Xiaoyi (Mimi) Chen

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I am an MSc Student in Social Data Science at the University of Oxford, where my research focuses on understanding online communication and political polarisation through big data and machine learning.

Portrait

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Categorical Online Sexism in Political Discourse: Transformer Classification of Tweets from the 2024 U.S. Presidential Election

Spring 2025 • University of Oxford
PythonLLMsNLP

A study of categorical sexism in tweets about Kamala Harris from the 2024 U.S. Presidential Election using a domain-adapted a DeBERTa model with continued pretraining and fine-tuning. Detected and classified hierarchical sexism in 1.9 million tweets to understand how backlash theory manifests in the public arena.

Code and paper available upon request

Quantifying Polarisation in Guest Sharing Patterns of Popular Political Podcasts

Spring 2025 • University of Oxford
PythonNetwork AnalysisWeb Scraping

An analysis of ideological polarisation in the political podcast ecosystem through a network of hosts and their guests with community detection via both projected and bipartite modularity maximization. Demonstrated how elite actors contribute to affective polarisation in the digital public sphere.

Code and paper available upon request

Heterogeneity in Drivers of Higher Education Attainment: Evidence from 21st Century China

Fall 2024 • University of Oxford
PythonPanel DataStatistics

An empirical analysis of the regional and temporal variations in drivers of higher education attainment in 21st century China using fixed effects panel regression

View Paper

Anime Genre Classification with Convolutional Neural Networks

Fall 2023 • New York University
PythonCNNClassification

Multi-label genre classification of anime poster images. Evaluated performance of a custom Convolutional Neural Network (CNN), transfer learning (ResNet50), semi-supervised learning, and AutoML methods

View on GitHub

The Food Delivery Dilemma: Classification and Evaluation of Real vs. Ghost Kitchens

Spring 2023 • New York University
PythonNLPClassification

A Natural Language Processing (NLP) project that classifies ghost kitchens based on menu item descriptions scraped from food delivery platforms through text embeddings and logistic regression

View on GitHub

Spotify Music Genre Classification

Spring 2023 • New York University
PythonMachine LearningClassification

A multi-class genre classification pipeline for Spotify tracks using engineered audio features with XGBoost. Utilised K-means clustering to explore underlying structure in the data, revealing genre overlaps and latent groupings.

View on GitHub

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I am always open to new opportunities and collaborations! Feel free to reach out to me through the form below or connect with me on LinkedIn, whether you have a question, want to discuss research, or just say hi.