Building a Social Media Algorithms for Good

In the vast sea of information, news feeds and search results act as our anchors to orient ourselves. However, these lists are primarily driven by attention and advertising models. Google has found immense success in this business sector, while Amazon and Apple use it as a supplementary source of income. Meanwhile, Meta's news feeds aim to spark curiosity and support their revenue stream. Sociologist Elke Wagner argues that these lists are intentionally designed to cause frustration, with no clear logic behind their selection of content. They seem never-ending and obscure their criteria for choosing certain results over others without providing any explanation.

An intriguing experiment was conducted at Stanford University, aiming to construct purposeful social media feeds that incorporated societal value into the disseminated information. The compelling outcome: the refined algorithm resulted in diminished animosity among users.

The project required translating social science concepts about democratic values into algorithmic objectives; creating a feed that implemented the democratic values model; and testing its impact on people’s partisan animosity. The result: The team found lower partisan animosity among people shown a feed that downranked (or removed and replaced) posts expressing highly anti-democratic attitudes.

Furthermore, the study participants demonstrated the same level of engagement as they would with a typical engagement-based feed. This highlights the flawed focus of news feeds from platforms like Meta and YouTube. There are many ways in which such algorithms could be enhanced to promote democratic values, sustainability, equality, and more. However, these potential improvements are largely ignored by major tech companies.