Stephanie Birkelbach

Understanding how online communities discuss and make sense of complex social issues is a central challenge in social media research, yet existing tools for large-scale discourse analysis are often closed-source, difficult to adapt, or limited to single analytical views. We present SocialPulse, an open-source subreddit sensemaking toolkit that unifies multiple complementary analyses -- topic modeling, sentiment analysis, user activity characterization, and bot detection -- within a single interactive system. SocialPulse enables users to fluidly move between aggregate trends and fine-grained content, compare highly active and long-tail contributors, and examine temporal shifts in discourse across subreddits. The demo showcases end-to-end exploratory workflows that allow researchers and practitioners to rapidly surface themes, participation patterns, and emerging dynamics in large Reddit datasets. By offering an extensible and openly available platform, SocialPulse provides a practical and reusable foundation for transparent, reproducible sensemaking of online community discourse.

@inproceedings{birkelbach26_socialpulse,
title = {{SocialPulse: An Open-Source Subreddit Sensemaking Toolkit}},
author = {Stephanie Birkelbach and Maria Teleki and Peter Carragher and Xiangjue Dong and Nehul Bhatnagar and James Caverlee
year = {2026},
booktitle = {arXiv}
}}

Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. We create a novel taxonomy of LLMs for story generation consisting of two major paradigms: (i) independent story generation by an LLM, and (ii) author-assistance for story generation -- a collaborative approach with LLMs supporting human authors. We compare existing works based on their methodology, datasets, generated story types, evaluation methods, and LLM usage. With a comprehensive survey, we identify potential directions for future work.

@inproceedings{teleki25_survey,
title = {{A Survey on LLMs for Story Generation}},
author = {Maria Teleki and Vedangi Bengali and Xiangjue Dong and Sai Tejas Janjur and Haoran Liu and Tian Liu and Cong Wang and Ting Liu and Yin Zhang and Frank Shipman and James Caverlee},
year = {2025},
booktitle = {EMNLP Findings}
}

Evaluating disfluency removal in speech requires more than aggregate token-level scores. Traditional word-based metrics such as precision, recall, and F1 (E-Scores) capture overall performance but cannot reveal why models succeed or fail. We introduce Z-Scores, a span-level linguistically-grounded evaluation metric that categorizes system behavior across distinct disfluency types (EDITED, INTJ, PRN). Our deterministic alignment module enables robust mapping between generated text and disfluent transcripts, allowing Z-Scores to expose systematic weaknesses that word-level metrics obscure. By providing category-specific diagnostics, Z-Scores enable researchers to identify model failure modes and design targeted interventions -- such as tailored prompts or data augmentation -- yielding measurable performance improvements. A case study with LLMs shows that Z-scores uncover challenges with INTJ and PRN disfluencies hidden in aggregate F1, directly informing model refinement strategies.

@inproceedings{teleki25_zscores,
title = {Z-Scores: A Metric for Linguistically Assessing Disfluency Removal},
author = {Maria Teleki and Sai Janjur and Haoran Liu and Oliver Grabner and Ketan Verma and Thomas Docog and Xiangjue Dong and Lingfeng Shi and Cong Wang and Stephanie Birkelbach and Jason Kim and Yin Zhang and James Caverlee},
year = {2025},
booktitle = {ICASSP},
}

LLMs serve as the backbone in SpeechLLMs, yet their behavior on spontaneous conversational input remains poorly understood. Conversational speech contains pervasive disfluencies -- interjections, edits, and parentheticals -- that are rare in the written corpora used for pre-training. Because gold disfluency removal is a deletion-only task, it serves as a controlled probe to determine whether a model performs faithful structural repair or biased reinterpretation. Using the DRES evaluation framework, we evaluate proprietary and open-source LLMs across architectures and scales. We show that model performance clusters into stable precision-recall regimes reflecting distinct ``editing policies.'' Notably, reasoning models systematically over-delete fluent content, revealing a bias toward semantic abstraction over structural fidelity. While fine-tuning achieves SOTA results, it harms generalization. Our findings demonstrate that robustness to speech is shaped by specific training objectives.

@inproceedings{teleki26_dres,
title = {Conversational Speech Reveals Structural Robustness Failures in SpeechLLM Backbones},
author = {Maria Teleki and Sai Janjur and Haoran Liu and Oliver Grabner and Ketan Verma and Thomas Docog and Xiangjue Dong and Lingfeng Shi and Cong Wang and Stephanie Birkelbach and Jason Kim and Yin Zhang and James Caverlee},
year = {2025},
}