Open Campaign is the political social network that matches individual voters and politicians based on their views and opinions about local and national issues.
Open Campaign was built to further the interaction between voters and politicians by providing transparency and clarity, resulting in educated and empowered voters who will have the ability to make informed decisions regarding candidates and their positions, regardless of budgets; and the policies that have the potential to bring about the necessary changes that society deserves.
The access to become and stay informed, while having the ability to influence and drive change, has never been easier or more efficient, for both individual voters and politicians, alike. Welcome to Open Campaign!
Open Campaign is the evolution of politics in the 21st century that draws voters, politicians and media into a Virtual Town Hall to:
Create, develop, discuss and highlight important issues.
Allow individuals to have a voice in the political sphere, without the inflexibility and limitations of current methods.
Provide politicians at all levels of government the ability to engage voters and gauge policy in real-time, from the local level all the way up to the federal level.
Politicians’ scores for each issue are drawn from interest group scores. Many interest groups rate or score politicians on the issue of concern to the group. These scores are commonly used as measures of where politicians stand on issues. On every issue where it was possible, we combined scores from multiple interest groups using a statistical technique called factor analysis. These scores give each politician a score between -1 (most liberal) and 1 (most conservative) for each issue. Users’ answers to the issue questions give them a score between -1 and 1 as well. We compare users’ scores with the politicians’ scores to provide a percent match to each politician.
Percent matches are calculated as follows. We calculate the absolute difference between the user’s position on an issue and each politicians’ position on that same issue. We multiply that difference by how important the user says the issue is to them. We then take the average of these importance-weighted differences, put that average on a 0 to 100 scale and subtract it from 100 to get percent match.