Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Social and Information Networks

arXiv:2606.21043 (cs)
[Submitted on 19 Jun 2026]

Title:Reducing the rate of personal insults in social media with bystander bots

Authors:Libby Hemphill, Lingyao Li, Ryan Burton, David Jurgens
View PDF HTML (experimental)
Abstract:Prompted by previous research on strategies for reducing interpersonal conflict and addressing problematic behaviors in online communities, a randomized controlled trial on Reddit compared various responses for reducing the rate of personal insults users post to the site. We generated replies from five deescalation strategies and used an automated procedure for posting them as replies to insulting comments. The findings reveal that automated replies to insults can effectively reduce their rate. Appreciation performed best. Not all strategies performed well, though. We conclude that automated responses are a viable tool for addressing some problematic behaviors. We discuss their potential utility and limitations.
Subjects: Social and Information Networks (cs.SI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2606.21043 [cs.SI]
  (or arXiv:2606.21043v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.2606.21043

Submission history

From: Libby Hemphill [view email]
[v1] Fri, 19 Jun 2026 02:20:05 UTC (26,595 KB)
Full-text links:

Access Paper:

license icon view license

Current browse context:

cs.SI
< prev   |   next >
new | recent | 2026-06
Change to browse by:
cs
cs.HC

References & Citations

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)