Mobilizing Inequalities: From Grievances to Conflict

  • Department of Linguistics
  Gremler, Frederik (2023): Ethnic Organizations Online

Ethnic Organizations Online


Digital media form an integral part of political actors' communication strategies. They leverage personal websites, Facebook pages, Twitter profiles, and Instagram accounts to disseminate information, communicate policy positions, and mobilize followers. Through digital media, politicians, political parties, and nongovernmental organizations alike are able to reach potentially massive audiences as nearly half the world's population is now connected to the Internet. Compared to other, more traditional media, digital media enable cost-effective, direct, two-way communication with diverse audiences. For political organizations that claim to represent specific ethnic groups, these information channels open up new opportunities and means to achieve their goals. Investigating their activities in the digital space constitutes the topic of this dissertation.

In the first paper (co-authored with Nils B. Weidmann), I present a new dataset for this purpose. It enables researchers to track the online activities of ethnic organizations. The Ethnic Organizations Online (EO2) database systematically captures Twitter, Facebook, and Instagram profiles as well as websites of political organizations with links to ethnic groups in 90 countries. I demonstrate the value of this dataset in three applications: First, I am able to show that separatist organizations are more likely to use Twitter than organizations without secessionist goals. Moreover, I find that organizations in autocracies invest fewer resources into their social media activity as elections approach. Finally, I compare organizations in power to those with opposition status: the former tend to communicate less about political phenomena and activities.

In the second paper (co-authored with Lea Haiges), I examine the content of political communication online, in particular how elections and party competition influence the use of ethnic identity appeals. The basis for this work is provided by hand-coding more than 9000 Facebook and Twitter posts. Based on this data, I train machine learning models that automatically detect identity appeals in over 2~000~000 million social media posts. Analyzing this data with regression models, I find the following: The closer an election, the higher the likelihood that an ethnic party will appeal to ethnic identities. In addition, I show that when more ethnic parties participate in a particular election, this results in a higher number of ethnic identity appeals. Both results provide evidence on axiomatic assumptions of theories of ethnic politics.

In the third paper, I turn to the effects of ethnic organizations' digital communication. I investigate whether individuals' who are exposed to references to ethnic identities online leads to increased identification with those very identities. To study this, I collect more than 200~000 Facebook comments authored in reply to 8000 Facebook posts of ethnic parties. I show that these parties face incentives to mention ethnic identities as this increases the reach of their posts. Their comment sections are more likely to feature comments with negative emotions, references to ethnic identities, and even toxic content. However, I find no evidence that these results extend to citizens' attitudes on the ground.

In summary, this dissertation offers important insights into the digital, political communication of ethnic organizations. It shows that these actors use social media strategically to achieve their goals -- although adoption of platforms has not been universal. However, when ethnic organizations take to social media the electoral context plays an important role. Moreover, ethnic organizations' digital communications carry wide-ranging implications in the digital space, as it can lead to more toxic language and negative comments. Although their offline impact remains unclear, the data collected in this dissertation provides a valuable starting point for further research.

Origin (projects)

Funding sources
Name Finanzierungstyp Kategorie Project no.
Sonstige DFG third-party funds research funding program 791/19
Further information
Period: 01.10.2019 – 31.03.2024