Scaling a social network is just inherently a very hard problem. Especially if you have a large userbase with a few very popular users. Stackshare recently did a nice blogpost about how we at Stream solve this for 300 million users with Go, RocksDB and Raft: https://stackshare.io/stream/stream-and-go-news-feeds-for-ov...
I think the most important part is using a combination of push and pull. So you keep the most popular users in memory and for the other users you use the traditional fanout on-write approach. The other thing that helped us scale was using Go+RocksDB. The throughput is just so much higher compared to traditional databases like Cassandra.
It's also interesting to note how other companies solved it. Instagram used a fanout on write approach with Redis, later on Cassandra and eventually a flavor of Cassandra based on RocksDB. They managed to use a full fanout approach using a combination of great optimization, a relatively lower posting volume (compared to Twitter at least) and a ton of VC money.
Friendster and Hyves are two stories of companies that didn't really manage to solve this and went out of business. (there were probably other factors as well, but still.) I also heard one investor mention how Tumblr struggled with technical debt related to their feed. A more recent example is Vero that basically collapsed under scaling issues.