Improving social media for democratic discourse

Fan Cheng; Amirhossein Farzmahdi; Pinyuan Feng; Kedar Garzón Gupta; Trenton Jerde; Nikolaus Kriegeskorte; Zi Qi Liow; Akihito Maruya; Savannah Smith; Patrick Stinson; JohnMark Taylor

arXiv · 2610.10816

Improving social media for democratic discourse

Fan Cheng·Amirhossein Farzmahdi·Pinyuan Feng·Kedar Garzón Gupta·Trenton Jerde·Nikolaus Kriegeskorte·Zi Qi Liow·Akihito Maruya·Savannah Smith·Patrick Stinson·JohnMark Taylor

View original source ↗Read source document ↗Export metadata ↓

Abstract

Social media have expanded opportunities for communication and political participation, but today's dominant platforms are optimized primarily for engagement and advertising revenue, contributing to concerns about polarization, misinformation, social isolation, and loss of civility. We explore how social media might instead be deliberately designed to support democratic discourse, collective deliberation, and collective intelligence. Drawing on literature across computer science, psychology, political science, and related fields, we present a modular collection of mechanisms that could be implemented individually or in combination. These include user-controlled and open recommender systems, tools for exposure to diverse perspectives, new forms of cognitive and epistemic feedback, collaborative and AI-assisted fact-checking, privacy and visibility controls, mechanisms for improving civility and evidentiary integrity, and reputation systems that reward high-quality participation. The proposals are intended both as a practical menu of design possibilities and as a starting point for broader interdisciplinary discussion about digital public spaces designed around democratic values rather than engagement alone.

Explore related subjects

cs.CY cs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Fan Cheng, Amirhossein Farzmahdi, Pinyuan Feng, Kedar Garzón Gupta, Trenton Jerde, Nikolaus Kriegeskorte, Zi Qi Liow, Akihito Maruya, Savannah Smith, Patrick Stinson, JohnMark Taylor. 2026-10-07. Improving social media for democratic discourse. https://arxiv.org/abs/2610.10816

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

KEEP EXPLORING

Related papers

arXiv2026

Artificial intelligence is creating a new global linguistic hierarchy

Artificial intelligence (AI) has the potential to transform healthcare, education, governance and socioeconomic equity, but its benefits remain concentrated in a small number of languages (Bender, 2019; Blasi et al., 2022; Joshi et al., 2020; Ranathunga and de Silva, 2022; Young, 2015). Language AI - the technologies that underpin widely-used conversational systems such as ChatGPT - could provide major benefits if available in people's native languages, yet most of the world's 7,000+ linguistic communities currently lack access and face persistent digital marginalization. Here we present a global longitudinal analysis of social, economic and infrastructural conditions across languages to assess systemic inequalities in language AI. We first analyze the existence of AI resources for 6003 languages. We find that despite efforts of the community to broaden the reach of language technologies (Bapna et al., 2022; Costa-Jussà et al., 2022), the dominance of a handful of languages is exacerbating disparities on an unprecedented scale, with divides widening exponentially rather than narrowing. Further, we contrast the longitudinal diffusion of AI with that of earlier IT technologies, revealing a distinctive hype-driven pattern of spread. To translate our findings into practical insights and guide prioritization efforts, we introduce the Language AI Readiness Index (EQUATE), which maps the state of technological, socio-economic, and infrastructural prerequisites for AI deployment across languages. The index highlights communities where capacity exists but remains underutilized, and provides a framework for accelerating more equitable diffusion of language AI. Our work contributes to setting the baseline for a transition towards more sustainable and equitable language technologies.

cs.CY↗

arXiv2026

Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments: An Empirical Study

This study aimed to develop and validate a computational framework for modeling therapist--client interaction processes and to examine how therapist behaviors and relational qualities relate to client self-disclosure and emotional expression in psychotherapy sessions. We developed automated assessment pipelines using large language models (LLMs) to measure therapist behaviors (empathy, exploration), relational quality (rapport), and client outcomes (self-disclosure, self-directed negative emotion, outward-directed negative emotion). Model-generated scores were validated against human annotations. We then applied these measures to approximately 2,000 hours of psychotherapy transcripts from the Alexander Street corpus. This yielded an estimation sample of 119,233 client utterances from 1,573 sessions and 37 clients. Automated measures showed good to excellent agreement with human annotations (ICC = 0.60 - 0.81), with rapport (ICC = 0.81), self-disclosure (ICC = 0.78), empathy components (ICC = 0.60 - 0.75), and emotion measurements (ICC = 0.66 - 0.80). SEM analyses revealed that therapist empathy and exploration were each associated with increased client disclosure and increased expression of self-directed negative emotions. Both behaviors were also associated with reduced expressions of enjoyment. Surprisingly, rapport in the prior session showed no association with client self-disclosure or negative emotion expression. This study demonstrates that LLM-based measurement combined with structural modeling can capture core therapeutic processes at scale.

cs.CY↗

arXiv2026

Empirically Grounded Behavioral Adaptivity for LLM-Based Virtual Patients in Psychotherapy Training

Simulated patients offer a scalable way to train psychotherapy micro-skills such as empathic responding and exploratory probing, but current systems either follow fixed scripts or rely on LLMs that drift unpredictably over long sessions. We present the Adaptive Virtual Patient (AVP), which adapts its disclosure behavior in response to trainee skill, from guarded, through moderate openness, to full disclosure. The AVP is informed by a structural equation model fit to nearly 2,000 hours of real-world psychotherapy transcripts, which quantifies how therapist empathy and exploration shift a patient's openness over time. An LLM generates the AVP's utterances conditioned on a disclosure level that the dynamics module updates each turn. In an evaluation with 20 clinicians and trainees over 80 sessions (1,033 turns), the AVP's disclosure rises in response to therapist empathy and exploration, while a prompt-only baseline stays flat; ablations confirm that the empirically motivated parameterization outperforms alternatives, with exploration carrying most of the adaptive signal.

cs.CY↗