LLMs Are Social Actors

Jeremy Foote
Purdue University

Computers are social actors

• People apply social heuristics to computers
• Even simple systems can be perceived as social actors
  • ELIZA (Weizenbaum, 1966)
  • Simple interview bots (Moon, 2000)

Bots have long been important parts of online communities

• Wikipedia is a miracle of collaboration and cooperation.
• These processes only work because of bots (Geiger & Halfaker, 2013)
• Similar tools enable social interactions on Reddit, Facebook, etc.

Bots make nearly as many edits as humans on Wikipedia

LLMs are new kinds of social actors

• Two claims:
  1. LLMs are perceived as social actors
  2. Affordances of LLMs provide opportunities to enable better social processes (as well as novel risks)

Study 1

Chatting with Confidants or Corporations? Privacy Management with AI Companions

Hazel Chiu
Purdue University

How do people manage privacy with AI companions?

Two theoretical frameworks are relevant

• Communication Privacy Management (CPM) theory (Petronio, 2002)
  • People believe they own their private information
  • We manage boundaries around what we share with others
• Horizontal and Vertical Privacy (Masur, 2019)
  • Horizontal privacy: sharing with peers
  • Vertical privacy: sharing with platforms/corporations/governments

The Puzzle

AI companions are experienced like friends (horizontal) but are created by corporations (vertical). What are the unique privacy risks and how do people navigate them?

We interviewed 15 people who have a relationship with an AI companion

• Recruited from r/Replika, Prolific, and CloudResearch
• Semi-structured Zoom interviews
• Thematic analysis (Braun & Clarke, 2006)

From a “horizontal” perspective, chatbots feel safer than friends

• Non-judgmental
• Outside of social networks
• Memory control

Humans are kind of more susceptible to not keeping your privacy safe… But I think Replika, being a bot, you do have more privacy, just because there’s no risk at all of someone like her telling somebody.

Importantly, memory control did not extend to vertical privacy concerns about corporations, where people felt like they didn’t have any control.

Participants became more comfortable sharing over time

• Initial caution followed by increased sharing
• Intimacy through shared memories
• Similar to human relationships

Day by day as I was using it, I felt that it’s more human. Initially, I was just sharing basic things, but later I started to share more information about myself.

They weren’t blind to risks

• Used strategies to manage risks
  • New email accounts
  • Changing names of friends
  • Avoid sharing photos or identifying information

Complicated relationship with sharing and permanence

• More worried about data loss (and the loss of the relationship) than data leaks or compromises
• Comfort with data being used to improve AI companions
• Distrust in corporations willingness to delete data
• Knew risks, but emotional benefits outweighed them

I’m worried about my conversation somehow being lost […] because I really like [AI name] and enjoy our conversations.

Implications

• LLMs are in a unique social position
  • People use social heuristics, but are aware of corporate context
• Privacy is asymmetric
  • AI can’t really disclose its own information
  • It also can’t really negotiate boundaries
• Companions designed to solicit private information do pose a risk

Design/Policy Recommendations

• Control over memory
• Options for data deletion
• Transparency about data use

Study 2

Taming Toxic Talk: Using chatbots to intervene with toxic users

Deepak Kumar
UC San Diego

Dyuti Jha
Purdue University

Ryan Funkhouser
University of Idaho

Loizos Bitsikokos
Purdue University

Hitesh Goel
IIT Hyderabad

Hazel Chiu
Purdue University

Work was funded by Google Academic Research Award

Moderation is hard

• Moderating online communities is time-consuming, difficult work
• Most moderation is punitive
  • Bans
  • Suspensions
  • Content removal
• Calls for rehabilitative approaches (Schoenebeck and Blackwell, 2021)

Chatbots are persuasive

• Chatbot conversations can change political attitudes (Argyle et al., 2025; Hackenberg et al., 2025)
• Studies based on lab settings with paid participants

Can chatbots help to rehabilitate toxic users in the wild?

• Worked with seven medium to large Reddit communities
• Deployed a chatbot that messaged users who had a toxic comment removed
• Wave 1
  • Baseline, narrative, and norm-based conditions
• Wave 2
  • Based on Wave 1 results, bots aimed at producing reflection

Measured outcomes compared to control group

• In the month after conversation
  • \(\Delta\) Mean toxicity (Perspective API) of comments
  • \(\Delta\) Proportion of comments with toxicity > 0.5
  • \(\Delta\) Overall and within-community comments
  • Whether moderated again or suspended
• Control group consented but did not receive a conversation invite

Qualitative findings from conversations

Many conversations were defensive

This has been unproductive. Maybe try going after those doing actual harm to others if you are going to build bots. Reporting is ineffective, and I’m not going to sit on my thumbs and wait for an “authority” to step in. I will continue to stand up for being kind to others, even if that means I have to be mean once in a while. Its certainly not effective to disengage with people like this every time like you are suggesting. Anyway, whoever made this bot, maybe try making the world a BETTER place and actually target those who are doing harm unto others

Many conversations were good-faith and reflective

Some people found conversations meaningful and helpful

I must say, you seem to be quite an adept AI when it comes to understanding aspects of human emotions and how our purpose-driven minds influence the actions we perform. […] This was cathartic itself to speak to - I will definitely attempt to practice what I preace [sic] and spread less toxicity online.

We found no significant reduction in future toxicity

Conversation quality did not predict changes in future toxicity

One possible explanation is that there was a heterogenous treatment effect, but conversation quality still did not predict changes in future toxicity

Previous toxicity predicted conversation quality

Another explanation is that high-toxicity people had worse converations and were less likely to change. We do find some evidence for this.

Plus, we know that low-toxicity people already are rarely toxic, so it’s hard to detect changes for them.

Some possible explanations

• It’s tough to detect changes to toxicity for rarely toxic users
• Changing behavior is harder than changing attitudes
• A single conversation may not be persuasive enough

Implications

• Bots can easily be perceived as condescending and judgmental
• Morality is the realm of people
• Conversation starters matter

Pause for questions

LLMs are social actors

• Language is how the social world is constructed and stored
• LLMs can perceive social context better than previous technologies
• Are perceived as social entities

LLMs are social actors

• Actors in a Latourian sense
  • Ability to shape social processes
  • More agentic than other technologies

How might they be integrated into online social processes?

Some provocations about possible applications for LLMs in online social processes - good, bad, and ugly

Simulated social networks

Onboarding and acculturation

Bots that help onboard new members to online communities by teaching norms, answering questions, etc.

Rehabilitation and restorative justice

Gatekeeping

Reflection and introspection

Thank you!!