The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2534
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:49:49.368975Z
status: published
lens: trace
sector: other
headline: Understanding and Supporting Online Discussion with Opinionated Chatbots
dek: Opinionated chatbots are increasingly present on online platforms and have the potential to shape public discourse by influencing individuals’ viewpoints before they engage in discussions. Despite their growing presence, the impact of interacting with opinionated chatbots on subsequent online interactions remains largely unexplored. This study investigated how exposure to different types of opinionated chatbots, specifically those expressing opposing, reinforcing, or balanced viewpoints, affected participants’ s…
gain_title: In a controlled experiment with 83 participants, 59% of those who prepared with a consistently opposing chatbot changed their opinion, versus 31% with a balanced chatbot and 33% with a reinforcing one.
problem_title: In the same experiment, participants rated the opposing chatbot lower on trust (4.56 on a 7-point scale) than the balanced chatbot (5.49) or the reinforcing one (5.17).
trace_subject: Opposing chatbot used to prepare for an online discussion on economic growth and environmental protection
gain_reading: In a controlled experiment with 83 participants, 59% of those who prepared with a consistently opposing chatbot changed their opinion, versus 31% with a balanced chatbot and 33% with a reinforcing one.
gain_evidence: 31% in Balanced condition (n=9), 59% in Opposing condition (n=16), and 33% in Reinforcing condition (n=9)
problem_reading: In the same experiment, participants rated the opposing chatbot lower on trust (4.56 on a 7-point scale) than the balanced chatbot (5.49) or the reinforcing one (5.17).
problem_evidence: participants reported significantly higher trust when interacting with Balanced chatbot (M = 5.49, SD = 0.79) than with Opposing chatbot (M = 4.56, SD = 0.67, p < .001)
quick_read: Researchers at the National University of Singapore and collaborators ran a controlled experiment with 83 adult students. Each person discussed whether economic growth can help environmental protection with a GPT-3.5-based chatbot for 5 to 10 rounds, then debated the same topic in text chat with a human who was actually a scripted confederate. The chatbot either opposed, reinforced or balanced the participant's views.

Opinion change was highest after the opposing chatbot: 59% of participants changed stance, against 31% with the balanced chatbot and 33% with the reinforcing one. Only the opposing-versus-balanced gap was statistically significant. The opposing bot also scored lowest on trust, so the design that moved opinions most was the one people liked least.

Chatbot style also carried into later talk with a human. Agreement expressions made up 49% of participants' statements after the reinforcing chatbot and 31% after the opposing one. The authors say this points to a trade-off for designers between prompting people to rethink positions and keeping trust in the tool.
limitation: The experiment used a single topic (economic growth and environmental protection), a short chatbot session and a scripted human partner. Only opposing versus balanced reached significance for opinion change, and the communication-quality difference did not survive correction.
tag: Dual reading
key_points: The 83 valid participants were split into balanced (29), opposing (27) and reinforcing (27) groups; seven of 90 responses were dropped for misunderstanding the task or inconsistent answers. | Opinion shift rates were 31% (balanced), 59% (opposing) and 33% (reinforcing); only opposing versus balanced differed significantly (p<.05). | Across 502 coded conversation rounds, communication patterns differed by condition (p=.013), driven by the opposing versus reinforcing contrast (Bonferroni-adjusted p=.008). | Agreement made up 49% of expressions after the reinforcing chatbot versus 31% after the opposing one; the opposing chatbot did not raise disagreement. | Trust scores differed by group (F(2, 80) = 10.54, p < .001); reinforcing also beat opposing on trust (p < .05). | Differences in communication quality did not hold after correction for multiple tests (adjusted p = .116). | Participants in the opposing group praised the chatbot's reasoning (57% of 14 commenters); some called it rigid or pushy, and two said it dropped its stance when challenged. | In open-ended answers, 60% of participants said the chatbots helped with background information and critical reasoning.
rundown: Participants were students aged 21 and over recruited via Telegram and paid 12 US dollars an hour. The session lasted about an hour. Participants first chatted with one of three chatbots built on the GPT-3.5 API with a temperature of 0.2, then held a roughly 10-minute text discussion in which a confederate consistently disagreed with them and gradually moved toward consensus.

Opinion shift was measured by comparing stance before and after the chatbot chat, with two coders agreeing on a 27-response subset. Communication patterns were coded for agreement, disagreement or unclear stance across 810 rounds, of which 502 contained valid arguments. Trust and communication quality were rated on seven-point scales.

Participants who prepared with the opposing chatbot described the human partner mainly in terms of reasoning quality (68% of those with positive views), while balanced and reinforcing participants stressed friendliness. The authors suggest private, interactive disagreement with a chatbot may let people reconsider views without feeling threatened, and propose the opposing and reinforcing styles as tools for different discussion goals.
sources:
- peer_reviewed | Proceedings of the ACM on Human-Computer Interaction | https://doi.org/10.1145/3817024 | 2026-09-23
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

6d40639953344602211efac35947de73845e0fcb30836680a1509b56934fa6cc

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000