all they see is a continuous stream of derogatory and discriminatory content, it normalizes the attitudes and language of misogyny. It generates a misogynistic worldview that the mind takes on.
How Do We See “Filter Bubbles” and Internalized Hatred Manifesting?
How this influx of filtered content shapes how users see themselves in relation to others can manifest in the form of online harassment. Misogynists and others with similar beliefs, when presented with content contending a more feminist perspective, will lash out because they may feel that their sense of identity and the way they see the world is being attacked. In a research survey conducted by the Pew Research Center on online harassment, it was found that the overall amount of online harassment has increased since 2017, stating, “Beyond politics, more also cite their gender or their racial and ethnic background as reasons why they believe they were harassed online” (Vogels, 2021).
As shown in the data, the cases of online harassment have shown a notable increase since 2017. We cannot say for certain, but it may not be unwise to rule out TikTok’s notoriously addictive algorithm as playing a part in this increase, as the platform was released in September 2016. It is also worth noting that the amount of harassment faced by users varies depending on their race, with Black and Hispanic users saying they often find themselves harassed due to their race (Vogels, 2021). This indicates that the backlash faced by women on TikTok is not only gendered but also racial. The research article also states that “50% of lesbian, gay or bisexual adults who have been harassed online say they think it occurred because of their sexual orientation” (Vogels, 2021). The takeaway here is that these trends and the way they generalize women result in more persecution targeting minority groups of women.
We find that through the perpetuation of misogyny, racism, homophobia, and transphobia in these filter bubbles, online spaces are made more dangerous to individuals belonging to minority groups.
How Does This Relate to a Watered-Down and Dehumanizing Image of Women?
Trends such as the girl math versus boy math and Roman empire trends are made general to garner as large of an audience as possible. While there may be a target audience for these trends, they will inevitably gain the attention of users who may not relate to the experiences or find them distasteful. This means that trends meant to be somewhat silly and uniquely catered to women with specific experiences may suddenly find themselves being co-opted by people with deep internalized prejudices to make it into something hateful or degrading. We can watch these trends devolve into another way to simply accuse women of being too unintelligent, too childish, too one-minded, fit only for sex & reproduction & motherhood, and simply not human enough to garner the respect of men.
Trends that gain a lot of attention on TikTok are used as a tool by and for misogynists to reaffirm what they already believe, that “modern” women are impudent and dismissive. Algorithms ensnare people into hateful and sexist communities that teach them how to twist every aspect of a woman to fit their mold of what a woman should be. They then go out into other parts of social media to take trends and content posted by women and either harass them for it or take the trend and turn it into a narrative demeaning women.
SOURCES
Ionescu, Claudiu Gabriel, and Monica Licu. "Are TikTok Algorithms Influencing Users’ Self-Perceived Identities and Personal Values? A Mini Review" Social Sciences 12, no. 8: 465. May 30, 2023. https://doi.org/10.3390/socsci12080465
Kirk, Gwyn & Margo Okazawa-Rey. “Gendered Lives: International Perspectives.” New York: Oxford University Press, 2020.
Vogels, Emily A. “The State of Online Harassment.” Pew Research Center: Internet, Science & Tech, January 13, 2021.
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