Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2507.06561

Towards Designing Social Interventions For Online Climate Change Denialism Discussions

Ruican Zhong·Shruti Phadke·Beth Goldberg·Tanushree Mitra
View original source ↗Read source document ↗Export metadata ↓

Abstract

As conspiracy theories gain traction, it has become crucial to research effective intervention strategies that can foster evidence and science-based discussions in conspiracy theory communities online. This study presents a novel framework using insider language to contest conspiracy theory ideology in climate change denialism on Reddit. Focusing on discussions in two Reddit communities, our research investigates reactions to pro-social and evidence-based intervention messages for two cohorts of users: climate change deniers and climate change supporters. Specifically, we combine manual and generative AI-based methods to craft intervention messages and deploy the interventions as replies on Reddit posts and comments through transparently labeled bot accounts. On the one hand, we find that evidence-based interventions with neutral language foster positive engagement, encouraging open discussions among believers of climate change denialism. On the other, climate change supporters respond positively, actively participating and presenting additional evidence. Our study contributes valuable insights into the process and challenges of automatically delivering interventions in conspiracy theory communities on social media, and helps inform future research on social media interventions.

Explore related subjects

cs.HCcs.SI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ruican Zhong, Shruti Phadke, Beth Goldberg, Tanushree Mitra. 2025-08-11. Towards Designing Social Interventions For Online Climate Change Denialism Discussions. https://doi.org/10.1145/3757432

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC↗
arXiv2026

PRIMMDebug: Teaching Secondary School Students a Reflective Approach to Debugging

Debugging is a challenging and infuriating experience for many secondary school students learning their first text-based programming language. One frequent problem is the lack of reflection in students' debugging strategies, which makes error resolution unlikely and teacher reliance common. Tools that encourage more reflective and teacher-independent debugging may foster more success with fixing errors, but are lacking. This paper presents PRIMMDebug, an approach for teaching the debugging process to secondary school students. PRIMMDebug consists of an online tool that takes students through the steps of a pedagogical process based on PRIMM, a framework for teaching programming. The tool encourages written articulation throughout the debugging process and limits students' ability to run and edit code at certain stages. A classroom study with PRIMMDebug found a general reluctance among students to engage with the reflection it promotes, despite teachers appreciating this emphasis on reflection. We end by suggesting three considerations for future pedagogical debugging research and tooling: balance structure and flexibility, teach shorter debugging heuristics, and use tooling early on in students' programming journey.

cs.HC↗
arXiv2026

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC↗