SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912801488896000 |
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| author | Shu, Yubo Zhang, Peng Wu, Meng Chen, Yan Zhou, Haoxuan Liu, Guanming Zhang, Yu Zhang, Liuxin Wang, Qianying Lu, Tun Gu, Ning |
| author_facet | Shu, Yubo Zhang, Peng Wu, Meng Chen, Yan Zhou, Haoxuan Liu, Guanming Zhang, Yu Zhang, Liuxin Wang, Qianying Lu, Tun Gu, Ning |
| contents | Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. However, existing LLM-based search systems primarily rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking. To address this gap, we explore how the integration of social cues into LLM-based search influences users' perceptions, experiences, and behaviors. Focusing on social media platforms that are beginning to adopt LLM-based search, we integrate design workshops, the implementation of the prototype system (SoulSeek), a between-subjects study, and mixed-method analyses to examine both outcome- and process-level findings. The workshop informs the prototype's cue-integrated design. The study shows that social cues improve perceived outcomes and experiences, promote reflective information behaviors, and reveal limits of current LLM-based search. We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01094 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking Shu, Yubo Zhang, Peng Wu, Meng Chen, Yan Zhou, Haoxuan Liu, Guanming Zhang, Yu Zhang, Liuxin Wang, Qianying Lu, Tun Gu, Ning Human-Computer Interaction Artificial Intelligence Information Retrieval Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. However, existing LLM-based search systems primarily rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking. To address this gap, we explore how the integration of social cues into LLM-based search influences users' perceptions, experiences, and behaviors. Focusing on social media platforms that are beginning to adopt LLM-based search, we integrate design workshops, the implementation of the prototype system (SoulSeek), a between-subjects study, and mixed-method analyses to examine both outcome- and process-level findings. The workshop informs the prototype's cue-integrated design. The study shows that social cues improve perceived outcomes and experiences, promote reflective information behaviors, and reveal limits of current LLM-based search. We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions. |
| title | SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking |
| topic | Human-Computer Interaction Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2601.01094 |