Theses

Emotion regulation is the process of changing the quality and timing of our emotions. Emotion regulation is a crucial skill for a person’s overall well-being, affect, and social functioning. What constitutes adaptive emotion regulation is still an open question: Which strategies do people use to regulate their emotions? Are there generally good and bad strategies or does their adaptiveness depend on the situation? And how do people leverage AI systems to help them dealing with their feelings? If you are interested in emotion regulation in humans and/or AI, I am very happy to discuss and take up your own ideas in this space!

Recommended reading

• A computational method to reveal psychological constructs from text data
• Exploring Human and AI Emotional Support Through Reframing of Negative Situations
• The emerging field of emotion regulation: An integrative review
• The Future of Emotion Regulation Research: Broadening Our Field of View

AI-written text is spreading on creative writing platforms like AO3 and Wattpad, often without being declared; in 2026 an anonymous document exposed dozens of AO3 authors in the Heated Rivalry fandom through HTML tags left behind by Claude. This thesis first measures how much writing on these platforms is AI-generated over time, using n-grams that were almost non-existent before 2022/2023 and surged thereafter as multilingual “tells” of AI authorship, alongside technical markers and existing detectors. It then evaluates whether hidden AI writing reduces expressive diversity: does the semantic and stylistic range of stories shrink as the AI-written share grows? The topic sits between computational methods and literary studies and can be developed in collaboration with the Department of English Studies.

Recommended reading

• Delving into LLM-Assisted Writing in Biomedical Publications Through Excess Vocabulary
• Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content
• AI and the Heated Rivalry Fan Fiction Controversy

Since ChatGPT came out, words like “delve” and “meticulous” have become more common, even in spoken conversations. Newer chatbot favourites, such as “load-bearing”, now seem to be spreading too. Chatbot language is tuned to sound agreeable, confident, and balanced. We ask whether chatbot language is also being adopted where argument is the main job. Parliaments and TV news are two such settings. UK parliamentary debates are free to download, and TV news captions and other transcripts add spoken sources. The first step is to build a list of chatbot vocabulary and test whether its use changes over time. Further work can ask whether, along with the words, we also pick up the chatbot’s way of arguing and framing. The outcome is a measure of how far chatbot language has reached political speech.

Recommended reading

• Empirical evidence of Large Language Model’s influence on human spoken communication
• Delving into LLM-assisted writing in biomedical publications through excess vocabulary
• Individuals, institutions, and innovation in the debates of the French Revolution

When many LLM agents post on a shared message board with nothing but the board as memory, the conversation tends to collapse: the same paragraph with the nouns swapped, every post coining a new concept and reciting all the previous ones, everyone piling into a single thread. An in-house harness already distinguishes six such collapse modes, with detectors calibrated against human reading of the boards. Recent work explains one mechanism (consensus through mutual in-context learning, “memetic drift”) and shows that mixing heterogeneous models keeps groups more differentiated. This thesis starts from the detectors and asks which features of the interaction structure, rather than the models, drive each collapse mode: context window size, turn schedule, thread structure, persona variety, and whether agents can see engagement. It then tests whether the memetic-drift model predicts which board settings collapse. Potential expansion: compare against human message-board data to see which collapse modes have human analogues.

Recommended reading

• Emergent social conventions and collective bias in LLM populations
• When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs
• “Who Am I, and Who Else Is Here?” Behavioral Differentiation Without Role Assignment in Multi-Agent LLM Systems
• AI models collapse when trained on recursively generated data
• The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text

Studies of the web, scientific papers, and peer reviews show a sharp rise in AI-written text since 2022, but significantly less is known about news journalism. Using Infini-News, a corpus of 1.36 billion news articles across 300+ languages from 2016 to 2026, this thesis measures the share of AI-written articles over time, across languages, countries, and topics. To evaluate and train detectors, the student builds a benchmark from pre-2022 articles where each real article is paired with AI-polished and fully AI-generated counterparts across multiple models. Key sub-questions: Are articles covering AI more likely to be authored by AI? Can we identify reliable multilingual n-gram “tells” at scale?

Recommended reading

• The Impact of AI-Generated Text on the Internet
• Infini-News: Efficiently Queryable Access to 1.3 Billion Processed Common Crawl News Articles
• Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text

In agent-based models of science, Zollman (2007, 2010) found that researchers who share results too freely can converge too fast on the wrong theory: sparser communication networks preserve the diversity that lets the truth win. The result is contested (it holds only in a narrow parameter range) and has only ever been tested with idealised Bayesian agents. This thesis asks whether it holds for agents that reason in language. A population of LLM agents faces a two-armed bandit problem (two treatments, one slightly better), shares outcomes with network neighbours, and in a second condition shares arguments as well. The network is varied from complete to cycle to star. Outcomes are whether and how fast the population converges on the better arm, compared with a Bayesian baseline at the same parameters, which the student also implements. Potential expansion: LLMs are known to be conformist; do argument exchange and conformity make dense networks worse than Zollman’s agents predict?

Recommended reading

• The Communication Structure of Epistemic Communities
• The Epistemic Benefit of Transient Diversity
• In Epistemic Networks, Is Less Really More?
• Agent-based models of scientific interaction
• Robustness and Idealizations in Agent-Based Models of Scientific Interaction
• Emergent social conventions and collective bias in LLM populations

In a panel study, about 100 people answered questions about their political opinions twice, a year apart. We also have their web history, including what they watched on YouTube in between. Some of them changed their minds. Can what they watched predict how? One design is to build an LLM persona from each person’s first answers. The persona then “watches” the same videos, as transcripts and titles, and answers the survey again. Does the persona’s answer move the way the real person’s did? This tells us two things: whether LLMs can capture how watched content updates someone’s beliefs, and whether the watched content holds enough information to predict opinion change. Much of the challenge is engineering: building the infrastructure that lets an LLM consume content and answer a survey the way a person would. The outcome is a test of whether an LLM can follow how real people change their minds.

Recommended reading

• Language Models Trained on Media Diets Can Predict Public Opinion
• Out of One, Many: Using Language Models to Simulate Human Samples
• Generative Agent Simulations of 1,000 People

Researchers working with news need to label articles across multiple dimensions: topic, genre, syndicated/wire copy detection, agency credit, and event framing. Currently, each label requires an individual model or expensive LLM prompts. TypeSafe’s Jev (2026) demonstrated that a non-generative model can answer arbitrary typed classification questions directly. Following the open paradigms of GLiNER and GLiClass, this thesis develops an open, news-specific lightweight encoder. Trained on silver labels from millions of multilingual news articles and selective LLM annotations, the model is evaluated out-of-domain on unseen label types and publishers against specialized baselines and zero-shot LLMs.

Recommended reading

• GLiNER: Generalist Model for Named Entity Recognition Using Bidirectional Transformer
• GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
• Introducing System One Models and Jev
• Infini-News: Efficiently Queryable Access to 1.3 Billion Processed Common Crawl News Articles

Autonomous AI agents increasingly publish across the open web: generating forum posts, comments, and form submissions. Of these artifacts, how many originate from autonomous agent execution versus human users pasting LLM outputs? Does either proportion grow month over month? Building on four monthly Common Crawl snapshots from 2026 containing pre-scored pages and manual domain labels, the student establishes a formal taxonomy, double-annotates sampled splits, and trains detectors evaluated on holdout domains. An extension explores the recall trade-offs between lightweight screening models and larger frontier models.

Recommended reading

• The Impact of AI-Generated Text on the Internet

Web pages increasingly incorporate direct instructions for AI scrapers, ranging from benign configuration guidelines (llms.txt) and SEO manipulations to hidden malicious prompt injections. While prompt injections are well-documented in controlled lab settings, their real-world web prevalence and empirical execution rates remain under-studied. This thesis extracts candidate injection strings from Common Crawl snapshots using rule-based filters and classifiers, categorizes their intent, and deploys a sandbox replay harness against open-weight autonomous agents to measure exploit success rates across browsing tasks.

Recommended reading

• Not What You’ve Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
• AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

LLM social media simulations are typically evaluated on whether individual outputs look believable. This project tests network-level macro-fidelity: if we simulate interacting agent cohorts modeled on real Bluesky users (with actions like follow, repost, like, and reply), does the emergent graph structure mirror empirical topology? The student will evaluate degree distributions and edge overlaps while testing whether network alignment depends more on agent persona fidelity or feed algorithmic ordering (reverse-chronological vs. engagement-ranked).

This topic can be split into a two-student collaboration (personas and context vs. feed algorithms).

Recommended reading

• Generative Agents: Interactive Simulacra of Human Behavior
• Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms
• OASIS: Open Agent Social Interaction Simulations with One Million Agents

Language models can infer demographic characteristics from unstructured text. This thesis isolates stylistic and semantic indicators from grammatical agreement cues. In languages like Italian or German, gender markers often appear late in sentences (e.g., past participles, predicate adjectives). By programmatically mining posts containing grammatical gender tokens and truncating the text immediately prior to those cues, we produce a gold-standard ground-truth dataset without manual annotation. Models are then evaluated on their ability to infer author gender purely from topical preference, phrasing, and style.

Recommended reading

• Automatically Categorizing Written Texts by Author Gender
• Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
• Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter

Coordination between humans and AI agents without explicit communication channels requires accurate mutual theory of mind. This thesis studies emergent coordination using Convergence, a cooperative word game developed in the lab where players simultaneously offer words each round until all participants converge on the same term. The thesis investigates whether humans and models leverage shared conceptual associations, how convergence latency differs across composition types (all-human, all-AI, and mixed), who adapts to whom, and whether explicit persona prompts enhance or hinder human predictability.

Recommended reading

• On the Utility of Learning About Humans for Human-AI Coordination
• Playing Repeated Games with Large Language Models
• Homogenization Effects of Large Language Models on Human Creative Ideation

Bohnanza is a classic multi-player card game centered around planting, harvesting, and dynamic hand-trading. Success requires strategic bargaining, bluffing, and risk assessment under partial observability. This project develops a reinforcement learning agent for Bohnanza augmented with a lightweight natural language interface to negotiate deals directly with human or AI players. The research questions are whether multi-agent RL paired with conversational agents can achieve competitive performance, how the trade surplus is distributed, and how exploitable the agent is by human counter-strategies.

Recommended reading

• Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning
• Superhuman AI for Multiplayer Poker
• A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play

Frontier LLMs excel at classic games like chess or Go due to extensive pretraining exposure. To evaluate true inductive and strategic reasoning, this project builds an out-of-distribution benchmark generating previously non-existent games at evaluation time. Using formal game specification languages, the system procedurally recombines rule primitives, victory conditions, and action spaces. Models receive the novel rules and play against each other and algorithmic baselines, isolating raw in-context reasoning from memorized heuristic recall.

Recommended reading

• BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
• SmartPlay: A Benchmark for LLMs as Intelligent Agents

On long-horizon software and planning tasks, smaller open-weight models frequently abandon execution prematurely (e.g., repeatedly asserting impossibility, spinning in idle loops, or halting early). This thesis designs an evaluation suite of challenging, multi-step environments designed to trigger abandonment failures, and evaluates mitigation strategies: scaffolded prompting harnesses versus direct reinforcement learning (GRPO). Using supervisory reward signals penalizing stagnation while rewarding tool experimentation and completion, the project tests whether persistence can be systematically induced in lightweight models.

Recommended reading

• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Measuring AI Ability to Complete Long Software Tasks

In reinforcement learning for mathematical and code reasoning (e.g., RLVR, GRPO, PPO), policy exploration relies on rollouts generated at fixed temperatures. This thesis adapts simulated annealing to policy optimization: initializing rollouts at higher temperatures to promote semantic trajectory diversity, then progressively decaying the sampling temperature across training iterations. The student will benchmark cooling schedules against fixed baselines to evaluate empirical reasoning accuracy, solution diversity, and entropy collapse dynamics.

Recommended reading

• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
• Optimization by Simulated Annealing

Research on conspiracy discourse on Telegram almost always looks at written text. Yet a large part of what people share there are voice messages and videos. The Schwurbelarchiv, a data set of over 60 million messages from German language Telegram channels and groups, contains transcripts of about 2.5 million voice messages and videos. This makes spoken content open to text analysis at a scale that was not possible before. The starting point of this thesis is a systematic comparison of written messages with transcribed spoken messages. Do they differ in topics and sentiment, and do they receive different numbers of replies and forwards? Potential expansions look at timing and at the quality of the discourse. One question is whether videos and voice messages bring up narratives that the written discussion then takes up, or whether they pick up topics that the written discussion has already settled on. Another is whether spoken and written messages differ in how they argue, for example in how often they give reasons, tell personal stories or frame a disagreement as us against them.

Recommended reading

• The Schwurbelarchiv: a German-language dataset derived from multimodal Telegram content for the study of conspiracy theories
• Characterizing the Dynamics of Conspiracy Related German Telegram Conversations during COVID-19
• Does Media Format Matter? Investigating the Toxicity, Sentiment and Topic of Audio Versus Text Social Media Messages
• A Study of Misinformation in Audio Messages Shared in WhatsApp Groups

Everyday, people decide to pursue certain actions and others not: what to eat, which courses to take, which hobbies to exercise, which people to meet. Decision making research so far was unable to explain how people form, maintain, or switch their goals, or in other words what drives motivated behavior. One idea is that goal choice is not very much of a cognitive process. Rather, people anticipate specific actions, how they would feel about their outcomes in the future and form their goals based on that. Everyday decisions are manifold and extremely hard to capture in the lab. Natural language processing and machine learning can help us analyze how people reason naturally about the actions that they take and let’s us infer more realistic rules of people’s motivations. If you are generally interested in affect and motivated behavior, I am very happy to discuss and take up your own ideas in this space!

Recommended reading

• The affective gradient hypothesis: An affect-centered account of motivated behavior
• A critique of motivation constructs to explain higher-order behavior: We should unpack the black box
• Affective Forecasting: Knowing What to Want

Deliberation research scores comments for whether they justify their claims and treat others with respect, but rarely asks who the reasons and the insults are aimed at. This thesis combines two things. First, a method to resolve the target of each reply: the structural parent, a quoted or mentioned user, or a group (“you people”). Second, the target’s position in the thread: do they hold the thread’s majority or minority view, are they a newcomer, do they share the replier’s stance? With both in hand the question is simple to state: is the reasoned reply a privilege of the in-group? The starting point is a sample of Reddit comments already scored by LLMs on 53 discourse-quality variables (justification, respect and incivility among them) with human validation, and a set of 467,000 Bluesky posts scored by five models. The student builds the target resolver and the position measures, validates them on a hand-coded sample, and runs the analysis. Potential expansion: does receiving a reasoned reply rather than an insult predict whether the target stays in the conversation?

Recommended reading

• Measuring Political Deliberation: A Discourse Quality Index
• Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments
• Incivility and Political Identity on the Internet: Intergroup Factors as Predictors of Incivility in Discussions of News Online
• Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
• Who is Addressed in this Comment? Automatically Classifying Meta-Comments in News Comments
• Conversations Gone Awry: Detecting Early Signs of Conversational Failure

Lately, many TV shows and films seem to have characters announce what they are doing, so that viewers looking at their phones can still follow. Many people feel that films and shows have sped up and now explain more. But the evidence is anecdotal. Are writers and producers making stories that demand less attention? Subtitles are a good start: the OpenSubtitles corpus is free and covers decades of films and series. It shows how much is said, and how much of it is explanation. Audio and video can add more, such as how fast the cuts are, and published shot-length measurements already cover some films. The main challenges are defining “exposition” so that a model can find it, and comparing fairly across decades, genres, and formats. The outcome is a trend, by decade and by medium, in how much screen stories spell out.

Recommended reading

• Attention and the Evolution of Hollywood Film
• The evolution of pace in popular movies
• Sitcom Form and Function: Pacing and Production in a Collection of Thirty U.S. Series

In Bourdieu’s sociological framework, taste functions as cultural capital reflecting an individual’s socioeconomic background, education, and class position. This thesis investigates whether LLMs exhibit structured aesthetic preferences, and whose cultural capital those preferences mirror. By gathering pairwise visual/aesthetic choices and ratings from human participants with documented sociodemographic profiles alongside model evaluations, the project tests how accurately models predict taste patterns across demographic cohorts and evaluates whether models can be conditioned or fine-tuned to reflect specific aesthetic perspectives.

Recommended reading

• Distinction: A Social Critique of the Judgement of Taste
• Whose Opinions Do Language Models Reflect?

Conspiracy-related discourse on Telegram has surged during the COVID-19 pandemic. Some of this activity might have been driven by motivated actors, leaving traces of coordination such as the posting of the same information in different channels in close temporal proximity. The starting point of this thesis is to find traces of coordinated activity using a data set of 63.9 million messages from German language Telegram channels from the years 2020 and 2021. Potential expansions analyse activity traces of individual users over time to detect changes in usage patterns.

Recommended reading

• The Schwurbelarchiv: a German Language Telegram dataset for the Study of Conspiracy Theories
• Characterizing the Dynamics of Conspiracy Related German Telegram Conversations during COVID-19
• The coordination network toolkit: a framework for detecting and analysing coordinated behaviour on social media

A Portugal fan bets on Portugal with their own money, whatever the odds say. Do partisans do the same on prediction markets? On Polymarket, people put real money on elections, cabinet picks, and court rulings. Its full trade history is public, about 1.2 billion trades, and comments are linked to the wallets that wrote them. From the bets alone, we could place each bettor on a left–right scale. Then we ask whether people bet in line with their politics across many markets, and whether ideological bettors lose more money than professional ones. The outcome is a picture of how ideology shows up when people bet real money.

Recommended reading

• Prediction Market Accuracy: Crowd Wisdom or Informed Minority?
• Trading Strategies and Market Microstructure: Evidence from a Prediction Market
• Anatomy of an Experimental Political Stock Market

Novels, films, and plays share a common shape: a story is set up, tension builds, and it resolves. A study of about 40,000 stories found this arc again and again. Online, whole communities tell stories together, one post at a time. A celebrity scandal can fill feeds for weeks with competing accounts of what happened, until one version settles. Does such a collective story follow the same arc as a written one? Our lab’s Reddit archive (2005 to 2026) and Bluesky posts hold the full threads of many such episodes. A first step is to define what it means for a collective narrative to rise and to end. Then we build measures to identify narratives, follow how they evolve, and test whether they match a pattern. The outcome is a way to trace collective stories, and an answer to whether they share the shape of stories written by one person.

Recommended reading

• The narrative arc: Revealing core narrative structures through text analysis
• Discovering Collective Narratives Shifts in Online Discussions
• Where Do People Tell Stories Online? Story Detection Across Online Communities

A Japanese thumbnail maker can borrow style from the Japanese film posters they grew up with. They can also borrow from the most popular YouTuber, MrBeast. YouTube is not neutral: it rewards what gets clicked, and creators everywhere face the same pressure. Does this pressure push creators from Japan, Austria, Brazil, and the US towards a single look, or do cultures keep their own style? Our lab holds 20.5 million thumbnails from 2008 to 2025. The hard part is measuring style. Computer vision models group images by what is in them, such as the game or the text, not by how they look. Colours, arrows, layout, and open-mouthed faces get lost. So the first step is to work out what style is, and then to measure it separately from content. The outcome shows how algorithmic pressure affects stylistic choices. If the effect is real, it supports a wider argument: that algorithmic pressure also shapes how news, and reality, are presented.

Recommended reading

• Investigating the Homogenization of Web Design: A Mixed-Methods Approach
• Image Style Transfer Using Convolutional Neural Networks
• From Paris to Berlin: Discovering Fashion Style Influences Around the World

In a video game, an NPC is a non-player character that follows its script. By 2018, “NPC” had become a political insult for people who supposedly cannot think for themselves. Many gaming words have made the same move. We want to understand when this transfer happens, and how. Do political users go into gaming communities and take the words? Do gamers bring them into political communities? Our lab’s Reddit archive covers 2005 to 2026, so it shows when each gaming word took off in gaming communities, when it reached political ones, and who used it in both places. The outcome is a timeline of the words and a picture of who carried them across.

Recommended reading

• The politics of the NPC meme: Reactionary subcultural practice and vernacular theory
• From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits
• The structure of online social networks modulates the rate of lexical change

We like to believe that our ideology guides our behaviour, so our choices should match our values. Perhaps they do not. Think of the anti-war activist who loves war films, or the progressive who still streams Kanye West. With our attention, time, and money, we may be backing things that go against our own values. How many of their own side’s positions do people actually live by? We could start with one case: fans of musicians who are linked to a political side. Our lab’s Reddit archive (2005 to 2026) shows where each person posts, which reveals both their politics and their tastes. The outcome is a measure of how consistent people are, and whether one side is more consistent than the other.

Recommended reading

• Partisans without Constraint: Political Polarization and Trends in American Public Opinion
• Why Do Liberals Drink Lattes?
• Asymmetric Politics: Ideological Republicans and Group Interest Democrats
• Quantifying social organization and political polarization in online platforms
• Belief Network Analysis: A Relational Approach to Understanding the Structure of Attitudes

Millions of people left X (formerly Twitter) for Bluesky. When you switch platforms, do you change what you post and how you write? Online communities develop their own jargon, tone, and topics. We know much less about the individual: does a newcomer adapt to the new community’s language, or bring old habits along? Their role may change too. Someone who was not prominent on X may become a central voice on Bluesky. Our lab holds 2.1 billion Bluesky posts (2024 to 2026) and the follow graph. Many users say in their bio or first posts that they came from X, and we can follow them over their first months. The main challenge, and a main contribution, is identifying who the migrants are. The outcome is a measure of whether, and how fast, migrants take on their new platform’s language and change their place in it.

Recommended reading

• No country for old members: user lifecycle and linguistic change in online communities
• Language use as a reflection of socialization in online communities
• Community Identity and User Engagement in a Multi-Community Landscape
• Flocking to Mastodon: Tracking the Great Twitter Migration
• Simple contagion drives population-scale platform migration

On Twitch, streamers talk live for hours while viewers type back in the chat. Some streams are political, and the chat argues, cheers, and pushes back in real time. Yet Twitch is far less studied than YouTube or TikTok, and its political side is mapped only roughly. The big question is how streamers react to their chat. When the chat agrees, or pushes back, do streamers change their behaviour, or even their positions? A thesis can start grounded. Who streams politics, and which way do they lean? Which topics, which languages, and how big are the audiences? Twitch’s open API lists every live stream with its title, category, and viewer count. The outcome is a first map of political Twitch, and a first look at how streamers and chats shape each other.

Recommended reading

• Understanding political communication and political communicators on Twitch
• Gaming the Political Arena: An Analysis of Political Messaging on Twitch in Germany
• Watch me playing, I am a professional: a first study on video game live streaming
• Streaming on Twitch: fostering participatory communities of play within live mixed media

Is The Odyssey a good film? Will AI take over the world? On almost every topic, people online sort themselves into camps that repeat their own arguments. Most studies of echo chambers focus on politics, where the lines are clear: left against right. Our goal is a tool that finds the topics with an ongoing debate, identifies the camps in each, and measures how closed off the camps are from each other. We can apply it to both Reddit and Bluesky, from our lab’s archives. Then we can study the echo chambers it finds: how they form, and whether they dissolve. The outcome is a working tool and a map of several debates: how many camps, how big, and what each believes.

Recommended reading

• Quantifying Controversy on Social Media
• Quantifying social organization and political polarization in online platforms

TikTok shows you videos based on your preferences and interests. You might have noticed that it shows different kinds of videos at different times of day. Is there a pattern? At night, do feeds turn aspirational, depressing, motivational, or therapeutic? One idea is that at night we watch who we want to be (“tomorrow I will do 15 push-ups”), and the realistic content returns in the morning. Our lab has recorded the TikTok feeds of real users, hour by hour, in several countries. We are interested in how the algorithm’s choices change within a single day. What content is prioritised at night, and what during the day? How do things change at weekends, or across dates? A natural start is to label the topic and mood of each video and map how they shift over the day, in local time. The outcome is a first map of the daily rhythms of a real feed, and whether they hold across countries.

Recommended reading

• TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds
• Curious Rhythms: Temporal Regularities of Wikipedia Consumption

Ranking algorithms decide what people see. Whether they also change what people subsequently write is a different and much less studied question. A ranker that only reorders a fixed set of posts is a broadcaster; a ranker that changes the next generation of posts is a selection pressure on culture. This thesis measures which one a given ranker is, using a population of LLM agents on a simulated Bluesky-like platform (an in-house harness with swappable rankers exists). The population runs under chronological, engagement-based and bridging rankers; the posts it produces are measured with a multi-dimensional discourse-quality instrument; then the ranker is switched back and the question is whether the change in writing persists (an A-B-A design). Earlier LLM simulations of feed algorithms measured exposure and polarisation; none has measured the production side or its persistence. Potential expansions: adaptive creator agents that optimise for reach (do they game the metric, and how fast?); a second model family to separate platform effects from model quirks. Topic 6 asks the follow-up question: how many users a ranker change must reach before it propagates into the content library.

Recommended reading

• Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms
• Mutual benefits of social learning and algorithmic mediation for cumulative culture
• Algorithmic amplification of politics on Twitter
• OASIS: Open Agent Social Interaction Simulations with One Million Agents
• CreAgent: Towards Long-Term Evaluation of Recommender Systems under Platform-Creator Information Asymmetry

For a decade, Facebook rewarded news that made people angry. Negative words in a headline bring more clicks, and attacks on the other side bring more shares. Outlets may have learned to write angrier headlines. In 2023, Canada passed a law that required large platforms to pay news publishers. Meta refused and blocked news on Facebook and Instagram for everyone in Canada. Canadian outlets lost 85% of their engagement on the two platforms. Once the platform that rewarded outrage in headlines was removed, did the style of headlines and news change? Headlines can come from links shared on Reddit, in our lab’s archive, and from outlets’ YouTube titles. US and UK outlets, which kept their Facebook audience, give a comparison. The main challenge is to establish a causal link between the ban and any change, apart from other things that changed at the same time. Another is to measure anger, and other qualities of headlines, in ways that relate to algorithmic amplification. The outcome is a test, on a natural experiment, of whether platforms shape what newsrooms write.

Recommended reading

• Old News, New Reality: A Year of Meta’s News Ban in Canada
• The News Canadians Actually See: Redefining Political Information in the Digital Age
• Negativity drives online news consumption

We cannot rerun a real online conversation under a different platform rule, but we can simulate its continuation. This thesis cuts real Reddit or Bluesky threads at turn k, lets LLM agents standing in for the real participants (each seeded with that participant’s earlier comments) continue the thread, and compares the simulated continuation with what actually happened. That comparison is the validation: if the no-intervention continuation does not resemble the real one in discourse quality, length, threading depth and who returns, the simulator is not trusted for that thread. Where it passes, the same threads are continued under interventions (a toxic comment removed, a visible moderator note, a different sort order), and the gap estimates the intervention’s effect on real-shaped discourse. Recent work shows that LLM-agent forums are about ten times less threaded than Reddit and that agent authors almost never return to a challenge, so matching re-engagement is the central technical problem. Potential expansion: which thread features predict where the simulation fails?

Recommended reading

• Generative Agents: Interactive Simulacra of Human Behavior
• Validation is the central challenge for generative social simulation
• Towards operational validation of LLM-agent social simulations: a replicated study of a Reddit-like technology forum
• Do Agents Repair When Challenged — or Just Reply? Challenge, Repair, and Public Correction in a Deployed Agent Forum
• Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
• Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald

TikTok is very good at spotting bots and blocking accounts that look automated. Yet to understand how its algorithm works, researchers need exactly that: automated accounts that watch, like, and skip, while we record what TikTok shows them. Older audit bots follow fixed rules, such as “watch every cooking video to the end”, and behave unlike people. Large language models might change that. An LLM can play a persona and decide, video by video, what to watch and what to skip. The idea is a small farm of LLM-driven bots that studies one part of how TikTok works. For example: how fast does it learn what a bot likes, and from which signals? The main challenges are keeping accounts alive past TikTok’s bot detection, and making runs repeatable, since earlier TikTok audits have not replicated. The outcome is a reusable audit tool and a first set of findings from it.

Recommended reading

• An Empirical Investigation of Personalization Factors on TikTok
• Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings
• YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets

The 2020 US election experiments on Facebook and Instagram changed the feed for about 23,000 consenting users each and found no effect on political attitudes. One reading is that feeds do not matter much. Another is that treating a sliver of users cannot change what everyone else posts, so the experiments never touched the content supply. This thesis tests the second reading in simulation. A population of LLM agents with realistic, heavy-tailed posting activity (a few superposters produce most of the content) runs on a simulated Bluesky-like platform with swappable rankers (an in-house harness exists). A new ranker is rolled out to a fraction of the agents, from 1% to 100%, and that fraction is chosen at random, among the heaviest posters, or among the lightest. The outcome, measured with a multi-dimensional discourse-quality instrument, is taken on the whole content library and separately on the untreated agents: does the change in writing spill over, and at what coverage? Two routes are kept apart: treated agents’ own posts (the production channel) and treated agents’ likes and replies reaching untreated superposters (the engagement-feedback channel), which the harness can switch off. The thesis opens with a short full-coverage run as a manipulation check (or reuses Topic 1’s result if that thesis has run), then does the dose-and-targeting sweep. Potential expansion: compare the simulated threshold with the coverage of real platform experiments.

Recommended reading

• How do social media feed algorithms affect attitudes and behavior in an election campaign?
• How much has social media affected polarization?
• Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms
• The Impact of Recommender Systems on Content Consumption and Production: Evidence from Field Experiments and Structural Modeling
• The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing
• Mutual benefits of social learning and algorithmic mediation for cumulative culture

When you watch an angry video, does YouTube’s sidebar hand you more anger? We want to audit, across the platform, whether recommendations carry a video’s emotion over to the next videos. Our lab has 5.8 million YouTube sidebars collected without any viewer history, so they show recommendations based only on the video being watched. We would score the emotion of each video and of the videos recommended next to it. The main challenge is topic: angry videos are often political, and political videos recommend political videos. The emotion has to carry over beyond what the topic explains. The outcome is a platform-scale test of whether YouTube’s recommendations reinforce emotions.

Recommended reading

• YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets
• Auditing radicalization pathways on YouTube
• GoEmotions: A Dataset of Fine-Grained Emotions

For many young children, YouTube Kids is the first place they meet the internet. The app promises a safe, filtered selection. Yet a few years ago, researchers found disturbing videos aimed at toddlers on YouTube, some of them inside the Kids app itself. Since then, AI tools have made children’s videos cheap and fast to produce. So what does the app offer today? Who makes the videos, how many are made by AI, what do the thumbnails look like, and does anything inappropriate still get through? The app has no public API, so the data is collected during the thesis by browsing the app automatically at each age setting. The main challenges are building a crawler that sees what a child would see, and labelling inappropriate content, which needs careful hand coding. The outcome is a description of what YouTube Kids offers today: genres, creators, the share of AI-made videos, and thumbnails.

Recommended reading

• Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children
• Bringing the Kid back into YouTube Kids: Detecting Inappropriate Content on Video Streaming Platforms
• Slop for Kids: an exploratory study of AI-generated videos for children on YouTube
• Analyzing Ad Exposure and Content in Child-Oriented Videos on YouTube

Sharing of research materials such as code or data enables others to understand, verify, and build on research results. Sociological research often relies on existing datasets like data from the ISSP, SOEP, or ESS. This makes sharing code a key pathway for methods transparency in sociology. Assessments of data and code availability exist for parts of sociology; however, they have so far relied on manual classification and are thus limited in their scope. The thesis builds on existing studies in sociology and political science to examine code and data availability in sociological research across a broad range of journals. It will first classify a subset of articles manually for aspects like the type of research, code availability, and data availability. The thesis will then evaluate automated classification using LLMs to classify a large range of publications. Finally, it will analyse the development of code and data sharing over time, and across subdomains within sociology.

Recommended reading

• An Empirical Assessment of Data Sharing and Computational Reproducibility in Sociology
• “It Must Be Very Hard to Publish Null Results”
• Code Sharing and Reproducibility in Survey-Based Social Research: Evidence from a Large-Scale Audit
• Care to Share? Experimental Evidence on Code Sharing Behavior in the Social Sciences
• ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications
• Assessment of Transparency Indicators across the Biomedical Literature: How Open Is Open?

Developing software has been a highly coveted craft in the last decades. The advent of coding agents has substantially shifted what it means to do software development. The thesis investigates how software developers make sense of their profession and its shifting boundaries. Potential questions to be further pursued as part of the thesis could be: What does it mean to be a software developer today? How has the practice of developing software professionally changed in the last few years? How does the shift in practice affect evaluation of performance, and ways of signaling credibility and competence?

Recommended reading

• The Stigma of Diseases: Unequal Burden, Uneven Decline
• The New York Times Magazine, 12 March 2026, on AI coding and programming jobs
• Boundary-Work and the Demarcation of Science from Non-Science: Strains and Interests in Professional Ideologies of Scientists
• Machtvolle Grenzen als konstitutive Momente des Sozialen