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Published:2026/1/11 10:19:04

ソーシャルメディアをギャル化💖 みんなで仲良く情報交換しよ!

超要約: SNS(ソーシャルネットワーキングサービス)の悪いとこ直して、みんなが楽しく話せる場所作ろ!✨

🌟 ギャル的キラキラポイント✨ ● SNSでよくある、情報が偏っちゃう問題(情報の偏り)を解決するぞ! ● 意見の違う人たちが仲良くなれるような工夫(バランスの実現)をするの! ● 新しいコミュニティ(集まり)がどんどん作れるように応援するよ!📣

詳細解説 ● 背景 SNSって便利だけど、なんかギスギスすることもあるよね?🥺 情報が偏ったり、特定のグループだけで盛り上がったり…。この研究は、そんなSNSの悪いところを直して、みんながもっと楽しく、仲良く話せるようにするための研究なんだ!

● 方法 まずは、SNSで何が問題かを見つけるよ!🤔 そして、3つの解決策を提案!

  1. コンテンツが、どんなコミュニティで人気なのかとかを表示する(社会的なコンテキストの付与)
  2. いろんな意見のバランスを取って、みんなが見れるようにする(バランスの実現)
  3. 新しいコミュニティが作りやすくなるようにする(社会的なダイナミズムの促進)

続きは「らくらく論文」アプリで

Community by Design

E. Glen Weyl / Luke Thorburn / Emillie de Keulenaar / Jacob Mchangama / Divya Siddarth / Audrey Tang

Social media empower distributed content creation by algorithmically harnessing "the social fabric" (explicit and implicit signals of association) to serve this content. While this overcomes the bottlenecks and biases of traditional gatekeepers, many believe it has unsustainably eroded the very social fabric it depends on by maximizing engagement for advertising revenue. This paper participates in open and ongoing considerations to translate social and political values and conventions, specifically social cohesion, into platform design. We propose an alternative platform model that includes the social fabric an explicit output as well as input. Citizens are members of communities defined by explicit affiliation or clusters of shared attitudes. Both have internal divisions, as citizens are members of intersecting communities, which are themselves internally diverse. Each is understood to value content that bridge (viz. achieve consensus across) and balance (viz. represent fairly) this internal diversity, consistent with the principles of the Hutchins Commission (1947). Content is labeled with social provenance, indicating for which community or citizen it is bridging or balancing. Subscription payments allow citizens and communities to increase the algorithmic weight on the content they value in the content serving algorithm. Advertisers may, with consent of citizen or community counterparties, target them in exchange for payment or increase in that party's algorithmic weight. Underserved and emerging communities and citizens are optimally subsidized/supported to develop into paying participants. Content creators and communities that curate content are rewarded for their contributions with algorithmic weight and/or revenue. We discuss applications to productivity (e.g. LinkedIn), political (e.g. X), and cultural (e.g. TikTok) platforms.

cs / cs.CY / cs.SI