SBOT Uncover

Boots makes up-to 60% of internet activities on the internet. For some work they are necessary, for some they are problems. Problems actually difficult to solve. Twitter is going through same problem (as a outside observer this is what i think). Forgot about someone want to buy or not, it’s very business model is at risk. What are the numbers of bots in Twitter? 5% , 10% , 20% or XX ? This creates confusion with advertisers who are the main source of income for the twitter. I believe Twitter has already publish how it counts bots in the system. Interesting reply was a poop emoji but let’s look at that thread.

Source: Twitter

While there is twitter way of counting a bots, I want to look into this problem as well and hopefully we can use if for other platform where bots exists. I am dividing this problem into 5 parts.

1. Finding/study bots.
2. Use machine Learning to identify bots.
3. Conclusion and Finding
4. Future of social media
5. …

As you can see, these are very big topics, if you want to join this journey and contribute. Please use this GitHub link: git@github.com:parlad/SBOT-Uncover.git. Follow me for more information of twitter at neupane parlad.

For the data, I am going to use Elon Must data from 3 months before he publish he is going to buy the Twitter.

Note: It’s unauthorized to reference this article without data. Also every research has limitation or area/topic which was ignored or not cover. It’s unauthorized to use it without mentioning this research limitation.

Let’s start by studying the user data.