Cited by 572Social Robot Interaction and HRIAI in Service InteractionsSpeech and dialogue systems

The DOI matches its Crossref record; 115 of the 116 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 425 sources its claims cite is checked for support yet.

How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design

Open access
Chaves, Ana Paula, Gerosa, Marco Aurelio·2021·International Journal of Human–Computer Interaction

Abstract

Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human–human communication. In this survey, we argue that chatbots should be enriched with social characteristics that cohere with users’ expectations, ultimately avoiding frustration and dissatisfaction. We bring together the literature on disembodied, text-based chatbots to derive a conceptual model of social characteristics for chatbots. We analyzed 56 papers from various domains to understand how social characteristics can benefit human–chatbot interactions and identify the challenges and strategies to designing them. Additionally, we discussed how characteristics may influence one another. Our results provide relevant opportunities to both researchers and designers to advance human–chatbot interactions.

Checked 2026-10-09 · 1 reference not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This survey analyzes 56 studies on disembodied, text-based chatbots to derive a conceptual model of 11 social characteristics grouped into conversational intelligence, social intelligence, and personification. It maps reported benefits, challenges, and strategies for each characteristic and proposes 22 interrelationship propositions to guide chatbot design and future research.

Key findings

  • The review derived 11 social characteristics: proactivity (18 studies), conscientiousness (11), communicability (6), damage control (12), thoroughness (13), manners (10), moral agency (6), emotional intelligence (14), personalization (11), identity (16), and personality (12).
  • These characteristics are grouped into three categories: conversational intelligence, social intelligence, and personification; the paper reports benefits such as increasing human-likeness, believability, engagement, and interpersonal relationships, alongside challenges such as timing, privacy, emotional regulation, and stereotyping.
  • The survey states 22 propositions about how characteristics influence one another, e.g., proactivity influences perceived personality and conscientiousness, and identity influences moral agency.
  • Most surveyed papers focus on task-oriented chatbots (59%), while general-purpose chatbots account for 33%; education is the most representative specific domain with 9 papers, followed by customer services with 5 papers.

Methods

  • Literature survey using Google Scholar with a search string covering synonyms such as chatbots, chatterbots, conversational agents, conversational interfaces, dialogue systems, and digital assistants; studies on embodiment, speech input, social bots, and personal assistants were excluded.
  • Screening started with about one thousand papers, reduced to 464, then 104, and finally 56 studies after title, abstract, and full-text review; the dataset of included and excluded studies is available in Chaves and Gerosa (2019).
  • Analysis used a qualitative coding process inspired by Grounded Theory: one researcher selected relevant statements and labeled them as characteristics, a second researcher reviewed the set, and both discussed merging, renaming, or removing characteristics; quotes were then labeled as benefits, challenges, or strategies.

Limitations

  • The paper states it focused on disembodied, text-based chatbots, excluding embodied and speech-based conversational agents, which may underrepresent domains such as Ambient Intelligence and Software Engineering.
  • The paper states the definition of chatbot is not consolidated, so some studies with relevant social aspects may not have been found despite using several synonyms and Google Scholar.
  • The paper states the conceptual model was derived through qualitative coding and relies on researchers’ subjective assessment, mitigated by in-person consensus discussions; it also notes that most surveyed results are exploratory and that strategies for moral agency and thoroughness were not identified.

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Integrity signals

Registry

Crossref · 10.1080/10447318.2020.1841438

Cites

115 of 116 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

308 Not checked yet · 46 Own interpretation · 44 Own result · 28 Own method · 28 No basis given

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Show all 189 claims

  • Probably not a substantial claim. 213
  • It may state two separate claims as one. 108
  • The quote only partly states this claim. 2
265 of 265
  • The paper relies on this without support.

    The increasing need to approximate conversational interaction styles raises users' expectations that chatbots present social behaviors habitual in human-human communication.

    “The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human-human communication.”
  • The paper relies on this without support.

    Chatbots' social characteristics knowledge is spread across several domains in which chatbots have been studied.

    “Although chatbots' social characteristics have been explored in the literature, this knowledge is spread across several domains in which chatbots have been studied, such as customer services, education, finances, and travel.”
  • The paper relies on this without support.

    A lack of studies brings together the social characteristics that influence the way users perceive and behave toward chatbots.

    “Whilst the literature has extensively reviewed the technical aspects of chatbots design (e.g., Ahmad, Che, Zainal, Rauf, and Adnan (2018); Deshpande, Shahane, Gadre, Deshpande, and Joshi (2017); Masche and Le (2017); Ramesh, Ravishankaran, Joshi, and Chandrasekaran (2017); Thorne (2017); Walgama and Hettige (2017); Winkler and S¨ ollner (2018)), a lack of studies brings together the social char”
  • The paper relies on this without support.

    The literature presents no coherent definition of chatbots.

    “The literature presents no coherent definition of chatbots; thus, to find relevant studies we used a search string that includes the synonyms 'chatbots, chatterbots, conversational agents, conversational interfaces, conversational systems, conversation systems, dialogue systems, digital assistants, intelligent assistants, conversational user interfaces, and conversational UI' .”
  • The paper relies on this without support.

    Most studies do not explicitly label their results as social characteristics/traits.

    “We decided to not include terms that relate to social characteristics/traits, because most studies do not explicitly label their results as such.”
  • The paper relies on this without support.

    The characteristics and categories are seldom explicitly highlighted in the literature.

    “Noticeably, the characteristics and categories are seldom explicitly highlighted to in the literature, so the conceptual model was derived using a qualitative coding process inspired by methods such as Grounded Theory (Auerbach & Silverstein, 2003) (open coding stage).”
  • The paper relies on this without support.

    The set of social characteristics identified from the surveyed literature is the relevant set for designing chatbot social behavior.

    “In this survey, we investigated the literature on disembodied, text-based chatbots to answer the question 'What chatbot social characteristics benefit human interactions and what are the challenges and strategies associated with them?'”
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Citations

Cites

Show all 115 references

Research works printed but not linked: 46

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Metadata

Authors
Chaves, A. P., & Gerosa, M. A.
Affiliations
  • Universidade Tecnológica Federal do Paraná (BR)
  • Northern Arizona University (US)
Corresponding author
Chaves, Ana Paula
Venue
International Journal of Human–Computer Interaction
Published
2020-11-08
Volume
37
Issue
8
Pages
729-758
ISSN
  • 1044-7318 (ISSN-L)
  • 1532-7590
OpenAlex
W2939803556
Cited by
572
References
115
Citations by year
2026: 101 · 2025: 134 · 2024: 118 · 2023: 104 · 2022: 74 · 2021: 36 · 2020: 3
FWCI
59.25
Type
journal article
Language
en
Open access
Locations
  • International Journal of Human-Computer Interaction (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
chatbots, human-chatbot interaction, social characteristics, chatbot design, conversation, text-based chatbots, user expectations, conceptual model

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