Citation

Your Fave Could Never: The Political Economy of Algorithmic Popularity

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Abstract:

In many online communities, an individuals’ social status is based on an ability to attract attention (Marwick 2013). Attention is quantified and made visible through metrics, such as “likes,” comments, or number of views (Baym 2013). The power of these metrics lies not only in their function as social capital, but in their conversion to economic capital. This may happen though revenue-sharing schemes like YouTube’s partner program (which, one YouTuber claims, pays $1,500-2000 for each million hits a video receives [Fast Company Studios 2014]); through talent companies like theAudience, which hires highly-followed young people to promote products and services (Brodesser-akner 2014); or through advertising sales or sponsored content.

However, metrics are deeply influenced by algorithms created by social media platforms. For instance, Twitter’s “recommended user” algorithm suggests accounts for new Twitter users to follow based on a complex formula that identifies “users across a variety of clusters who tweet actively and are engaged with their audiences” (Elman 2010), funneling hundreds of thousands of new followers as a result. The number of Twitter followers is extremely important in a variety of fields, as it serves as a metric for influence. Highly-followed users have access to sponsorship opportunities, and they may reap other rewards. Actors’ online fan bases factor into casting decisions, while journalists negotiate higher salaries based on their Twitter followers (Sternberg 2013).

This paper investigates the relationship between reputation metrics, algorithms, and the economic and political benefits of micro-celebrity, an online self-presentation strategy in which users view their audience as a ‘fan base’ and reach out to them accordingly (Marwick 2013). Algorithmically-generated metrics such as Spotify’s “Most Viral,” Instagram’s “Explore” tab, Reddit’s “Best” and YouTube’s “Most Popular” add not only to the reputations and influence of users but, often, to the economic rewards these users receive. In a social media ecosystem that has been criticized for the economic disparities between platform owners and users, it is worth scrutinizing the “automatic, proprietary, and opaque” algorithms that further such inequality.
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Association:
Name: International Communication Association 65th Annual Conference
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http://www.icahdq.org


Citation:
URL: http://citation.allacademic.com/meta/p983655_index.html
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MLA Citation:

Marwick, Alice. "Your Fave Could Never: The Political Economy of Algorithmic Popularity" Paper presented at the annual meeting of the International Communication Association 65th Annual Conference, Caribe Hilton, San Juan, Puerto Rico, <Not Available>. 2015-12-02 <http://citation.allacademic.com/meta/p983655_index.html>

APA Citation:

Marwick, A. "Your Fave Could Never: The Political Economy of Algorithmic Popularity" Paper presented at the annual meeting of the International Communication Association 65th Annual Conference, Caribe Hilton, San Juan, Puerto Rico <Not Available>. 2015-12-02 from http://citation.allacademic.com/meta/p983655_index.html

Publication Type: Session Paper
Abstract: In many online communities, an individuals’ social status is based on an ability to attract attention (Marwick 2013). Attention is quantified and made visible through metrics, such as “likes,” comments, or number of views (Baym 2013). The power of these metrics lies not only in their function as social capital, but in their conversion to economic capital. This may happen though revenue-sharing schemes like YouTube’s partner program (which, one YouTuber claims, pays $1,500-2000 for each million hits a video receives [Fast Company Studios 2014]); through talent companies like theAudience, which hires highly-followed young people to promote products and services (Brodesser-akner 2014); or through advertising sales or sponsored content.

However, metrics are deeply influenced by algorithms created by social media platforms. For instance, Twitter’s “recommended user” algorithm suggests accounts for new Twitter users to follow based on a complex formula that identifies “users across a variety of clusters who tweet actively and are engaged with their audiences” (Elman 2010), funneling hundreds of thousands of new followers as a result. The number of Twitter followers is extremely important in a variety of fields, as it serves as a metric for influence. Highly-followed users have access to sponsorship opportunities, and they may reap other rewards. Actors’ online fan bases factor into casting decisions, while journalists negotiate higher salaries based on their Twitter followers (Sternberg 2013).

This paper investigates the relationship between reputation metrics, algorithms, and the economic and political benefits of micro-celebrity, an online self-presentation strategy in which users view their audience as a ‘fan base’ and reach out to them accordingly (Marwick 2013). Algorithmically-generated metrics such as Spotify’s “Most Viral,” Instagram’s “Explore” tab, Reddit’s “Best” and YouTube’s “Most Popular” add not only to the reputations and influence of users but, often, to the economic rewards these users receive. In a social media ecosystem that has been criticized for the economic disparities between platform owners and users, it is worth scrutinizing the “automatic, proprietary, and opaque” algorithms that further such inequality.


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