Allison Lahnala

University of Bonn

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Charles Welch

IT University of Copenhagen

Lucie Flek

University of Bonn

Rada Mihalcea

University of North Texas

Ken Resnicow

University of Michigan–Ann Arbor

Verónica Pérez-Rosas

University of Michigan–Ann Arbor

Jonathan K. Kummerfeld

University of Sydney

AÇ

Anıl Çamcı

University of Michigan–Ann Arbor

David Jurgens

University of Michigan–Ann Arbor

Lawrence C. An

University of Michigan–Ann Arbor

Larry An

University of Michigan–Ann Arbor

Cooperative Institutions

University of Michigan–Ann Arbor

Philipps University of Marburg

University of Pennsylvania

Delft University of Technology

Johns Hopkins University

IT University of Copenhagen

Tokyo Institute of Technology

Administration for Community Living

American Jewish Committee

Ann Arbor Center for Independent Living

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Expressive Interviewing: A Conversational System for Coping with COVID-19

arXiv (Cornell University) (2020)

Charles Welch Allison Lahnala Verónica Pérez-Rosas Siqi Shen Sarah Seraj

The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce \textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system's design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.

Interview

Isolation

Pandemic

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A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing

Allison Lahnala Charles Welch David Jurgens Lucie Flek

We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility. Moreover, (3) emotional empathy is overemphasized, skewing our focus to a narrow subset of simplified tasks. We believe these issues hinder research progress and argue that current directions will benefit from a clear conceptualization that includes operationalizing cognitive empathy components. Our main objectives are to provide insight and guidance on empathy conceptualization for NLP research objectives and to encourage researchers to pursue the overlooked opportunities in this area, highly relevant, e.g., for clinical and educational sectors.

10.18653/v1/2022.findings-emnlp.157

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The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs

arXiv (Cornell University) (2025)

Allison Lahnala Charles Welch David Jurgens Lucie Flek

Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with different theoretical groundings. We study (1) the dimensionality of empathy definitions, (2) the correspondence between the defined dimensions and measured/observed properties, and (3) the conduciveness of the data to represent them, finding they have a significant impact to performance compared to other transfer setting features. Characterizing the theoretical grounding of empathy tasks as direct, abstract, or adjacent further indicates that tasks that directly predict specified empathy components have higher transferability. Our work provides empirical evidence for the need for precise and multidimensional empathy operationalizations.

10.48550/arxiv.2501.14981

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Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

arXiv (Cornell University) (2021)

Allison Lahnala Gauri Kambhatla Jiajun Peng Matthew T. Whitehead Gillian Minnehan

Natural language processing methods have been applied in a variety of music studies, drawing the connection between music and language. In this paper, we expand those approaches by investigating \textit{chord embeddings}, which we apply in two case studies to address two key questions: (1) what musical information do chord embeddings capture?; and (2) how might musical applications benefit from them? In our analysis, we show that they capture similarities between chords that adhere to important relationships described in music theory. In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians. In the second case study, we show the potential benefits of using the representations in tasks related to musical stylometrics.

Chord (peer-to-peer)

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A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing

arXiv (Cornell University) (2022)

Allison Lahnala Charles Welch David Jurgens Lucie Flek

10.48550/arxiv.2210.16604

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Citations (1)

Modeling Proficiency with Implicit User Representations

arXiv (Cornell University) (2021)

Kim Breitwieser Allison Lahnala Charles Welch Lucie Flek Martin Potthast

We introduce the problem of proficiency modeling: Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency. This enables the filtering and ranking of social media posts on a given topic as per user proficiency. Unlike experts on a given topic, proficient users may not have received formal training and possess years of practical experience, but may be autodidacts, hobbyists, and people with sustained interest, enabling them to make genuine and original contributions to discourse. While predicting whether a user is an expert on a given topic imposes strong constraints on who is a true positive, proficiency modeling implies a graded scoring, relaxing these constraints. Put another way, many active social media users can be assumed to possess, or eventually acquire, some level of proficiency on topics relevant to their community. We tackle proficiency modeling in an unsupervised manner by utilizing user embeddings to model engagement with a given topic, as indicated by a user's preference for authoring related content. We investigate five alternative approaches to model proficiency, ranging from basic ones to an advanced, tailored user modeling approach, applied within two real-world benchmarks for evaluation.

User Engagement

10.48550/arxiv.2110.08011

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Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization

arXiv (Cornell University) (2025)

Allison Lahnala Vasudha Varadarajan Lucie Flek H. Andrew Schwartz Ryan L. Boyd

The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across a range of online community forums. By focusing on verbal behavioral signatures of extremist traits, we develop a framework for quantifying extremism at both user and community levels. Our research identifies 11 distinct factors, which we term ``The Extremist Eleven,'' as a generalized psychosocial model of extremism. Applying our method to various online communities, we demonstrate an ability to characterize ideologically diverse communities across the 11 extremist traits. We demonstrate the power of this method by analyzing user histories from members of the incel community. We find that our framework accurately predicts which users join the incel community up to 10 months before their actual entry with an AUC of $>0.6$, steadily increasing to AUC ~0.9 three to four months before the event. Further, we find that upon entry into an extremist forum, the users tend to maintain their level of extremism within the community, while still remaining distinguishable from the general online discourse. Our findings contribute to the study of extremism by introducing a more holistic, cross-ideological approach that transcends traditional, trait-specific models.

Radicalization

Violent extremism

10.48550/arxiv.2501.04820

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Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion

arXiv (Cornell University) (2022)

Flora Sakketou Allison Lahnala Liane Vogel Lucie Flek

There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle opinion fluctuations and detecting fine-grained stances. The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term. All posts are annotated by non-experts and a significant portion of the data is also annotated by experts. We provide a strategy for recruiting suitable non-experts. Our analysis of the inter-annotator agreements shows that the resulting annotations obtained from the majority vote of the non-experts are of comparable quality to the annotations of the experts. We provide analyses of the stance evolution in short term and long term levels, a comparison of language usage between users with vacillating and resolute attitudes, and fine-grained stance detection baselines.

Sentiment Analysis

Radicalization

Polarity (international relations)

10.48550/arxiv.2204.10190

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Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

arXiv (Cornell University) (2021)

Allison Lahnala Gauri Kambhatla Jiajun Peng Matthew T. Whitehead Gillian Minnehan

Chord (peer-to-peer)

10.48550/arxiv.2102.02917

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Expressive Interviewing: A Conversational System for Coping with COVID-19

Charles Welch Allison Lahnala Verónica Pérez-Rosas Siqi Shen Sarah Seraj

Charles Welch, Allison Lahnala, Veronica Perez-Rosas, Siqi Shen, Sarah Seraj, Larry An, Kenneth Resnicow, James Pennebaker, Rada Mihalcea. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. 2020.

2019-20 coronavirus outbreak

Interview

10.18653/v1/2020.nlpcovid19-2.6

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Citations (24)