A single-character change in how tweets are distributed highlights a massive strategic decision for Twitter.
When I first started using Twitter, I’d start my tweets with the names of other users. I wanted to give them credit for their ideas. I later discovered very few people even saw my tweets because of how they were composed.
Bots have a bad reputation. Whether they’re sinister botnets, carrying out coordinated attacks, or annoying spambots, polluting our digital universe with crappy auto-generated content, bots are a much maligned bunch.
You might ask, if bots are bad and a huge portion of Twitter activity is already generated by bots, Why is Primal creating another one?
Small conversational data such as tweets or text messages are a goldmine of individual interests. Millions of people everyday tweet about their favourite food, send a Kik or Facebook message about a recent TV episode, or take a photo on Instagram while attending a sporting event or concert.
News alert! Your social network wants to get to know you. Unfortunately, identifying and understanding your interests based on 140 characters is pretty hard to do! That’s why marketers are busy trying everything to get to know you better.
In this post, we’ll demonstrate how Primal can be used to power social media analysis.