Theses

In today’s digitally mediated society, social media platforms are arenas where stereotypes, prejudice, and discrimination are both reproduced and reconfigured. Digital features—such as anonymity, publicized privacy, and context collapse—often expose users to discriminatory content in the public sphere. This exposure can heighten feelings of vulnerability, prompting marginalized groups to leave mainstream public spaces in favor of forming counterpublics. Within these niche environments, users find support, challenge dominant narratives, and mobilize social activism. While passive engagement in the public sphere is associated with negative psychological outcomes, active participation in counterpublics tends to foster positive well-being. The spiral of silence theory further elucidates how dissenting voices are muted in hostile environments, and recent models of active versus passive social media use (Verduyn, Gugushvili, & Kross, 2022) underscore the complex interplay between digital engagement and mental health.

This thesis proposes a data-driven investigation into how digital environments influence user well-being through patterns of social media engagement. Building on the extended active-passive model, the study hypothesizes that mainstream public spheres—characterized by exposure to discriminatory content and hostile reactions—encourage passive usage and lower well-being, whereas counterpublics provide a safer space that promotes active engagement and enhances well-being.

Research Questions & Hypotheses:

• RQ1: How does exposure to discriminatory content in the public sphere influence user engagement patterns on social media?
• RQ2: Do users in counterpublics exhibit higher levels of active engagement and report better well-being compared to those in mainstream public spheres?
• H1: Users frequently exposed to discriminatory content in mainstream public spheres will show increased passive engagement behaviors and report lower well-being.
• H2: Users active in counterpublics will demonstrate higher active engagement metrics and report enhanced well-being.

Possible Methodology:

• Data Collection:

  • Employ web scraping techniques (using APIs and custom scripts) to gather posts, comments, and interaction data from social media platforms such as Twitter and Reddit.
  • Categorize data into two groups: content originating from mainstream public spheres versus counterpublic spaces, based on established criteria from research on public spheres, counterpublics, and context collapse.

• Data Analysis:

  • Content Analysis: Use natural language processing (NLP) methods to detect discriminatory language and measure sentiment in posts.
  • Engagement Metrics: Classify user behavior as active (e.g., posting, commenting, sharing) or passive (e.g., lurking, minimal interaction).
  • Statistical Testing: Apply regression analysis and other statistical techniques to correlate exposure to discriminatory content, engagement type, and self-reported well-being (integrating survey data where available).
  • Validation: Triangulate findings by comparing scraped behavioral data with user self-reports and established theoretical models such as the extended active-passive model (Verduyn, Gugushvili, & Kross, 2022).

Candidate Requirements & Contact:
We invite candidates with strong methodological skills in data collection and literature synthesis, familiarity with digital media research, and a passion for exploring social inequality and mental health in online environments. For further information or to discuss your research proposal, please contact:

Potential supervisor:

Georg Voronin

Selected References:

• Allport, G. S. (1954). The Nature of Prejudice. Addison-Wesley.
• Habermas, J. (1989). The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society. MIT Press.
• Fraser, N. (1990). Rethinking the Public Sphere: A Contribution to the Critique of Actually Existing Democracy. Social Text, 25/26, 56–80.
• Noelle-Neumann, E. (1993). The Spiral of Silence: Public Opinion – Our Social Skin. University of Chicago Press.
• Verduyn, P., Gugushvili, N., & Kross, E. (2022). Do social networking sites influence well-being? The extended active-passive model. Current Directions in Psychological Science, 31(1), 62–68.

The proliferation of fake news and hate speech on social media has profound implications for societal cohesion and democratic processes. Unlike misleading information intended merely to confuse, a subset of disinformation is deliberately crafted to incite hatred toward specific social groups. This project aims to systematically review the interdisciplinary literature on hate-inducing disinformation.

Methodology:
The project will undertake a systematic literature review in accordance with established guidelines (e.g., PRISMA). It will synthesize findings from diverse fields—including media studies, political communication, and computational social science—to map out the mechanisms and consequences of hate-inducing disinformation. Special emphasis will be placed on content analysis methodologies to understand both the construction and propagation of hateful narratives.

Candidate Requirements & Contact:
Candidates with a strong research background in media studies, digital politics, or computational social science—particularly those with expertise in content analysis and systematic review methodologies—are invited to apply. 

Potential supervisor:
Georg Voronin

Selected References:

• Lazer, D., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. (2018). The science of fake news. Science, 359(6380), 1094–1096.
• Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.
• Tandoc, E. C., Lim, Z. W., & Ling, R. (2018). Defining “fake news”. Digital Journalism, 6(2), 137–153.
• Marwick, A., & Lewis, R. (2017). Media manipulation and disinformation online. Data & Society Research Institute.
• Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236.
• Benkler, Y., Faris, R., & Roberts, H. (2018). Network Propaganda: Manipulation, Disinformation, and Radicalization in American Politics. Oxford University Press.

User-generated content lies at the heart of social networking sites (SNSs) and serves as a crucial pillar political discourse. While traditional research on self-disclosure has primarily focused on the privacy calculus, little is known about the broader socio-political aspects—especially when it comes to political content and activism. This project aims to bridge this gap by examining how users weigh perceived benefits and costs when engaging in political self-disclosure online. In addition to exploring the dynamics of public spheres, counterpublics, and the spiral of silence, this research will illuminate how these factors shape political activism and online engagement.

Core Research Questions:

• What factors underpin the socio-political calculus of political self-disclosure on SNSs?
• How do perceived benefits, costs, and cost-mitigating factors influence users’ willingness to engage in political discourse?
• In what ways do public spheres, counterpublics, and the spiral of silence interact to shape political activism online?

Methodology:
This research will adopt a systematic literature review framework following PRISMA guidelines. It will synthesize interdisciplinary studies from communication, sociology, and digital media research to critically evaluate theoretical and empirical insights on political self-disclosure.

Candidate Requirements & Contact:
We invite candidates with strong methodological skills in literature synthesis, familiarity with digital media research, and a passion for exploring social inequality. 

Potential supervisor:
Georg Voronin

Selected References:

• Abramova, O., Wagner, A., Krasnova, H., & Buxmann, P. (2017). Understanding self-disclosure on social networking sites-a literature review.
• Krasnova, H., Günther, O., Spiekermann, S., & Koroleva, K. (2009). Privacy concerns and identity in online social networks. Identity in the Information Society, 2, 39-63.
• Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online social networks: Why we disclose. Journal of information technology, 25(2), 109-125.
• Wagner, A., Krasnova, H., Abramova, O., Buxmann, P., & Benbasat, I. (2018). From˜ Privacy Calculus™ to˜ Social Calculus™: Understanding self-disclosure on social networking sites.
• Habermas, J. (1989). The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society. MIT Press.
• Fraser, N. (1990). Rethinking the Public Sphere: A Contribution to the Critique of Actually Existing Democracy. Social Text, 25/26, 56–80.
• Noelle-Neumann, E. (1993). The Spiral of Silence: Public Opinion – Our Social Skin. University of Chicago Press.

As AI entities become increasingly integrated into everyday digital life, the nature of human-like interactions with these systems has emerged as a critical area of study. This literature review will synthesize a broad spectrum of research addressing diverse relational dynamics with AI, including teams at work, friendships, parasocial one-sided engagements, romantic relationships, and intergroup contact. By mapping theoretical frameworks and empirical findings from media psychology, social psychology, and human-computer interaction, the review will identify both commonalities and divergences in how these interactions are conceptualized and measured. 

Methodology:
The project will undertake a systematic literature review in accordance with established guidelines (e.g., PRISMA). It will synthesize findings from diverse fields—including media studies, political communication, and computational social science—to map out the mechanisms and consequences of hate-inducing disinformation. Special emphasis will be placed on content analysis methodologies to understand both the relationship between humans and AI agents.

Candidate Requirements & Contact:
Candidates with a strong research background in media studies, digital politics, or computational social science—particularly those with expertise in content analysis and systematic review methodologies—are invited to apply. For further information, please contact:

Georg Voronin
E-mail: georg.voroninuuni-potsdampde

Selected References: 

• Guerrero, L. K., Andersen, P. A., & Afifi, W. A. (2017). Close Encounters: Communication in Relationships (6th ed.). Sage Publications.
• Waytz, A., Cacioppo, J. T., & Epley, N. (2010). Who sees human? The stability and importance of individual differences in anthropomorphism. Journal of Personality and Social Psychology, 98(2), 281–294.
• Luger, E., & Sellen, A. (2016). “Like Having a Really Bad PA”: The Gulf Between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5286–5297). ACM.
• Breazeal, C. (2003). Toward sociable robots. Robotics and Autonomous Systems, 42(3–4), 167–175.
• Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3–4), 143–166.

In the evolving landscape of social media, the emergence of virtual and AI-driven influencers marks a significant paradigm shift, challenging traditional notions of interaction and influence. This research aims to delve into three critical aspects of this phenomenon: the nature of interactions with AI influencers, the implications of these entities on users' social media well-being, and the dynamics of para-social relationships formed with virtual/AI entities.

Topics may include, but are not limited to, the following

• Social Media Intergroup Contact with Virtual/AI Influencers: The investigation centers on the application of intergroup contact theory to the domain of social media, particularly in interactions involving AI influencers. It seeks to understand the role of virtual or AI entities, designed to mimic human beings in social media, in influencing intergroup relations. The primary objective is to evaluate the potential of AI influencers in reducing prejudice under the framework of intergroup contact theory.
• Well-being in para-social relationsships Social Media with Virtual/AI Influencers: The use of social media can have both positive and negative effects on the well-being of its users. In recent years, the emergence of virtual/AI influencers, who are increasingly indistinguishable from real people on social media platforms, has introduced new dynamics in digital interactions. This study aims to explore the effects of these virtual/AI influencers on the well-being of social media users, examining how their presence may impact mental health, self-perception, and overall well-being in and beyond the digital sphere.
• Para-social Relationships with Virtual/AI Entities: AI entities such as Snapchat's My AI, ReplikaAI and Celebrity AI are designed to form intimate bonds with real people, often targeting areas such as both romantic and friendship bonds while monetizing digital loneliness. The study of this phenomenon addresses the nature of the parasocial relationships between users and these AI agents and aims to understand the psychological dimensions, the impact on users and the wider implications for future human-computer interactions.

These studies represent a comprehensive effort to understand the multifaceted effects of virtual and artificial agents through social media. By exploring these diverse areas, the research aims to provide valuable insights into the complex interplay between virtual agents, para-relationships, and individual well-being.

Requirements & Contact:

Students should have an interest in social media analytics, sociological theory, and mixed-methods research.

Further information, detailed references, and specific keywords related to each area of study can be provided upon request, ensuring a comprehensive and tailored understanding of these complex social media phenomena.

If you are interested on writing a thesis on Social Cohesion and Digital Democracy, please reach out to Georg Voronin.