algorithmic choice & control
Instagram Tests Reels Algorithm Control Options
socialmediatoday.com
Bonsai implements a platform-agnostic framework comprising Planning, Sourcing, Curating, and Ranking modules
BONSAI: Intentional and Personalized Social Media Feeds
Bonsai: Intentional and Personalized Social Media Feeds
arxiv.org
End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting
arxiv.orgIn this work, we compare three strategies—(1) example labeling, (2) rule writing, and (3) large language model (LLM) prompting—for end users to build personal content classifiers
Similarly, some users want to categorize their emails into more fine-grained folders other than the default “Spam” or “Promotions” folders [ 63 , 98 ]. As a result, there have been growing calls to empower end users to customize content curation classi-fiers [ 42 , 43 ]. In particular, researchers have argued for personal content moderation tools... See more
End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting
We found that writing prompts generally allowed participants to create personal content filters with higher performance more quickly, primarily driven by having the highest recall despite comparable precision across conditions.
End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting
In addition, curation tools should enable easy iteration to improve initial creations incrementally, as so-cial media users may be more amenable to short tasks spread out over many sessions as opposed to s single lengthy task. Instead, existing systems often assume that users want to create highly performant custom classifiers in a single sitting [... See more
End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting
As a result, for many users to realistically participate in curation, tools for authoring classifiers should support rapid initialization . As end users often-times engage with internet content as a leisure activity, a successful tool should allow them to quickly and intuitively build an initial classifier with decent performance.