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A recent study provides a nuanced understanding of the mechanisms driving polarization and issue alignment on Twitter/X and reveals how political polarization in Germany is reinforced and structured by two distinct types of highly active users: influencers and multipliers.

The work is based on a large-scale analysis of more than 19 million tweets covering daily trending topics in Germany between 2021 and 2023. The results show a strikingly clear structural division of the German Twittersphere into two ideological camps—one predominantly left-liberal, the other right-conservative.

While polarization is not a novel finding in social media research, the authors could identify strong issue alignment across diverse political topics. This means that users consistently position themselves in the same ideological cluster across different issues such as climate change, COVID-19, energy, migration, or media trust.

This cross-issue alignment contrasts with previous survey-based research, which typically finds only weak or issue-specific polarization in public opinion. The paper is in the journal Proceedings of the International AAAI Conference on Web and Social Media.

Key actors: Influencers and multipliers

At the center of this alignment phenomenon are two types of highly active users: influencers, who generate widely shared, ideologically charged content, and multipliers, who primarily act as curators by retweeting content that matches their ideological stance. While influencers resemble traditional opinion leaders such as politicians or media figures, multipliers are less visible yet arguably more influential in shaping the structure of online discourse.

The analysis shows that multipliers play a decisive role since their intensive retweet behavior amplifies and bundles ideologically consistent content, thereby facilitating polarized and aligned opinion clusters.

Mathematical approach

To uncover these patterns, the study combined advanced computational methods. A machine-learning-based topic modeling approach inductively classified millions of tweets into thematic categories across thousands of trending topics, creating a high-dimensional map of public discourse. Retweet activity was then modeled as directed network, with links representing retweets between users.

Applying stochastic block modeling—a statistical method for detecting community structure in networks—the researchers identified, for each trend, whether the retweet network is polarized or not, and extracted the clusters that correspond to opposing ideological camps. Finally, an alignment metric was developed to measure how consistently individual users stayed within the same ideological block across topics.

Multipliers—whose authenticity is also supported by the study—consistently showed higher alignment scores than influencers, indicating their role in binding issues together.

Issue alignment across topics

Polarization is not limited to a few "hot-button" issues but spans multiple political fields simultaneously. The analysis of the top 1,000 influencers and multipliers reveals that multipliers are more active and maintain stronger ideological alignment across topics than influencers. While most political issues are highly aligned, non-political topics such as gaming and music attract different user groups and remain weakly polarized.

A similar realignment effect appeared for Ukraine-related discussions, where some right-leaning influencers broke with their cluster's dominant pro-Russian stance—a pattern again less visible among multipliers.

Across all topics, multipliers consistently show higher issue alignment than influencers. The topic alignment matrix shows a high issue alignment across topics, except for Music and Gaming, with a gradual difference between strongly aligned issues like COVID, Journalism, and German politics, and less aligned topics like Social politics.

User activity patterns show that multipliers are typically active in a larger number of trends than influencers. A comparison of the size of political clusters highlights further differences. Among the 1,000 most retweeted influencers, we observe a majority of accounts belonging to the left-liberal cluster. This is reversed for multipliers: among the 1,000 most active multipliers, the team observed a majority of users from the right-conservative cluster.

The authors highlight the importance of considering the role of social media in shaping public opinion and the need for further research into the mechanisms driving polarization. The study has limitations that call for further research, such as the need to shed light on the discursive reasons of issue alignment, which the authors plan on investigating through the lens of conflicting narratives.

Further research is needed to explore the alignment of regular users and to determine if similar patterns of polarization exist on other social media platforms. Preliminary findings suggest that consistent polarization and issue alignment may hold for the majority of users, and that influential hyper-active users on other platforms, such as Facebook, may have similar effects as multipliers on Twitter.