We should build a “permanent” national social graph, similar to what we’ve done with the national voter file. Currently, the DNC, TargetSmart, Catalist, and soon the Democratic Data Exchange maintain curated databases listing all of the voters in the United States. We would benefit greatly from a similar data effort which seeks to learn “real-world” relationships among people, which may be relevant to political organizing. Such relationships would include family ties, friendships, religious affiliations, alumni networks, etc. Once these relationships are “learned” they do not need to be “re-learned” - e.g., if in one campaign cycle we learn that Alice is related to Bob, then we can leveage that same fact in the next cycle, because they are still related.
Such a data structure would enable us to target our messages at scale: not only could we discern what message is best to send to individual voters to motivate them to support a given cause or candidate, we could also discern which messenger is the best to deliver that message.
Via Sri Kulkarni with permission - experience with existing relational organizing efforts has taught a few lessons to date:
- Relational organizing is best done at large scale, because a supporter’s contacts are not generally restricted to a single district.
- Relational organizing needs sufficient time to build a large network.
- It is possible to scale relational organizing with paid organizers or volunteers - but the incentive structures for the two are different and paid organizers may not stick around for the long term.
- Relational organizing can be married with a “precinct captain” approach. (In the precinct captain approach, a state or district is broken down into precincts and individuals are given responsibility for turning out voters within their precinct.) Often, precinct captains wish to add their network of contacts within the precinct into the relational program as contacts.
These lessons can clearly be applied to a long-term, permanent social graph building project.
See discussions in the following podcast episodes:
- Lana Hansen and Sri Kulkarni, at 1:00:17
- Jeremy Smith, at 1:12:06