Some reddit users just love disagreeing, finding new AI-operated troll-spotting algorithms

In today’s fragmented online scenario, it is more difficult to identify harmful actors such as trolls and misinformation.

Often, efforts to present malicious accounts they focus on analyzing what they say. However, our latest research shows that we should pay more attention to it to do-And how they do.

We have developed a way to identify potentially harmful online actors, which is entirely based on their behavior pattern – the way they interact with others – rather than the content they shared by them. We Presented our results recently ACM web conferenceAnd was awarded the best paper.

What people call, beyond seeing it

Traditional approaches to spot problematic online behavior usually depend on two methods. To check a material (what people are saying). To analyze others Network connection (Who follows whom).

These methods have limitations.

Users can crack content analysis. They can code their language carefully, or share misleading information without using clear trigger words.

Network analysis is low on platforms like redditHere, the relationship between users is not clear. Commits are held around subjects rather than social relations.

We wanted to find a way to identify harmful actors, which could not be easily prepared. We felt that we can focus on behavior – how people interact, instead of what they say.

To teach AI to understand human behavior online

Our approach uses a technique called Inverted reinforcement learningThis is a method that is commonly used to understand human decision making in areas such as autonomous driving or game theory.

We adapted to analyze the technique how users behave on social media platforms.

The system works by observing the user’s functions, such as making new threads, posting comments and answering others. With the tasks it infects the underlying strategy or “policy” that drives their behavior.

In our Reddit case study, we analyzed 5.9 million interactions in six years. We identified five different behavior individuals, including a particularly notable group – “disagreements”.

Meet ‘disagreements’

Perhaps our most striking result was looking for a whole class of Reddit users, whose primary purpose seems to disagree with others. These users seek opportunities to post particularly contradictory comments, especially in response to disagreement, and then proceeded without waiting for the answer.

“Disagreement” was the most common in politically centered subredits R/News, R/WorldnewsAnd R/politicsInterestingly, they were now very few in the banned Pro-Trump Forum r/the_donald Despite its political attention.

This pattern shows how behavior analysis can highlight dynamics that can miss material analysis. In R/The_Donald, users tried to agree with each other, directing the enmity towards external goals. It can explain dynamic why traditional material is moderation Struggling To solve problems in such communities.

Football fan and gamers

Our research also revealed unexpected relations. Users who discuss completely different subjects sometimes display equally similar behavior patterns.

We found striking similarities among users discussing football (at) R/Football) And e-sport (at) R/Legoflegands,

This equality emerges from the fundamental nature of both communities. Sockers and e-Sports fans attach to a parallel manner: they support passionately specific teams, follow matches with intensive interest, participate in warm discussions about strategies and players’ performance, celebrate victory, and disburse necklace.

Both communities promote strong tribal identity. Users protect their favorite teams by criticizing rivals.

Whether the Premier League strategy or the League of Legends Champion, the underlying interaction pattern – the time of reactions, sequence and emotional tone – consistent in these top specific communities.

This challenges traditional knowledge about online polarization. While Eco Chambers are often blamed for increasing division, our research suggests that behavioral patterns can cross topical boundaries. Users can be divided more by how they discuss.

Beyond troll detection

The implications of this research expand well beyond the educational interest. Platform moderators can use behavioral patterns to identify potentially problematic users, before they post large versions of harmful materials.

Unlike material moderation, behavior analysis does not depend on understanding the language. This is difficult, because changing one’s behavior pattern requires more effort than adjusting the language.

The approach can also help designing more effective strategies to combat misinformation. Instead of fully focusing on the material, we can design systems that encourage more creative engagement patterns.

For social media users, this research provides a reminder of how we attach online – not only what we say – look at our digital identity and affect others.

Since online spaces continue to struggle with manipulation, harassment and polarization, the approaches that consider behavioral patterns with material analysis can provide a more effective solution to promote healthy online communities.

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