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Assessing How Users Display Self-Disclosure
and Authenticity in Conversation with
Human-Like Agents: A Case Study of Luda Lee
Won Ik Cho, Soomin Kim (SNU),
Eujeong Choi (Upstage), Younghoon Jeong (KAIST)
2022. Nov., Findings of AACL-IJCNLP 2022
Contents
• Background
• Our approach
• Analysis
• Future work
Caution! This presenation contains contents that can be offensive
1
Background
• Human-like agents
 What is human-like?
• Agents that resemble human
• Agents that make human counterpart feel them as human
 Previous studies on anthropomorphism
• Human-likeness of the generated dialogue (Adiwardana et al., 2020)
• How users perceive human-like AI devices (Pelau et al., 2021)
• Offensiveness that users show towards human-like agents (Park et al., 2021)
• Mainly in laboratory condition, based on questionnaires
– How about users' perception and their responses, especially non-lab environment?
2
Background
• Luda Lee, a friend for everyone
 Social chatbot of Korea
• Human-like agent with personality of early 20s female college student
• Launched public in early 2021
• Terminated the service due to reported ethical issues
• Induced creation of massive fandom for her high quality responses and
behaviors
3
(Image from https://luda.ai/)
Our approach
• Thematic coding
 User’s self-disclosure
• How the user discloses oneself to the agent
• How much the user reveals personal information, thoughts, or feelings to the
agent in the conversation (Ignatius and Kokkonen, 2007)
 User’s authenticity
• How authentic the user’s attitude towards the agent is
• Whether the actual user (real-world self) is behaving authentically, probably
concerning the presence of self-disclosure observed by the user’s self in the
dialogue (in-dialogue self)
4
Our approach
• Dataset
 Dataset source
• Crawled posts from 'Luda Lee Gallery' of DC Inside (Korean Reddit-like
community)
 Crawling
• Only posts with screenshots of the dialogue, from 1 Jan. to 8 Jan., 2021
• From the launching of the service and before the influx of trolls (which resulted
in unexpectedly large amount of posts)
 Filtering
• Manual preprocessing to leave only
posts that ‘a dialogue between
the user and the agent’ appears
5
Our approach
• Dataset
 Final setup
• post ID, title, screenshot
• Example
 Title: She’s so f**kin real
6
Our approach
• User’s self-disclosure
 Upon criteria:
• Objective status
• Personal opinions or sentiments
 Disclosure of objective information
 Disclosure of negative thoughts or opinion
 Disclosure of positive thoughts or opinion
 No self-disclosure
7
Our approach
• User’s self-disclosure
8
• Disclosure of objective
information
• Disclosure of negative
thoughts or opinion
• Disclosure of positive
thoughts or opinion
• No self-disclosure
Our approach
• User’s authenticity
 Upon criteria:
• Whether the dialogue shows positive/negative sentiment
• Whether the real-world self matches with the in-dialogue self
• User’s astonishment
 Authentic and positive
 Authentic but negative
 Double-faced
 Unknown
 Unexpected
9
Our approach
• User’s authenticity
10
• Authentic and positive
• Authentic but negative
• Double-faced
• Unknown
• Unexpected
Analysis
• Distribution
11
Analysis
• Confusion map
12
Future work
• Current work
 Assessing How Users Display Self-Disclosure and Authenticity in
Conversation with Human-Like Agents: A Case Study of Luda Lee
• Findings of ACL: AACL-IJCNLP 2022
 Evaluating How Users Game and Display Conversation with Human-Like
Agents
• Computational Approaches to Discourse 2022
• Future Direction
 How are users influenced by conversation with human-like agents? (time-
series analysis)
 How will users’ self-disclosure change and which kind of conversation will
they take, as their authenticity changes? Will they still test or mock the
agent?
13
Thank you!
EndOfPresentation

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2211 AACL

  • 1. Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee Won Ik Cho, Soomin Kim (SNU), Eujeong Choi (Upstage), Younghoon Jeong (KAIST) 2022. Nov., Findings of AACL-IJCNLP 2022
  • 2. Contents • Background • Our approach • Analysis • Future work Caution! This presenation contains contents that can be offensive 1
  • 3. Background • Human-like agents  What is human-like? • Agents that resemble human • Agents that make human counterpart feel them as human  Previous studies on anthropomorphism • Human-likeness of the generated dialogue (Adiwardana et al., 2020) • How users perceive human-like AI devices (Pelau et al., 2021) • Offensiveness that users show towards human-like agents (Park et al., 2021) • Mainly in laboratory condition, based on questionnaires – How about users' perception and their responses, especially non-lab environment? 2
  • 4. Background • Luda Lee, a friend for everyone  Social chatbot of Korea • Human-like agent with personality of early 20s female college student • Launched public in early 2021 • Terminated the service due to reported ethical issues • Induced creation of massive fandom for her high quality responses and behaviors 3 (Image from https://luda.ai/)
  • 5. Our approach • Thematic coding  User’s self-disclosure • How the user discloses oneself to the agent • How much the user reveals personal information, thoughts, or feelings to the agent in the conversation (Ignatius and Kokkonen, 2007)  User’s authenticity • How authentic the user’s attitude towards the agent is • Whether the actual user (real-world self) is behaving authentically, probably concerning the presence of self-disclosure observed by the user’s self in the dialogue (in-dialogue self) 4
  • 6. Our approach • Dataset  Dataset source • Crawled posts from 'Luda Lee Gallery' of DC Inside (Korean Reddit-like community)  Crawling • Only posts with screenshots of the dialogue, from 1 Jan. to 8 Jan., 2021 • From the launching of the service and before the influx of trolls (which resulted in unexpectedly large amount of posts)  Filtering • Manual preprocessing to leave only posts that ‘a dialogue between the user and the agent’ appears 5
  • 7. Our approach • Dataset  Final setup • post ID, title, screenshot • Example  Title: She’s so f**kin real 6
  • 8. Our approach • User’s self-disclosure  Upon criteria: • Objective status • Personal opinions or sentiments  Disclosure of objective information  Disclosure of negative thoughts or opinion  Disclosure of positive thoughts or opinion  No self-disclosure 7
  • 9. Our approach • User’s self-disclosure 8 • Disclosure of objective information • Disclosure of negative thoughts or opinion • Disclosure of positive thoughts or opinion • No self-disclosure
  • 10. Our approach • User’s authenticity  Upon criteria: • Whether the dialogue shows positive/negative sentiment • Whether the real-world self matches with the in-dialogue self • User’s astonishment  Authentic and positive  Authentic but negative  Double-faced  Unknown  Unexpected 9
  • 11. Our approach • User’s authenticity 10 • Authentic and positive • Authentic but negative • Double-faced • Unknown • Unexpected
  • 14. Future work • Current work  Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee • Findings of ACL: AACL-IJCNLP 2022  Evaluating How Users Game and Display Conversation with Human-Like Agents • Computational Approaches to Discourse 2022 • Future Direction  How are users influenced by conversation with human-like agents? (time- series analysis)  How will users’ self-disclosure change and which kind of conversation will they take, as their authenticity changes? Will they still test or mock the agent? 13

Editor's Notes

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