Bot or Human?: The Crucial Task of Bot Detection in Social Listening

One of the key challenges in the field of social listening is identifying and filtering out bots, a task that relies on several methods working in unison. Bot detection methods are essential for social listening tools to ensure that the data collected from social media platforms is accurate and representative of genuine human interactions. Our social media sentiment tool, PUMP, is one of the very few social listening products with a comprehensive and advanced bot detection mechanism. In this article, we will explore some of the most commonly used bot detection approaches for the purpose of social sentiment data purification.

Account Age Analysis

One of the simplest ways to detect bots is by looking at the age of the account. Bots are often created in large batches and have relatively recent creation dates. Therefore, accounts that are very new may be flagged for further investigation. This method is one of the most basic in the arsenal of a bot hunter. Naturally, it is so trivial that it only forms one small part of the overall bot detection framework.

Activity Patterns

Bots tend to follow predictable patterns of activity, such as posting at regular intervals or posting similar content. There are algorithms that can analyze these patterns and flag accounts that exhibit suspicious behavior. For example, if an account posts dozens of tweets in a matter of minutes, it is likely a bot. Similarly, if an account is posting identical content across multiple social media platforms, again, it is likely a bot.

There are also other activity pattern giveaways, e.g., speed of posting. Bots can post, comment, and navigate social media platforms at speeds that are unattainable for even the sharpest human with the best internet connection.

They might also be incredibly adept and fast at reciprocating, or to put it in more simple terms “liking back”. If you are surprised that a particular account likes your posts at all times of the day seconds after you have liked their content, you might be exchanging niceties with a bot.

Follower-to-Following Ratio

Bots often have a high follower-to-following ratio because they follow many accounts but have few followers. This ratio is often one of the most telling signs of a social media bot. For example, an account that follows 10,000 users but has only 100 followers is often a bot. Of course, there is always a possibility that some human users might have such skewed follower-to-following ratios. The ratio is only one evaluation factor when it comes to bot detection. ?

Content Analysis

Bots often post similar or irrelevant content which can be detected through content analysis. Social listening tools can use natural language processing (NLP) algorithms to analyze the content of posts and identify patterns that suggest the account is a bot. For example, if an account is posting repetitive messages or spammy content, it is likely a bot.

Network Analysis

Bots often operate in networks, with many bots working together to amplify certain messages. Network analysis is used to identify clusters of accounts that may be bots. For example, if several accounts are posting identical or very similar content at the same time, it is likely they are part of a bot network. Similar to many criminals, bots love ganging up together to ply their trade.

Machine learning algorithms

Machine learning algorithms can be trained to detect bots based on historical data analysis. Social listening tools can use these algorithms to learn from past bot behavior and identify new bots based on similar patterns. For example, if a new account exhibits similar activity patterns to previously identified bots, it might be a bot.

The five approaches above form the basic foundation of bot detection on social media. Naturally, no single bot detection method is foolproof, and a combination of methods is typically necessary for effective bot detection. Additionally, as bots become more sophisticated, bot detection methods must continually evolve and adapt. This is a truly never-ending battle!

At ZENPULSAR, we are at the forefront of bot detection to bring you the most relevant and high-quality sentiment data. When you use social media sentiment to support your trading and investment decisions, you wouldn’t want to rely on signals generated by bots.