1. Introduction
8,11,15, Echo chamber detection in social networks refers to identifying and analyzing the formation and reinforcement of homogeneous groups in online communities, where individuals with similar beliefs and attitudes tend to congregate and reinforce each other’s views. This phenomenon is a concern as it can lead to the spread of misinformation and the reinforcement of harmful stereotypes and biases. Echo chambers are particularly likely in areas where right-wing populist content is disseminated via social media [ 1 2 ]. The awareness of echo chambers and their effects has increasing importance in democratic and free societies, as echo chambers can partly make the necessary political discourse dysfunctional [ 3 4 ] and can foster polarization [ 5 6 ] in open societies. Many studies indicate that echo chambers in social networks might affect democratic elections [ 7 9 ]. Particularly in the case of very close election outcomes, such as Brexit [ 10 12 ], these communication patterns can be decisive and exploited by non-democratically-legitimized actors (even foreign powers with manipulative tendencies) to pursue their interests, which benefit from divided societies or exploit them for their own purposes [ 13 ]. Kremlin-orchestrated troll accounts, in particular, have gained sad notoriety here, exploiting these mechanisms to shape opinion in democratic societies [ 14 16 ].
17, Thus, there are significant reasons for free societies to understand and manage these mechanisms so that free speech can continue to unfold its constructive power and not be abused to hatch “manipulative cuckoo eggs” of foreign powers or non-democratically-legitimized actors. In particular, the spread of disinformation works very well in echo chambers. To recognize echo chambers is, therefore, of considerable interest. However, there are several challenges in detecting echo chambers in social networks. One major challenge is the lack of clear definitions and criteria for identifying echo chambers and the difficulty in quantifying the extent to which an online community is homogeneous. Additionally, echo chambers can occur at different granular levels, such as individual users, groups, or entire platforms. The state-of-the-art methods for echo chamber detection in social networks involve using computational techniques to analyze the structure and content of online communities, such as network analysis, natural language processing, and machine learning. These methods have been applied to various platforms, such as Twitter, Facebook, and Reddit [ 5 18 ].
20,21, While content-based identification is more reliable, it is also more laborious and dependent on a large amount of labelled training data which often does not exist or has to be recorded and labelled elaborately. So, content-considering methods work very well for past events with sufficient data but have problems adapting to new circumstances (such as the Russia–Ukraine war). The automated detection of orchestrated troll accounts is especially difficult in the details and usually does not lead to reliable results [ 19 22 ].
This paper, therefore, investigates whether purely structural analysis of communication and interaction patterns can be used to identify echo chambers. Because social networks like Twitter provide samples of their data streams for free, the study was also interested in whether this could be done with a sample of network interactions. For example, the public Twitter event stream API provides a 1% sample of the actual event data stream. Furthermore, studies like [ 23 ] show that the provided samples are representative.
Research Question 1: Can echo chamber detection be accurately performed using graph-based analysis of a sample of network interactions without the need for additional information, such as the content of the interactions or characteristics of the individuals involved?
The object of investigation was the public sample of events of Twitter retweets (German language). Users can post short messages on Twitter, called “tweets”, including text, images, and links. These tweets can be seen by their followers.
A “status post” is a tweet that a user composes and shares on their own profile. It can include text, images, and links and can be seen by their followers. Status posts are the starting point of information distribution on social networks like Twitter. Their content can only be captured and processed using complex natural language processing (NLP) methods.
A “reply” is a tweet a user composes in response to another user’s tweet. When a user replies to a tweet, the original tweet is linked within the reply so that others can see the context of the conversation. Replies can also be seen by the followers of the user who wrote the original tweet. Replies can be confirming, questioning, contradicting, referring, and, of course, any other form. Consequently, these interactions also require complex NLP methods to classify the interaction’s character.
A “retweet” is when a user shares another user’s tweet on their profile. Retweets allow users to share content from other users with their followers. The analytical advantage of retweets is that content is shared without additional remarks or annotations. Although this cannot be said with certainty, it is predominantly safe to assume that a retweeter will have no significant issues with the original opinion of a tweet. Due to the accumulation of retweet interactions between the same accounts, it can be assumed that the content of these accounts is close to each other without having to analyze the actual content.
A “quote” is similar to a retweet, but instead of simply sharing the original tweet, the user includes it as a quote in their tweet, along with their commentary. This allows users to share and comment on tweets in a way that allows the context of the original tweet to remain visible. Unlike a retweet, the original content is accompanied by comments that can change the meaning of the original tweet from case to case. This possible change in meaning can be sarcasm, corrections, annotations, etc., which usually require complex content-based analysis using NLP methods.
Retweets in particular are a powerful tool for identifying communities on Twitter because they indicate that a user is interested in and endorsing the content of another user’s tweet. When users retweet a tweet, they share it with their followers and endorse it as something they find valuable or interesting. Analyzing retweeting patterns among users makes it possible to identify groups of users with similar interests and share similar content. Retweets can also be used to identify the most influential members of a community. Users frequently retweeted by others are likely to be seen as leaders or experts within a community, and their tweets are likely to be more widely seen and shared. Identifying these influential users is possibly better for understanding a particular community’s dynamics and interests.
Additionally, retweets can be used to trace the spread of information and ideas within a community. When a tweet is retweeted, it is exposed to a new group of users who may be interested in the same topic. Analyzing retweet patterns makes it possible to see how information and ideas spread within a community and how different groups of users influence the conversation. In summary, focusing on retweets is a viable approach to detecting communities on Twitter because it allows the identification of groups of users with similar interests and the detection of influential members, and traces the spread of information and ideas within a community. Therefore, this study focuses on retweet interactions because of the analysis’s simplicity. However, this leads to a second research question: