Projects

Regulation Room

Regulation Room is designed and operated by the Cornell e-Rulemaking Initiative (CeRI) and hosted by the Legal Information Institute (LII). The site is a pilot project that provides an online environment for people and groups to learn about, discuss, and react to selected rules(regulations) proposed by federal agencies. It expands the types of public input available to agencies in the rulemaking process, while serving as a teaching and research platform. Learn more.

Drafting Room Experiment

The Drafting Room experiment is testing the boundaries of effective online civic engagement. Is it possible to go beyond soliciting feedback from individual members of the public and help them move towards collaboratively producing effective policy inputs? Specifically, we are focused on three main questions:

• What psychological and experiential factors predict different levels of civic engagement in online deliberation of policy?
• What effect does engagement in online deliberation of public policy have on people’s perceptions of the decision-making processes and institutions?
• What effect do different facilitative interventions have on co-production of policy inputs in an online environment?

We investigate those questions by using a large scale controlled experiment focused on deliberation of an actual campus policy change. The practical goal of the project is to test platform features and facilitation procedures for collaborative drafting of policy inputs by the members of the public.

Exploring predictors of effective online civic engagement: The development of NLP techniques to identify prime targets for rulemaking outreach efforts

Policymaking bodies have limited financial, human, computational, and temporal resources for recruiting members of the public to participate in online deliberations surrounding rulemaking processes. Thus in order to make the most efficient and effective use of these resources, this project is an effort to improve outreach strategies by identifying people who are likely to be highly motivated and capable contributors. Its aim is to develop natural language processing techniques that can analyze text online to detect cognitive and experiential characteristics that are positively or negatively associated with a person's willingness and ability to participate effectively.

To date, experiments have concentrated on recruiting people from the social media platform Twitter by analyzing the text that Twitter users post. An initial experiment in Spring 2012 examined whether text similarity between rulemaking concepts and a Twitter user's bio, tweets, or some combination was correlated with that person's willingness to participate during an open comment period on CeRI's RegulationRoom.org.

The current experiment begun in Summer 2012 continues to explore predictors of an individual's readiness for engagement. In particular, the focus is on identifying citizens who exhibit evidence of:

1. topical expertise and interest as evidenced by the individual's mention in tweets of salient terms and phrases drawn from rule documents and materials or
2. self-efficacy as demonstrated by the individual's expression in tweets of certain dimensions of psychological models of personality.

Additionally, the current experiment investigates whether outreach messaging can be crafted with wording that amplifies and appeals to these interests and mental states in order to be more persuasive, achieve better response rates, and elicit higher quality comments.

Unsubstantiated Claim Detection

With the continued advancement of information technology, we are experiencing an explosion of user participation in the web environment. In order to efficiently manage the growing amount of information, this project aims to automatically evaluate the quality of user generated texts, such as reviews and comments, by means of determining whether each claim is accompanied by substantiation. A working assumption here is that user generated texts that consist of substantiated claims are of better quality than those that contain unsubstantiated claims.