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Answer engines and other communication partners

Elena Esposito

Communication Theory2026https://doi.org/10.1093/ct/qtaf036article
ABDC A
Weight
0.50

Abstract

The article explores the social role and implications of large language models (LLMs) through the lens of media theory. Rather than considering LLMs as advanced forms of artificial intelligence, I argue that a communication-focused perspective provides a more effective way to interpret their impact on information management in contemporary society and to address the associated ethical and operational challenges. Supported by information management tools such as archives, catalogs, and later search engines, previous communication media expanded the scope of communication, making it possible to reach more, distant, diverse, and possibly anonymous communication partners. LLMs now signify a new phase in the evolution of communication, as they function themselves as communication partners capable of responding autonomously to user queries in a personalized manner. This perspective highlights and explains the capabilities and limitations of various LLM-based chatbots and Retrieval-Augmented Generation (RAG) models, while also addressing issues such as misalignment and hallucinations.

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https://doi.org/https://doi.org/10.1093/ct/qtaf036

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@article{elena2026,
  title        = {{Answer engines and other communication partners}},
  author       = {Elena Esposito},
  journal      = {Communication Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/ct/qtaf036},
}

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Answer engines and other communication partners

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.