Can Python Help Elon Find Twitter Bots and Streamline Acquisition?

Musk has ramped up his battle over Twitter bots recently, wobbling US$44 billion purchase of Twitter

Elon Musk's proposed US$44 billion purchase of Twitter appears to be wobbling, with the billionaire raising concerns about the number of inauthentic accounts, or bots, on the social media platform. Musk said his deal to buy Twitter couldn't move forward unless it provides public proof that less than 5% of its accounts are fake or spam or is controlled by Twitter bots, as the company reported in a May 2 regulatory filing. Musk has ramped up his battle over Twitter bots recently, calling on the U.S. Securities and Exchange Commission to investigate Twitter's claims and asking his 93 million followers for feedback on their experience using the platform.

Twitter did ban thousands of Twitter bots that violated Twitter's policies. Although, the booming business of selling automated bot accounts has only increased the number of bot accounts. The banned accounts were soon replaced by millions of new bot accounts.

The bot accounts can tweet content, interact with users, follow users, or retweet content associated with a specific account or hashtag. It is estimated that over 15% of accounts on Twitter are automated bot accounts.

What are Twitter Bots?

Twitter bots is a bot software that uses the Twitter API to interact and engage with Twitter users. The Twitter bots can be programmed or automated to perform a specific task or series of tasks. It can autonomously tweet, retweet, like, follow, unfollow, or DM other accounts. A customer support chatbot is a prime example of a Twitter bot. It can help improve the overall customer support experience by improving the response time. Although these Twitter bots do not pretend to be real people. These bots are set with clear expectations to help them point to the logical steps or call for action.

Why is it Relevant to Detect Bots?

It is relevant to detect bots because you need to know what the bots are doing, which in turn will tell you how the sentiment of a particular stock on Twitter is being manipulated. When the Twitter sentiment of a particular stock is calculated, the tweets made by bot users are identified and removed. This gives the true sentiment sans manipulation. This true sentiment can be a very powerful metric, when used with other technical indicators, to call the tops and bottoms of a trend.

Can Python help in detecting Twitter bots?

In python, a library called botometer is used to know if a particular tweet was made by a bot or not. The botometer library uses a machine-learning algorithm trained on tens of thousands of labeled data. This algorithm's output is a probability on a scale of 0 to 1, where 1 indicates that a Twitter account is managed by a bot.

The Botometer API takes the user id as the input and then extracts 1200 features related to that used to compute a score. The Botometer gives separate scores for the following categories:

Network features

Network features of a user include information on the retweets, mentions, and hashtags that a user tweeted in the past. For example, If the user is retweeting only those tweets made on a particular handle then the user is most likely a bot.

User features

This contains user-specific information such as the user's name, language, location, account created date, etc. Generally, bots do not contain such information. And if they do, it will be something gibberish.

Temporal features

The category, temporal features analyze the tweet rate, timing patterns of tweeting and retweeting, etc. For example, if the account tweets at the same time intervals, then it is most likely a bot.

And other features are network features, content features, and sentiment features.

So, in this way, Python might help Elon detect Twitter bots and streamline acquisition.