272
edits
Changes
Jump to navigation
Jump to search
Christy Warden (Social Media) (view source)
Revision as of 17:43, 25 October 2016
, 17:43, 25 October 2016no edit summary
Today I created a plan to experiment with the crawler which is explained on [[Christy Warden (Twitter Crawler Application 1)]]
I researched and followed accounts recommended by the crawler and plan to check back on Tuesday to see if they follow back. After I do this for a few times, I will be able to see how to adjust my criteria for choosing someone to follow and plan on automating the system. The end goal would be for me to run a large program every time I come to work, which unfollows people we followed who didn't follow back, follows new people based on the algorithms that I am testing. I am spending time today figuring out how to automate the follow/unfollow process so that this can be achieved quickly once I get some results from this initial follow spree.
10/25/16
Today I came back to discover that only 2 people in approximately 30 followed us back after my last week follow spree. I wrote a program which unfollows all the people who didn't follow us back so that we don't wrack up huge numbers of following. I am considering that maybe it would be better to target people who don't have a high number of followers or whose follower/following ratio is very low. I incorporated these components into my algorithm, but I am not certain that I have discovered the optimal balance for the total score of the potential follower. I used a tactic which incorporated this score concept this week. Additionally, I automated this process so that people who achieve a threshold score are automatically followed by the program, which significantly improved my efficiency in following. Because of this, I was able to follow around 70 people this week which should provide us with more data for Thursday when I check the result of this experiment. I plan on asking some of the Stat/ math interns for help with calculating the significance of the scores so that I can figure out which score make-up best correlates with the probability of a follow back.
Something interesting that I noticed was that when I was following large numbers of people today, we gained about 4 followers. I assume those accounts are also operating on a crawler of some kind and noticed us following mutual accounts? I wonder if we could explore this as a strategy in and of itself, gaining followers by following huge batches of people that are tracked by other crawlers? I am not sure this would be efficient, however, because I am assuming those crawlers also unfollow people who fail to respond to them which defeats the purpose.
Another thought, the only way that we will ever break out of the "follow someone and hope for a follow" 1:1 ratio is if the followers that we are gaining are people who will retweet us and interact with our content. That way they will garner attention for us in their own audience of followers and we will gain unrequited followers. So even though we only gained 2 followers out of the 30 I followed last time, they actually seemed like quality accounts and one of them even retweeted us. I think the long process of seeking accounts carefully rather than just following mass numbers of people will ultimately build an active follower base. Thus, we won't become one of those accounts with like 73k followers that get 1 favorite and 0 retweets on the vast majority of their content.
We need to start considering automated interactions with the accounts that follow us or that we follow (like maybe auto-favoriting a few of their tweets or having someone draft a DM for the people we are trying to win over?). I am definitely not the best person to come up with a framework for this, however so I would need to talk to Ramee/ Anne/ any of the social sciences interns about some possible approaches.
<!-- null edit dummy -->[[Category:McNair Staff]]
