Understanding the Attention Economy: How Social Media Algorithms Shape Information Consumption

In the fast-paced digital age, social media companies have firmly established themselves as profit-driven entities, relentlessly pursuing financial gains.

The underlying principle governing social media companies' strategies lies in the concept of attention economics, as defined by Thomas H. Davenport and John C. Beck, a paradigm that treats human attention as a scarce commodity and applies economic theory to solve information management problems. In simpler terms, attention is a finite resource - each person has only so much of it.

Given that attention is the selective concentration of limited human cognitive resources on a given information, to the exclusion of other perceivable information, profit-oriented social media companies are incentivized to develop algorithms that aim to maximize the time consumers spend within their closed ecosystems.

These algorithms often prioritize controversial contents which are strategically engineered to incite contentious debates and promote polarization among users.

This calculated approach serves to attract a larger audience and generate heightened levels of interaction, leading to increased Web traffic — a compelling proposition for advertisers.

By promoting content that sparks intense reactions and emotions, social media companies create an atmosphere conducive to prolonged user engagement, which ultimately results in increased Web traffic. This alluring prospect makes social media platforms particularly appealing to advertisers seeking optimal exposure for their products and services.

In addition to maximizing user engagement, social media companies such as Facebook, Twitter, TikTok, Instagram, WhatsApp, WeChat, and Slack continually nudge us towards consuming fragmented information , encompassing various themes and sentiments.

The interactions of social media platform users with these diverse data fragments are subsequently analyzed and processed by digital products to display micro-targeted embedded marketing messages, utilizing intricate big data algorithms beyond our full comprehension. These practices significantly impact and shape our perspectives and behaviors, compelling us to perpetually react to simulated digital events orchestrated by machines to reinforce our beliefs or manifest our feelings of disgust

In truth, machine algorithms manipulate everything behind the scenes, while misleading digital information consumers into believing they are in control of public opinion and the information flow on electronic social media.

注意力经济：社交媒体算法如何形塑资讯受众所见、所想

面子书、推特、抖音、Instagram、WhatsApp、微信和Slack等不断的助推（nudge）我们吸收、分享和转发碎片化资讯（fragmented information）的资讯消费（information consumption）生态。这些零散的个体数据过后再被大型平台以我们无法完全窥探和了解的大数据算法，转换成高度针对性（micro-targeting）的智能商品和置入性行销，影响和形塑我们的看法和行为，让我们不间断的回应由机器“导演”的拟真数码化事态，以及其周边现象（reactively respond to the machine orchestrated events and the associated symptoms）。事实是，机器算法在背后操控一切，却让数码资讯受众群误以为，我们正在主导电子社交媒体的舆论和信息流（machine algorithms leading from behind but make you feel that you are leading in the front）。