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.

Intergroup contact theory, as pioneered by Allport (1954), has long served as a foundational framework for understanding how interactions between members of different groups can reduce prejudice and foster social cohesion. In today’s digital era, however, the emergence of social media as a central arena for interpersonal communication introduces new dimensions to this classic theory. Digital platforms not only facilitate rapid and widespread interactions but also introduce unique variables—such as anonymity, algorithmic content curation, and platform-specific affordances—that may transform traditional mechanisms of prejudice reduction. This thesis project aims to systematically review and synthesize empirical studies across disciplines (e.g., psychology, communication studies, and information systems) to assess social media intergroup contact.

Methodology:
Adopting a systematic literature review framework in accordance with the PRISMA protocol, this project will critically assess empirical studies from multiple disciplines. This approach aims to distill best practices and theoretical advancements, offering a comprehensive synthesis of current knowledge on intergroup contact in online environments.

Candidate Requirements & Contact:
Candidates with a strong interest in social psychology and digital communication research, as well as a solid grounding in systematic literature review methodologies, are encouraged to apply. For further information, please contact:

Georg Voronin
E-mail: georg.voroninuuni-potsdampde

Selected References:

• Allport, G. S. (1954). The Nature of Prejudice. Addison-Wesley.
• Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90(5), 751–783.
• Dovidio, J. F., Gaertner, S. L., & Kawakami, K. (2003). Intergroup contact: The past, present, and the future. Group Processes & Intergroup Relations, 6(1), 5–21.
• Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The Social Psychology of Intergroup Relations (pp. 33–47). Brooks/Cole.
• Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23(1), 3–43.
• Baym, N. K. (2015). Personal Connections in the Digital Age. Polity Press.

In today’s digital ecosystem, intimate conversational and romantic AI models—such as Replika, Snapchat’s My AI, Celebrity AI, and platforms like Fanvue—are enabling users to forge unique, one-sided bonds. These parasocial relationships can provide emotional support and validation while potentially contributing to loneliness, dependency, or a decline in offline social interactions. Recent technological advancements have rendered these AI systems increasingly sophisticated, blurring the lines between simulated interaction and human connection. This evolution necessitates a deeper exploration of how such interactions influence psychological well-being and the fabric of offline relationships.

Possible Methodology:

• Experience Sampling: Capture real-time data on users’ experiences and emotional responses during interactions with these AI systems.
• Semi-Structured Interviews: Conduct in-depth interviews with users to gain detailed insights into the formation, evolution, and impact of these parasocial relationships.

Note: Other methodological approaches may also be viable.

Possible Research Questions:

1. How do parasocial interactions with intimate conversational and romantic AI systems influence users’ overall well-being?
2. What factors contribute to the formation of these parasocial bonds?
3. How do users perceive the benefits (e.g., emotional validation, social support) and drawbacks (e.g., increased loneliness, dependency) associated with these interactions?
4. In what ways do these digital relationships affect the quality of users’ offline interpersonal connections?

Candidate Requirements & Contact:
We invite candidates with expertise in qualitative research methods—particularly experience sampling and semi-structured interviews—who are interested in digital media, social psychology, or human–computer interaction. For further details or to discuss your research proposal, please contact:

Georg Voronin
E-mail: georg.voroninuuni-potsdampde

Selected References:

• Horton, D., & Wohl, R. R. (1956). Mass Communication and Para-Social Interaction: Observations on Intimacy at a Distance. Psychiatry, 19(3), 215–229.
• Rubin, A. M., Perse, E. M., & Powell, R. A. (1985). Loneliness, Parasocial Interaction, and Local Television News Viewing. Human Communication Research, 12(2), 155–180.
• Giles, D. C. (2002). Parasocial Interaction: A Review of the Literature and a Model for Future Research. Media Psychology, 4(3), 279–305.
• Hartmann, T. (2016). Parasocial interaction, parasocial relationships, and well-being. In The Routledge handbook of media use and well-being (pp. 131-144). Routledge.
• Hoffner, C. A., & Bond, B. J. (2022). Parasocial relationships, social media, & well-being. Current Opinion in Psychology, 45, 101306.
• Stein, J. P., Liebers, N., & Faiss, M. (2024). Feeling better... But also less lonely? An experimental comparison of how parasocial and social relationships affect people’s well-being. Mass Communication and Society, 27(3), 576-598.

Description:

Artificial Intelligence (AI) is being used more and more these days by companies and individuals for a wide variety of tasks which can be best seen through the popularity of ChatGPT and similar (AI)-based technologies. One of the areas in which AI is being applied is to recognize patterns in different types of data, which can span from formats such as images, and videos to textual data. Further, these technologies find application in different contexts such as in medicine to recognize diseases in medical images (Chan et al., 2020) or text processing in the hiring process with AI-based CV screening software (Albert, 2019). However, one frequent criticism of the use of AI is the lack of explanation of its results due to the black box principle that often surrounds the AI technologies. This black box principle describes a situation where it is not possible to see the inner workings of a technology meaning that it is not feasible to follow how a given input to a technology results in the technology’s output which tests the individual's trust in the technology and the correctness of its results. This in turn led to the development and spread of explainable AI (XAI) techniques such as SHapley Additive exPlanation (SHAP) or Local Interpretable Model-Agnostic Explanations (LIME), which can be used to attempt to explain the results of different AI approaches.

In the field of cyber security, communication online for example on social media platforms such as Twitter about cyber threats is a valuable resource for identifying relevant incidents (Eyilmez et al., 2020). Moreover, AI-based approaches for the detection of cyber security-related events online are proving to be successful and promising approaches for the purpose of cyber threat identification (Sceller et al., 2017).  In the context of XAI’s effect on the acceptance of AI results research results are inconclusive due to different research findings indicating an increase in acceptance while others don’t (Schemmer et al., 2022). This begs the question of whether XAI explanations have an effect on the acceptance of AI-detected cybersecurity-related events. Due to the public not being very familiar and well-versed in the topic of cyber security while it is becoming an increasingly important topic to society the effect of XAI on the acceptance of AI-identified events compared to human-identified incidents could differ from the acceptance of AI-identified events in different domains. This research could help in increasing the public's willingness to follow warnings of cyber threats through adjustments based on the findings of this research.

Therefore, in this thesis, different messages will be designed using AI and human-identified incidents. Furthermore, a distinction between AI-based identification with an XAI component and without an XAI component shall be made. These are to be visualized and evaluated in the form of mockups or a click dummy. The evaluation will be based on an online study and incorporate recent knowledge from literature (e.g., Eyilmez et al., 2022; Sceller et al., 2017; Schemmer et al., 2022; Riebe et al., 2023, Basyurt et al., 2022).

Requirements & Contact:

For this thesis you should be interested in cyber security, Artificial Intelligence and ideally have worked with quantitative research data before.

If you would like to apply for this thesis, please contact Ali Sercan Basyurt

References:

Albert, E. T. (2019). AI in talent acquisition: a review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215-221.

Basyurt, A. S., Fromm, J., Kuehn, P., Kaufhold, M. A., & Mirbabaie, M. (2022). Help Wanted-Challenges in Data Collection, Analysis and Communication of Cyber Threats in Security Operation Centers.

Chan, H. P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020). Deep learning in medical image analysis. Deep Learning in Medical Image Analysis: Challenges and Applications, 3-21.

Eyilmez, K., Basyurt, A., Stieglitz, S., Fuchss, C., Reuter, C., & Mirbabaie, M. (2022). A Design Science Artefact for Cyber Threat Detection and Actor Specific Communication.

Riebe, T., Biselli, T., Kaufhold, M. A., & Reuter, C. (2023). Privacy Concerns and Acceptance Factors of OSINT for Cybersecurity: A Representative Survey. Proceedings on Privacy Enhancing Technologies, (1), 477-493.

Sceller, Q. Le, Karbab, E. M. B., Debbabi, M., and Iqbal, F. 2017. “SONAR: Automatic Detection of Cyber Security Events over the Twitter Stream,” ACM International Conference Proceeding Series.

Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., & Vössing, M. (2022, July). A meta-analysis of the utility of explainable artificial intelligence in human-AI decision-making. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (pp. 617-626).

Description

The emergence of mobile devices such as smartphones and wearables has transformed the healthcare industry, enabling patients to self-manage their health. Mobile app platforms such as the Google Play Store and the Apple App Store have become key players in the mobile health (mHealth) domain (Gleiss et al., 2021). Those apps collect much data, that might one the one hand improve their outcomes, but might also induce privacy risks that are especially crucial in the health domain. From previous research it is known, that there is a phenomenon called the “privacy paradox” (Kokolakis 2017), which posits that even though users say that they value their privacy, they do not act accordingly. Some researchers propose a privacy calculus perspective (Wang et al. 2016) on this issue, which implies that users weigh the perceived benefits and risks before making a decision.

One other explanation could be, that collecting data and sharing it with others might lead to the app becoming part of a larger ecosystem of developers, third-party functionalities and other applications. This would take into account generativity (Fürstenau et al. 2023), i.e., that developers that are not part of the organization can be part of the wider ecosystem of an app (social view on generativity), as well as it is possible to integrate and develop new products and features (product view on generativity) by using and sharing data.

To do so, pre-existing data on data collection from the Apple App Store, as well as data from Github can be used to identify privacy and generativity. The thesis will provide a first conceptual view on a potential privacy-generativity-tradeoff as well as preliminary empirical evidence.

Requirements & Contact

For this thesis you should be interested time-series analysis and have some previous experience with Python. You should be interested in digital platforms as well as ecosystems. If you would like to apply for this thesis, please contact Kai Schewina.

References

Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469-490.

Fürstenau, D., Baiyere, A., Schewina, K., Schulte-Althoff, M., & Rothe, H. (2023). Extended generativity theory on digital platforms. Information Systems Research, 34(4), 1686-1710.

Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & security, 64, 122-134.

Wang, T., Duong, T. D., & Chen, C. C. (2016). Intention to disclose personal information via mobile applications: A privacy calculus perspective. International Journal of Information Management, 36(4), 531-542.

Description:

Large Language Models (LLMs) such as ChatGPT have the capability to generate scientific text, which is likely to be increasingly adopted in research. Current estimates suggest that at least 10 percent of scientific literature has been assisted by LLMs, with significant implications. This bachelor thesis should answer the question that field of science adopted LLMs the most, by analyzing the abstracts of journals different fields.

Requirements:

The following skills are required in order to succeed:

• Proficiency in at least one programming language, with a preference for Python.
• Knowladge in or willingness to learn fundamental and advanced techniques of natural language processing
• Interrest in the research domain.

Additionally you need to be enrolled in a Bachelor programm at the University of Potsdam.

Further information can be provided upon request.

If you are interested on writing a thesis in this field, please reach out to Till Schirrmeister