activity
20182026
most citedKeyword Assisted Embedded Topic Model

24 citations · 27 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare

Man Luo, Bahareh Harandizadeh, Amara Tariq +5

Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of cli…

cs.CL2024

Risk and Response in Large Language Models: Evaluating Key Threat Categories

Bahareh Harandizadeh, Abel Salinas, Fred Morstatter

This paper explores the pressing issue of risk assessment in Large Language Models (LLMs) as they become increasingly prevalent in various applications. Focusing on how reward mode…

cs.CL2022

Noise Audits Improve Moral Foundation Classification

Negar Mokhberian, Frederic R. Hopp, Bahareh Harandizadeh +2

Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed…

cs.IR2021★ 24 cited

Keyword Assisted Embedded Topic Model

Bahareh Harandizadeh, J. Hunter Priniski, Fred Morstatter

By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents. Probabilistic…

cs.SI2021★ 3 cited

Mapping Moral Valence of Tweets Following the Killing of George Floyd

J. Hunter Priniski, Negar Mokhberian, Bahareh Harandizadeh +4

The viral video documenting the killing of George Floyd by Minneapolis police officer Derek Chauvin inspired nation-wide protests that brought national attention to widespread raci…

cs.HC2018

Crowd-Machine Collaboration for Item Screening

Evgeny Krivosheev, Bahareh Harandizadeh, Fabio Casati +1

In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in…