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Facebook’s Social Media Algorithms Study Arguments

Introduction

The article “The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms” discusses user experience with social network algorithms. The author examines user awareness of this domain and reviews the assumptions of people in terms of algorithmic computation. The purpose of this paper is to review the article and write a polemic against this scholarly text.

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Overview

Overall, the article by Bucher (2017) investigates the impressions of people regarding the algorithmic imaginary of social networks. The author has introduced this concept to encompass the core of algorithms, their functionality, and their intended purpose. The study exhibits the emotions of people when they can determine that particular offers, commercials, or other options have been generated by an algorithm.

Imaginary has a high social power since it affects people to a significant degree causing confusion, irritation, surprise, and other feelings. Some of the participants mentioned that they have started placing a particular emphasis on their activity in social networks since they were dissatisfied with what they could observe as a result of their behavior quantification (Bucher, 2017). According to the study, the imaginary is rather complex since algorithms produce disparate emotions in people.

Argumentation

One of the conclusions drawn by Bucher (2017) is the assumption that algorithms do affect the activity of people on the Internet. Further, the author states that this interaction is a two-way process, and people also influence algorithms through particular attention to their actions or entries. According to the researcher, “affective encounters between people and the Facebook algorithm are not just productive of different moods and sensations, but also play a generative role in molding the algorithm itself” (Bucher, 2017, p. 41).

It is reasonable to agree with the first part of this assumption while the latter one lacks justification (Kitchin, 2014). According to experts in the field, algorithm production occurs as a result of machine learning, which is an analytical process (Hallinan & Striphas, 2014). The refinement of algorithms happens when a certain amount of data has been accumulated. Therefore, the core of such computation lies in the digitally mediated surveillance rather than tendencies in user perceptions.

Also, it is quite difficult to state what factors determine human decision-making. Therefore, algorithms emerge as a result of the algorithmic computation that occurs based on a prediction rather than on the ability of users to generate their responses (Russell, 2013). Thus, algorithms are formed as a reaction to updated entries or actions, and they are not descriptive of human behavior (Siegel, 2016). They are ontogenetic, and the rebound of the system to changes in user behavior should be regarded as a mathematical response of the model.

Apart from that, the author uses the evidence received from 25 participants. It can be assumed that despite the qualitative character of the research, the chosen sample is not representative enough to draw credible and objective conclusions (O’Dwyer & Bernauer, 2013). The provision of statistically significant results could have reinforced the outcomes of the study.

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Conclusion

Thus, it can be concluded that the article throws light on the effects of Facebook algorithms on people. The author has stated that algorithm imaginary is a two-way process in which algorithms and users mutually influence each other. However, it can be argued that such an assumption might be flawed since algorithms are based on a model that has an ontogenetic character.

References

Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30-44.

Hallinan, B., & Striphas, T. (2014). Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, 18(1), 117-137.

Kitchin, R. (2014). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14-29.

O’Dwyer, L. M., & Bernauer, J. A. (2013). Quantitative research for the qualitative researcher. Thousand Oaks, CA: SAGE.

Russell, M. A. (2013). Mining the social web: Data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more (2nd ed.). Farnham, UK: O’Reilly Media.

Siegel, E. (2016). Predictive analytics: The power to predict who will click, buy, lie, or die (2nd ed.). Hoboken, NJ: John Wiley & Sons.

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StudyCorgi. (2020, December 23). Facebook's Social Media Algorithms Study Arguments. Retrieved from https://studycorgi.com/facebooks-social-media-algorithms-study-arguments/

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"Facebook's Social Media Algorithms Study Arguments." StudyCorgi, 23 Dec. 2020, studycorgi.com/facebooks-social-media-algorithms-study-arguments/.

1. StudyCorgi. "Facebook's Social Media Algorithms Study Arguments." December 23, 2020. https://studycorgi.com/facebooks-social-media-algorithms-study-arguments/.


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StudyCorgi. "Facebook's Social Media Algorithms Study Arguments." December 23, 2020. https://studycorgi.com/facebooks-social-media-algorithms-study-arguments/.

References

StudyCorgi. 2020. "Facebook's Social Media Algorithms Study Arguments." December 23, 2020. https://studycorgi.com/facebooks-social-media-algorithms-study-arguments/.

References

StudyCorgi. (2020) 'Facebook's Social Media Algorithms Study Arguments'. 23 December.

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