Effective Altruism (EA) often promotes unconventional ideas, some of which gain traction while others face backlash for being too "weird" or misaligned with mainstream values. This creates a dilemma: when should advocates push fringe ideas, and when should they adjust their messaging to avoid alienating potential allies? Currently, there’s no systematic way to measure how "weird" an idea is perceived to be, how this perception varies across groups, or how it changes over time. This gap leads to inefficiencies in advocacy and missed opportunities to refine communication strategies.

Measuring and Tracking "Weirdness"

One way to address this gap could involve creating a tool that quantifies the perceived weirdness of EA ideas. This tool might use surveys, sentiment analysis of media coverage, and expert ratings to assign a "weirdness score" to specific ideas, such as prioritizing global health over local charities or focusing on AI alignment. It could also track public and EA-internal endorsement rates over time, broken down by demographics like age, geography, and political affiliation. By comparing perceptions between EA members and the general public, the tool could highlight discrepancies—for example, whether advocating for insect welfare is seen as equally weird inside and outside EA circles.

Strategic Insights and Applications

The tool could correlate weirdness scores with real-world outcomes, such as donation rates or media backlash, to identify when unconventionality helps or harms. A dynamic dashboard might allow users to explore historical trends, simulate how framing changes could affect perceptions, or submit new ideas for evaluation. Potential beneficiaries include EA organizations (to tailor messaging), advocates (to gauge personal risk/reward), researchers (to study idea acceptance), and mainstream philanthropists (to identify overlooked causes). Stakeholder incentives could range from altruistic participation in surveys to partnerships with polling firms for data access.

Execution and Challenges

A minimal version could start with manual surveys of EA and non-EA groups to score a few high-profile ideas, visualizing the disparity in perceptions. Scaling up might involve automating data collection through polling APIs or media sentiment analysis, while refining the tool could add predictive modeling—for example, estimating how advocating for a specific idea might affect donor retention. Challenges include ensuring the objectivity of weirdness scores (by using multiple data sources) and avoiding overfitting to EA echo chambers (by adequately representing non-EA demographics).

This tool could fill a unique gap by turning subjective perceptions into actionable data, helping EA navigate the trade-offs between innovation and social acceptability. By providing insights into how ideas are received, it could empower advocates to communicate more effectively while staying true to high-impact goals.