Democracy depends on an informed electorate. But political issues and ballot measures can be confusing, obscuring the effects of one outcome versus another. Moreover, politics is personal. Once we make an initial decision about an issue, it can be hard to change our mind or see things from “the other side.” And talking about issues with those with whom we disagree can be challenging, especially when the conversation feels more like a debate than a discussion. Technology offers ways to alleviate these difficulties, but not without introducing problems of its own. The Internet and social media promised new ways for people to connect, discuss issues, and learn from each other. But in practice, both often inflame passions, solidify echo chambers, and spread misinformation. More recently, LLM chat interfaces may help people stay informed through personalized access to information, but mainstream chatbots tend to match user beliefs rather than clarifying or challenging them.12 Without the kind of pushback you’d encounter in a discussion between disagreeing friends, chatbots are ill-suited for helping people think through political issues in a balanced way. The goal of CivicChats is to address these shortcomings. Starting with ballot measures, CivicChats helps people better understand political issues through three different modes of discussion: a Q&A mode for understanding what a measure does and what’s at stake, an argumentative mode that presents competing views to your own, and a reflective mode that helps you examine and develop your own thinking.

(7/n) Led by CMlab PhD student Ziyu Chen, with @ChenhaoTan, Junfei Sun, Chenxi Li at UChicago -- thanks for inspiring all this during my 2024 sabbatical -- @joshnguyen99 at UPenn, and Jing Yao, Xiaoyuan Yi and Xing Xie at Microsoft Research Asia

(6/n) Future work? Generalizable classification across cultures and languages, and investigating generalisable prompting methodolgy on other subjective text classification tasks.

So what? MoVA could help: (1) Analyze Public Discourse: Understand the core values driving large-scale conversations on social and political issues. (2) Build Better AI: Ensure that artificial intelligence systems communicate in a way that's aligned with basic human ethics.

(5/n) MoVa also offers a new application to evaluate psychological surveys: by scoring relevance of moral dimensions for each survey item, we can detect potentially multi-loaded items in instruments like MFQ, MAQ, and PVQ, helping researchers rethink questionnaire design.

(4/n) MoVa provides resources defining this generalizable classification, including 16 labeled datasets and benchmarking results across four major, theoretically-grounded frameworks: Moral Foundations Theory (MFT), Human Values, Common Morality, and Morality-as-Cooperation (MAC)

(3/n) Our new methodology insight: "all@once LLM prompting strategy" outperforms fine-tuned models across multiple domains and frameworks. Why does it work? It uses inter-label dependencies resembling a classifier chain approach in ML.

(2/n) What is generalizable classification here? We think there are three key elements 1. New data domains - from short informal text to long passages. 2. New moral and value dimensions. 3. New frameworks - e.g. moral foundations, Schwartz human values, and many more!

Second, users' individual choices are essentially performing stochastic mirror descent on this objective, and will converge to the equilibria in the limit. (3/n)

We first discovery an optimization view of attention allocation. When users make choices reflecting personal preference and overall popularity - there is an underlying concave overall objectives encoding total utility and diversity. (2/n)

💡Want to present your amazing research at a cool conference and in a fantastic location? 🚨Don't miss the last deadline for ICWSM 2023 (Cyprus in June 2023). Still one month left (January 15, 23:59 AoE). 🌞Looking forward to your amazing papers and welcoming you all to Cyprus!

Look forward to talking at ISAAC about temporal data, including: models for events in continuous time, connecting back to discrete time space, machine learning tools for temporal processes, and a range of applications. Slides and references are here drive.google.com/drive/folde…

We invite research contributions to the Web and Society Track at #TheWebConf 2023. URL: www2023.thewebconf.org/calls… You can reach the track chairs @juhi153, @yelenamejova, @lexing at: websoc-thewebconf2023@easychair.org

Algorithm charts moral culture in 100k dilemmas 🔗cecs.anu.edu.au/news/100k-di… @ANUComputing teams up w/ @ANU_Philosophy to analyse 100k @Reddit threads debating moral dilemmas, a step toward endowing #AI w/ moral reasoning ability. @joshnguyen99 @lexing @cvklein @Jenny_L_Davis