Search Results for author:
Found 28 papers, 6 papers with code
Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021
Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently.
Nonsuicidal Self-Injury and Substance Use Disorders: A Shared Language of Addiction
To this end, we build a set of LDA topics across both NSSI and SUD Reddit users and show that shared language across the two domains includes SUD recovery language in addition to other themes common to support forums (e. g., requests for help and gratitude).
Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time.
The Illusion of Empathy: How AI Chatbots Shape Conversation Perception
As AI chatbots become more human-like by incorporating empathy, understanding user-centered perceptions of chatbot empathy and its impact on conversation quality remains essential yet under-explored.
DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversity
On average, we observe a 54 percent reduction in the error of average features between human and LLM-generated conversations.
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas
Thus, it may be the case that employing LLMs (which do not have such human factors) in these tasks results in a lack of variation in data, failing to reflect the diversity of human experiences.
Vernacular? I Barely Know Her: Challenges with Style Control and Stereotyping
Large Language Models (LLMs) are increasingly being used in educational and learning applications.
Building Knowledge-Guided Lexica to Model Cultural Variation
Cultural variation exists between nations (e. g., the United States vs. China), but also within regions (e. g., California vs. Texas, Los Angeles vs. San Francisco).
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data.
Historical patterns of rice farming explain modern-day language use in China and Japan more than modernization and urbanization
We used natural language processing to analyze a billion words to study cultural differences on Weibo, one of China's largest social media platforms.
Psychological Metrics for Dialog System Evaluation
We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens in which conversational agents express a diversity of both states (e. g., emotion) and traits (e. g., personality), just as people do.
Robust language-based mental health assessments in time and space through social media
Compared to physical health, population mental health measurement in the U. S. is very coarse-grained.
Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances
Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing.
Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community
In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i. e., who is "the asshole").
Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models
Adaptive mobile device-based health interventions often use machine learning models trained on non-mobile device data, such as social media text, due to the difficulty and high expense of collecting large text message (SMS) data.
Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency
As the frequency of Black names on social media is strongly correlated with Black Americans' prevalence in the population, this results in spurious anti-Black WEAT estimates wherever few Black Americans live.
Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021
Black Lives Matter (BLM) is a decentralized social movement protesting violence against Black individuals and communities, with a focus on police brutality.
Quantifying Community Characteristics of Maternal Mortality Using Social Media
While most mortality rates have decreased in the US, maternal mortality has increased and is among the highest of any OECD nation.
Correcting Sociodemographic Selection Biases for Population Prediction from Social Media
However, social media users are not typically a representative sample of the intended population -- a "selection bias".
Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes
NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental.
Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
Mental health predictive systems typically model language as if from a single context (e. g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e. g. either the message-level or user-level).
Residualized Factor Adaptation for Community Social Media Prediction Tasks
Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e. g. age, education rates, race) of the community from which the language originates.
Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the CLPysch 2018 Shared Task
The goal of the shared task was to use childhood language as a marker for both current and future psychological health over individual lifetimes.
DLATK: Differential Language Analysis ToolKit
We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses.
On the Distribution of Lexical Features at Multiple Levels of Analysis
Natural language processing has increasingly moved from modeling documents and words toward studying the people behind the language.
