How algorithmic amplification distorts scientific communication: systemic gaps in attention economies leave publics misinformed despite data abundance
Original framing: “How missing information can misinform” — Phys.org
The study echoes mid-20th-century fears about mass media’s role in public understanding, but today’s attention economy is turbocharged by surveillance capitalism and platform monopolies. Historical parallels include the 1920s radio boom, where sensationalized science reporting led to public misinformation about health and technology—yet solutions then focused on regulation, not algorithmic redesign. The current crisis is distinct in scale and speed, but the pattern of extractive media distorting public knowledge is not new.
The UC San Diego study inadvertently reveals how the attention economy’s logic—optimizing for shareability over accuracy—mirrors historical patterns of knowledge extraction, from colonial science to modern surveillance capitalism.
Platforms like Meta and Google, which profit from engagement metrics, act as digital gatekeepers, truncating science into decontextualized fragments that serve their bottom line, not public understanding. This dynamic disproportionately harms marginalized communities and Indigenous epistemologies, which resist commodification but are systematically excluded from algorithmic visibility. The solution lies not in tweaking engagement algorithms but in dismantling the extractive logics that underpin them, replacing them with cooperative, community-led models that treat knowledge as a relational, not transactional, good. The trickster’s lesson is clear: the absurdity of an attention economy that turns truth into a commodity demands not just critique but structural reinvention, where platforms serve knowledge, not the other way around.