Abschlussarbeiten

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:

Georg Voronin
E-mail: georg.voroninuuni-potsdampde

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.

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 prompt marginalized groups to transition away from mainstream public spaces and form counterpublics, where they find support, challenge dominant narratives, and mobilize social activism. While passive engagement in the public sphere may yield negative psychological outcomes, active participation in counterpublics is often associated with more positive effects. The spiral of silence theory further explains how dissenting voices may be muted in hostile environments, and recent models of active versus passive social media use (Verduyn, Gugushvili, & Kross, 2022) underscore the complex interplay between online engagement and well-being.

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 discrimination dynamics in online environments.

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.  For further information, please contact:

Georg Voronin
E-mail: georg.voroninuuni-potsdampde

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.

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.

Research has shown that social comparisons on social media—when individuals compare themselves with peers or idealized images—can evoke envy and reduce self-esteem (Krasnova et al., 2015; Vogel et al., 2014; Chou & Edge, 2012). However, while these effects are well-documented for real people, it remains unclear whether exposure to highly polished AI digital personas—such as Lil Miquela or Fit Aitana—produces similar outcomes. These AI representations, despite their human-like presentations, lack genuine personal experiences, raising questions about whether the absence of authenticity moderates the negative impact typically observed in social comparisons. This study will explore the psychological mechanisms underlying social comparisons with AI digital personas and their effects on users’ overall well-being.

Possible Methodology:

• Controlled Laboratory Experiments: Quantitatively assess levels of envy, negative affect, and self-esteem following exposure to AI digital persona content.
• Experience Sampling: Capture real-time data on users’ emotional and cognitive responses to AI digital persona content in everyday environments.

Note: Other methodological approaches may also be employed depending on the research focus.

Research Questions:

1. How does exposure to highly polished AI digital persona content affect users’ well-being, particularly in terms of envy, negative affect, and self-esteem?
2. Are the effects of social comparisons with AI digital personas comparable to those experienced when comparing with real peers or idealized human images?
3. What psychological mechanisms underlie the adverse well-being outcomes associated with social comparisons in the context of AI digital personas?

Candidate Requirements & Contact:
Candidates with expertise in experimental design, quantitative data analysis (using ESM, SPSS, or R), and qualitative research methods are encouraged to apply. For further details or to discuss your research proposal, please contact:

Georg Voronin
E-mail: georg.voroninuuni-potsdampde

Selected References:

• Krasnova, H., Widjaja, T., Buxmann, P., Wenninger, H., & Benbasat, I. (2015). Research Note—Why Following Friends Can Hurt You: An Exploratory Investigation of the Effects of Envy on Social Networking Sites among College-Age Users. Information Systems Research, 26(3), 585–605.
• Vogel, E. A., Rose, J. P., Roberts, L. R., & Eckles, K. (2014). Social Comparison, Social Media, and Self-Esteem.Psychology of Popular Media Culture, 3(4), 206–222.
• Chou, H.-T. G., & Edge, N. (2012). “They Are Happier and Having Better Lives than I Am”: The Impact of Using Facebook on Perceptions of Others’ Lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117–121.
• Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook Use, Envy, and Depression: A Comparison between Heavy and Light Social Media Users. Computers in Human Behavior, 43, 139–146.
• Steers, M.-L. N., Wickham, R. E., & Acitelli, L. K. (2014). Seeing Everyone Else’s Highlight Reels: How Facebook Usage is Linked to Depressive Symptoms. Journal of Social and Clinical Psychology, 33(8), 701–731.
• Appel, H., Crusius, J., & Gerlach, A. L. (2016). Social Comparison, Envy, and Depression on Facebook: A Study Looking at the Effects of High Comparison Standards on Depressive Symptoms. Cyberpsychology, Behavior, and Social Networking, 19(2), 83–88.
• Fardouly, J., Diedrichs, P. C., Vartanian, L. R., & Halliwell, E. (2015). Social Comparisons on Social Media: The Impact of Facebook on Young Women’s Body Image Concerns and Mood. Body Image, 13, 38–45.

Description:

In recent years, the widespread adoption of machine learning (ML) and artificial intelligence (AI) technologies has revolutionized various industries, from business to healthcare. However, a critical limitation inherent in many AI models is their reliance on associative relationships rather than causal ones (Pearl 2018). This raises concerns regarding the potential for these models to make misjudgments and yield unintended consequences, particularly in scenarios where causal understanding is important.

This thesis seeks to explore the hidden costs of AI by investigating the implications of relying on associative relationships in AI models. Some even propose, that AI is not able to learn anything at all (Bishop 2021). The central hypothesis is that the failure to uncover causal relationships may lead to inefficient decisions and negative outcomes, posing risks for businesses or social applications such as digital health.

The study will evaluate these hidden costs by replicating previous machine learning applications and reevaluating them using causal models, investigating an economic or societal impact of using AI.

By highlighting the importance of causal inference in AI models, this thesis aims to motivate the development of more “causable” (Chou et al. 2022) AI systems, thereby ensuring their effective deployment.

Requirements & Contact:

For this thesis you should be interested in (critical) perspectives on artificial intelligence and have previous experiences in data science projects.

If you would like to apply for this thesis, please contact Kai Schewina.

References

Bishop, J. M. (2021). Artificial intelligence is stupid and causal reasoning will not fix it. Frontiers in Psychology, 11, 2603.

Chou, Y. L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022). Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications. Information Fusion, 81, 59-83.

Pearl, J. (2018). Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016.

Description

The rise of social media has transformed not only how individuals communicate but also how public administrations interact with citizens. Platforms like X (formerly Twitter), Instagram and TikTok are no longer mere publicity tools, they are spaces where public trust is shaped, administrative services are offered, and new forms of citizen-state interaction emerge (Song & Lee, 2016). Despite growing citizen satisfaction with local services, trust in government institutions has been declining in Germany and other democracies (Bertelsmann Stiftung, 2017; dbb, 2023). Social media offers opportunities to counter this erosion of trust by promoting transparency, inclusivity, and co-production (Scupola & Mergel, 2022). However, using social media in public organizations also creates tension: the fast-paced, emotional, and algorithm-driven logic of platforms contrasts with bureaucratic norms of formality, legality, and neutrality (Meijer & Torenvlied, 2016). One possible angle for this thesis could be to investigate which factors influence social media use in public organizations and how this affects citizen trust and perceived legitimacy from a Technology-Organization-Environment (TOE) framework (Baker, 2011) and/or the Technology Enactment perspective (Cordella & Iannacci, 2010).

The thesis may take one of the following perspectives (or even take another perspective of your choice):

Interaction quality perspective: How do technological, organizational, and contextual factors influence the way social media is used by government organizations to communicate with citizens and how does that shape interaction quality?

Trust and transparency perspective: How does the technology enactment of public organizations on social media shape public perceptions of trust and transparency?

Network perspective: How can social media interactions between agencies, platforms, and citizens be mapped and analyzed as digital governance networks? What does that tell us about explicit and implicit associations between these actors?

Depending on your interests, the thesis can focus on a specific domain (e.g., police, education, social services) or a specific level of government (e.g., municipal communication). Methodologically, the thesis can apply social network analysis, surveys or digital trace data (e.g., scraped posts, likes, retweets). A mixed-methods approach is also possible.

Requirements & Contact

Interest and previous experiences with the research topic as well as familiarity with any appropriate methods and context are beneficial but not mandatory. If you would like to apply for this thesis, please contact Kai Schewina.

References

Alsehani, F. N., Wahab, A. W. B. A., & Shuib, L. (2024). Factors Influencing Social Media Adoption in Government Organizations: A TOE Framework and PLS-SEM Model Analysis. IEEE Access.

Baker, J. (2011). The technology–organization–environment framework. In Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1, 231–245.

Bertelsmann Stiftung. (2017). Schwindendes Vertrauen in Politik und Parteien. Eine Gefahr für den gesellschaftlichen Zusammenhalt? Gütersloh. Abgerufen unter: https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/schwindendes-vertrauen-in-politik-und-parteien

Cordella, A., & Iannacci, F. (2010). Information systems in the public sector: The e-Government enactment framework. The Journal of Strategic Information Systems, 19(1), 52–66.

dbb. (2023). dbb Bürgerbefragung Öffentlicher Dienst 2023. Der öffentliche Dienst aus Sicht der Bevölkerung. Berlin. Abgerufen unter: www.dbb.de/fileadmin/user_upload/globale_elemente/pdfs/2023/230815_dbb_Buergerbefragung_2023_final.pdf

Feeney, M. K., & Porumbescu, G. (2021). The limits of social media for public administration research and practice. Public Administration Review, 81(4), 787–792.

Meijer, A. J., & Torenvlied, R. (2016). Social media and the new organization of government communications: An empirical analysis of Twitter usage by the Dutch police. The American review of public administration, 46(2), 143-161.

Scupola, A., & Mergel, I. (2022). Co-production in digital transformation of public administration and public value creation: The case of Denmark. Government Information Quarterly, 39(1), 101650.

Song, C., & Lee, J. (2016). Citizens’ use of social media in government, perceived transparency, and trust in government. Public Performance & Management Review, 39(2), 430–453.