You Are What You Watch: How Our Media Consumption Shapes Our Identity in a World of Algorithms

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We’ve all heard the saying ‘you are what you eat’, but have you ever considered that the same might be true for the media we consume? From news articles to social media feeds, the content we consume daily can shape our thoughts, beliefs, and even our identities. So, just as we carefully choose our meals to nourish our bodies, perhaps it’s time we start being more intentional about what we feed our minds.

Media plays a significant role in shaping the values and beliefs of its audience, according to many media effect theories. However, the active audience theory rejects this notion, arguing that audiences are not passive recipients of media messages. Rather, they are actively engaged in interpreting and constructing meaning from media content.

The advancements in technology have provided us with numerous media choices to select from, allowing us to exercise our agency in deciding what values and beliefs we want to consume. The responsibility of selecting and being mindful of the media we consume falls on us, the audience.

Although audiences are adapting and gaining more freedom of choice in their media consumption, it’s important to consider whether the media and the powerful forces behind it are also adapting to maintain control over what audiences consume. In the end, the question remains: are we defined by what we choose to watch, or are we limited by what is offered to us?

The Power of Media: Portrayal of Identity

It is indeed undeniable that the media has the power to influence its audience. This power can extend to how we perceive ourselves and our place in the world, as well as the products and services we choose to consume. This power comes from limited access and provided channels, media have great power in monopolizing what the audience watches. A lot of media effect theory has explored the potential impact of media consumption on individuals and their identities.  For example, cultivation theory posits that the repetition of certain images and messages in media can shape viewers’ perceptions and beliefs about what is normal or acceptable. According to the study of media and mass communication, it is reasonable to assume that media can have significant effects on people’s feelings, opinions, attitudes, and behaviors (McQuail & Deuze, 2020).

However, it’s still debatable whether the influence of media on identity is direct or indirect, even if the effect model itself is wrong (Gauntlett, 2003). It’s worth questioning whether the media still holds the same level of power if audiences have an active say in their choices. Some argue that the active audience theory provides a more accurate representation of how individuals interact with media. According to this theory, media content is only part of the equation; audiences bring their own experiences and worldviews to the process of interpreting and constructing meaning. With the rise of digital media and numerous platforms, people’s preferences and behaviors are also playing a significant role in shaping their media consumption choices. Differences in gender, class level, academic discipline, and personality traits were found using different platforms and also in the purpose of use (Kim et al., 2014). From that point, media consumption can portray the identity of the audience. different from media is shaping audience identity, indeed, media consumption can portray the identity of the audience. Therefore, it can be argued that technological advancement has transformed the power dynamic between media and its audiences, allowing audiences to actively participate in defining and portraying their own identities through media consumption choices.

Whether media still holds the same level of power or audiences have an active say in their choices, it can’t be ignored that media hold significant means in reflecting audience identity and shaping social norms. Especially technological advancements in digital media have significantly impacted the relationship shift between media and their audience. This relationship extends beyond media consumption, as algorithms are used to suggest content based on audiences’ preferences. Media consumption, therefore, can be seen as a two-way process where media has the power to shape individuals and their perceptions while individual choices and preferences also can portray and influence their media consumption.

Is It Really Freedom of Choice?

Despite the increased variety of media consumption choices, it is important to question whether this freedom of choice is genuine or merely an illusion. As various digital media already implement various complex algorithms to personalize content, individuals may feel like they have free choice but are actually being guided towards particular options. At the very basic level, algorithms offer consumers information relevant to their choices. In reality, these algorithms are designed to guide individuals toward particular options based on their browsing history, search queries, and other personal data (Dou et al., 2007; Qiu & Cho, 2006; Sugiyama et al., 2004). These algorithms are often designed with biases and preferences of media companies, advertisers or the government, which may potentially present skewed, even discriminating content to individuals (Diakopoulos, 2014; Eslami et al., 2017; Kleinberg et al., 2018). Other than biases, algorithms also have their reality construction that tends to increase individualization, commercialization, inequalities, and deterritorialization and decrease transparency, controllability, and predictability (Just & Latzer, 2017).

These factors suggest that while audiences may have more choices than ever before, their true freedom of choice is limited and often shaped by external factors beyond their control, bias, and algorithmic design. This limited freedom of choice can have significant consequences for individuals, particularly in terms of their identity formation. The content that individuals consume plays a vital role in shaping their perceptions, beliefs, values, and ultimately their identity (Shim et al., 2011). With the vast amount of media available, it can be challenging to curate a well-rounded media diet that exposes individuals to a diverse range of perspectives and experiences. Furthermore, the biases inherent in media production and distribution can perpetuate stereotypes, reinforce harmful narratives, and contribute to the marginalization of certain groups (Turner Lee, 2018). For instance, racial bias in an algorithm that guides health decisions (Obermeyer & Mullainathan, 2019), and labor discrimination (Bertrand & Mullainathan, 2004).

The Impact of Limited Freedom of Choice on Identity Formation

The consequences of limited freedom of choice go beyond just identity formation and extend to our decision-making abilities. In today’s world, we are bombarded with an overwhelming amount of information and choices, which can lead to decision fatigue and mental exhaustion. As a result, individuals may be tempted to limit their choices and opt for the easiest, most convenient option available, which may not be the best choice for their overall well-being. The limitations on our freedom of choice in media consumption and other areas of our lives can have far-reaching consequences for our decision-making abilities and overall well-being. It’s crucial to be aware of these limitations and to actively seek out diverse perspectives and experiences to ensure that we’re not falling into the trap of limiting our choices and doing less thinking.

Algorithms are surely helpful in streamlining our media consumption and decision-making processes, but it’s important to acknowledge their limitations and potential biases. As mentioned before, the content that individuals consume plays a vital role in shaping their identity (Shim et al., 2011), and limitations on their freedom of choice can have a significant impact on this process. The algorithms and biases present in media consumption can limit individuals’ exposure to diverse perspectives and experiences, which in turn would hinder their identity exploration.

This highlights the importance of promoting media literacy and critical thinking skills, which can help individuals recognize biases and make informed decisions. Moreover, media literacy and critical thinking skills can enable individuals to identify the limitations in their freedom of choice and seek out alternative sources of information that challenge their existing beliefs and broaden their perspectives.

References

Diakopoulos, N. (2014). Algorithmic Accountability Reporting: On the Investigation of Black Boxes. https://doi.org/10.7916/D8ZK5TW2

Dou, Z., Song, R., & Wen, J.-R. (2007). A large-scale evaluation and analysis of personalized search strategies. Proceedings of the 16th International Conference on World Wide Web, 581–590. https://doi.org/10.1145/1242572.1242651

Eslami, M., Vaccaro, K., Karahalios, K., & Hamilton, K. (2017). “Be Careful; Things Can Be Worse than They Appear”: Understanding Biased Algorithms and Users’ Behavior Around Them in Rating Platforms. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 62–71. https://doi.org/10.1609/icwsm.v11i1.14898

Gauntlett, D. (2003). The Worrying Influence of ‘Media Effects’ Studies. In M. Barker & J. Petley, Ill effects the media/violence debate (Second edition). Routledge.

Just, N., & Latzer, M. (2017). Governance by algorithms: Reality construction by algorithmic selection on the Internet. Media, Culture & Society, 39(2), 238–258. https://doi.org/10.1177/0163443716643157

Kim, K.-S., Sin, S.-C. J., & Tsai, T.-I. (2014). Individual Differences in Social Media Use for Information Seeking. The Journal of Academic Librarianship, 40(2), 171–178. https://doi.org/10.1016/j.acalib.2014.03.001

Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2018). Discrimination in the Age of Algorithms. Journal of Legal Analysis, 10, 113–174. https://doi.org/10.1093/jla/laz001

McQuail, D., & Deuze, M. (2020). Mcquail’s media and mass communication theory (7th ed). SAGE Publications.

Obermeyer, Z., & Mullainathan, S. (2019). Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People. Proceedings of the Conference on Fairness, Accountability, and Transparency, 89–89. https://doi.org/10.1145/3287560.3287593

Qiu, F., & Cho, J. (2006). Automatic identification of user interest for personalized search. Proceedings of the 15th International Conference on World Wide Web, 727–736. https://doi.org/10.1145/1135777.1135883

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Turner Lee, N. (2018). Detecting racial bias in algorithms and machine learning. Journal of Information, Communication and Ethics in Society, 16(3), 252–260. https://doi.org/10.1108/JICES-06-2018-0056

 

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