<aside> ⬆️ back to The Ecosystem of “Social Agriculture” (FINAL)


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Social agriculture depends upon the logic of social media platforms, which largely depend on ad-driven business models. The full extent of algorithms and features that power these social media platforms exceed the scope of this report, but a few examples have clear bearing on social agriculture.

Temporal messaging queues reflect an intuitive model of everyday communication

"Temporal messaging queues" are the most basic example of an influential algorithm. This feature underpins some of the major platforms used for social agriculture, such as WhatsApp and Telegram.



The technical details of how these queues are implemented are not relevant to understanding their impact. The most important aspect of these queues is that messages are brought to a viewer's attention based on the time they were sent.

A diagram of how basic messaging queues work.

A diagram of how basic messaging queues work.

People often seek this predictable design in social agriculture. The temporal messaging queue reflects an intuitive model of everyday communication, i.e., the message at the top of the stack of messages which grabs a user's attention is simply the last thing sent. These models are intuitive, predictable, and easy for users to understand as everyday communication utilities. Tools that use this messaging paradigm have become critical for everyday agricultural practices, like sending instructions to a farm manager or taking orders from customers. This model serves as an important contrast to the very different model of “algorithmic news feeds.”

Algorithmic news feeds fundamentally distort information-sharing practices in favor of attention

Most social media platforms employ some form of news feed feature. This acts as a personalized front page for every user, where information presented at the top of the feed to each person is carefully orchestrated by algorithms. Platforms like Facebook and Douyin use this model.



News feeds are critical to these platforms' advertising business models. The way these feeds work is opaque, as compared to the simple messaging queue model outlined above. These systems required complex software architectures from their earliest incarnations. (Facebook first released the news feed in 2006). The video below explains how Facebook’s current news feed is powered by machine learning to score posts based on "signals" (such as user likes). The video claims this work enables Facebook to personalize the news feed for every user with “the content that matters most to them.”

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[Source: Facebook](https://www.facebook.com/plugins/video.php?href=https%3A%2F%2Fwww.facebook.com%2FEngineering%2Fvideos%2F264352435037706%2F&show_text=0&width=560)

Source: Facebook

There are reasons to doubt that Facebook’s algorithm straightforwardly personalizes our news feed to show us posts that matter most to us. The company’s business objective is better described as maximizing "time on device," a term taken from the gambling industry to measure the performance of slot machines. In fact, news feed algorithm design has been inspired by concepts from slot machine design, such as irregular or intermittent reward schedules to keep users hooked. These algorithms maximize attention, not content value, from a user perspective.

Intermittent reward mechanisms generate more "time spent playing", play with a great interactive demo to learn more

Intermittent reward mechanisms generate more "time spent playing", play with a great interactive demo to learn more

Time on device is misaligned with the requirements of better information exchange. If such news feed algorithms are designed to maximize time on device, this is problematic for social agriculture. As discussed in Examining large-scale groups in social agriculture in Kenya, information sharing in large agricultural groups accounted for a significant share of posts. Moreover, when asked about The experience of social agriculture from users in Kenya, agricultural workers reported that "getting farming tips" was their main activity, suggesting that information exchange observed in groups was "what matters most" to users.