Decoding the Gender Divide: No Wonder Algorithms Shape What We See and Believe?

Stuart Hall proposed the notice of Encoding/Decoding in 1973, stating that media makers use their precise language to encode information, but the audience will interpret (or decode) it according to their own ideas. This decoding process can take one of three forms: Dominant reading, where the audience fully accepts the meaning expected by the producer; Negotiated reading, where the audience agrees with certain viewpoints of the information, but also has their own interpretation of other viewpoints; Finally, there is Oppositional reading, in which the audience completely rejects the expected information and creates their own meaning for the text.

Translating Media Messages–Stuart Hall's Encoding/Decoding | dino sossi

The big data inference algorithms on online platforms, especially in today’s era, are all done very well on major mainstream platforms. Content creators (such as social media companies or advertisers) may encode users’ information based on their gender, interests, and behavior. For example, female users may be coded as liking fashion, beauty, and parenting related content. But male users may be pushed more content about technology, cars, and sports. These pieces of information are pushed to different audiences through algorithms designed by the creator. For example, a study revealed that an ad promoting STEM careers reached more men than women, even when designed to be neutral (Lambrecht & Tucker, 2019). Additionally, algorithms often reflect and perpetuate harmful stereotypes about women, leading to biased product recommendations and limiting their choices (Mishra et al., 2019).

According to Hall’s thesis, audiences (whether male or female) “decode” these signals depending on their own origins, interests, and culture. Women, for example, may readily accept beauty-related advertisements or films if they align with their expectations and interests. This is known as a dominant interpretation, in which they accord with the intended meaning. However, some women may question or examine these advertisements critically. This is a negotiated interpretation, in which people partially agree with the message but modify it depending on their own experiences. On the other hand, if some women reject these advertisements, believing that they perpetuate gender stereotypes or objectify women, they will adopt an opposing interpretation in which they entirely disagree with the message.

And the same, males may decode information in various ways. For example, males watching sports or technology material may totally embrace the ideals given (dominant reading). However, if some men dispute these issues, believing they are too commercial or lack variety, their decoding may be a negotiated interpretation. If they entirely oppose these views, believing the information to be outdated or prejudiced, their decoding may be an oppositional reading.

dinosossi. “Translating Media Messages–Stuart Hall’s Encoding/Decoding.” Dino Sossi, 1 Apr. 2018, dinosossi.wordpress.com/2018/04/01/translating-media-messages-stuart-halls-encoding-decoding/.

Fosch-Villaronga, Eduard, et al. “Gendering Algorithms in Social Media.” ACM SIGKDD Explorations Newsletter, vol. 23, no. 1, 26 May 2021, pp. 24–31, kdd.org/exploration_files/4_specialsectionBIAS_5.pdf, https://doi.org/10.1145/3468507.3468512.

Kant, Tanya. “Identity, Advertising, and Algorithmic Targeting: Or How (Not) to Target Your “Ideal User.”” MIT Case Studies in Social and Ethical Responsibilities of Computing, 10 Aug. 2021, mit-serc.pubpub.org/pub/identity-advertising-and-algorithmic-targeting/release/2, https://doi.org/10.21428/2c646de5.929a7db6.

MAMBROL, NASRULLAH. “Dominant, Oppositional, and Negotiated.” Studylib.net, 7 Nov. 2020, studylib.net/doc/7262886/dominant–oppositional–and-negotiated.

Mambrol, Nasrullah. “Analysis of Stuart Hall’s Encoding/Decoding | Literary Theory and Criticism.” Literariness, 7 Nov. 2020, literariness.org/2020/11/07/analysis-of-stuart-halls-encoding-decoding/.

Media Studies. “Stuart Hall’s Reception Theory | Encoding and Decoding Messages.” Media Studies, 1 Oct. 2020, media-studies.com/reception-theory/.

Revision World. “Reception Theory – Media Studies – Revision World.” Revisionworld.com, 2019, revisionworld.com/a2-level-level-revision/media-studies-level-revision/reception-theory.

Schroeder, Jonathan E. “Reinscribing Gender: Social Media, Algorithms, Bias.” Journal of Marketing Management, vol. 37, no. 3-4, 14 Oct. 2020, pp. 1–3, https://doi.org/10.1080/0267257x.2020.1832378.

3 thoughts on “Decoding the Gender Divide: No Wonder Algorithms Shape What We See and Believe?

  1. The blog post does a great job of addressing a complex topic, how algorithms influence gender perceptions. You effectively use real life examples to illustrate how biased algorithms can reinforce stereotypes, making the discussion relatable. And the post is well structured, guiding the reader through key points. The use of statistics and research references strengthens the argument, adding credibility.
    A way to improve could be addressing solutions or suggestions for mitigating these biases.

  2. Hi there, I really enjoyed reading this blog! It explains the nuanced topic of gender representation in an easy way. To see that algorithms play such a big role in perpetuating gender stereotypes by recommending certain content, even though it is designed to be neutral. This goes to show that there is still a big issue with strict and dangerous gender roles. Especially because young children being recommended content about things they are ‘supposed’ to like, promotes these standards even more.

    Out of my own curiosity, I would have liked to see more examples of algorithms influencing gender-based content recommendation and examples of how it negatively effects people in real life!

  3. Hi,

    I really enjoyed reading this piece of work. It was insightful, informative and entertaining. There were lots of visual multimedia content which made the reading process seem easier. Your sources were nicely referenced in your writing. You have a great selection of references at the bottom. I learned a few things about encoding and decoding that I did not know before especially as I wrote about something different entirely in my own blog post on this topic.

    Fantastic work!

    Abdikarim

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