Search Results for author:
Found 78 papers, 15 papers with code
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).
Measuring the Language of Self-Disclosure across Corpora
Being able to reliably estimate self-disclosure – a key component of friendship and intimacy – from language is important for many psychology studies.
Item Response Theory for Efficient Human Evaluation of Chatbots
Conversational agent quality is currently assessed using human evaluation, and often requires an exorbitant number of comparisons to achieve statistical significance.
TopEx: Topic-based Explanations for Model Comparison
Meaningfully comparing language models is challenging with current explanation methods.
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Experts across diverse disciplines are often interested in making sense of large text collections.
Conceptor-Aided Debiasing of Large Language Models
We propose two methods of applying conceptors (1) bias subspace projection by post-processing; and (2) a new architecture, conceptor-intervened BERT (CI-BERT), which explicitly incorporates the conceptor projection into all layers during training.
StyLEx: Explaining Style Using Human Lexical Annotations
Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021).
Empathic Conversations: A Multi-level Dataset of Contextualized Conversations
Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed.
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions.
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.
Social Media Reveals Urban-Rural Differences in Stress across China
This paper studies linguistic differences in the experiences and expressions of stress in urban-rural China from Weibo posts from over 65, 000 users across 329 counties using hierarchical mixed-effects models.
Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica
People convey their intention and attitude through linguistic styles of the text that they write.
WikiTalkEdit: A Dataset for modeling Editors' behaviors on Wikipedia
We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor.
Predicting Responses to Psychological Questionnaires from Participants' Social Media Posts and Question Text Embeddings
Finally, as a side contribution, the success of our model also suggests a new approach to study survey questions using NLP tools such as text embeddings rather than response data used in traditional methods.
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments
Although the prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties.
Studying Politeness across Cultures Using English Twitter and Mandarin Weibo
We study the linguistic features associated with politeness across US English and Mandarin Chinese.
Social and Information Networks Computers and Society
Generalized SHAP: Generating multiple types of explanations in machine learning
Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output.
Learning Word Ratings for Empathy and Distress from Document-Level User Responses
The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods.
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".
Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
In this work we exploit a newly-created Arabic dataset with ground truth age and gender labels to learn these attributes both individually and in a multi-task setting at the sentence level.
DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect
Prediction of language varieties and dialects is an important language processing task, with a wide range of applications.
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations
Systemic bias in word embeddings has been widely reported and studied, and efforts made to debias them; however, new contextualized embeddings such as ELMo and BERT are only now being similarly studied.
Conceptor Debiasing of Word Representations Evaluated on WEAT
Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias.
ChatEval: A Tool for Chatbot Evaluation
We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems.
Continual Learning for Sentence Representations Using Conceptors
Distributed representations of sentences have become ubiquitous in natural language processing tasks.
Understanding and Measuring Psychological Stress using Social Media
In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data.
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Word vectors are at the core of many natural language processing tasks.
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data.
Identifying Locus of Control in Social Media Language
Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances.
Modeling Empathy and Distress in Reaction to News Stories
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction.
Unsupervised Morphology Learning with Statistical Paradigms
This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e. g., suffixes) that can be applied to a homogeneous set of words (e. g., nouns or verbs).
User-Level Race and Ethnicity Predictors from Twitter Text
User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias.
Diachronic degradation of language models: Insights from social media
It asks the question: given that the social media platform and its users remain the same, how is language changing over time?
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.
Enabling Deep Learning of Emotion With First-Person Seed Expressions
The computational treatment of emotion in natural language text remains relatively limited, and Arabic is no exception.
Multiscale Hidden Markov Models For Covariance Prediction
This paper presents a novel variant of hierarchical hidden Markov models (HMMs), the multiscale hidden Markov model (MSHMM), and an associated spectral estimation and prediction scheme that is consistent, finds global optima, and is computationally efficient.
Neural Tree Transducers for Tree to Tree Learning
We introduce a novel approach to tree-to-tree learning, the neural tree transducer (NTT), a top-down depth first context-sensitive tree decoder, which is paired with recursive neural encoders.
Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions
Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires.
Assessing Objective Recommendation Quality through Political Forecasting
Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility.
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.
Controlling Human Perception of Basic User Traits
Much of our online communication is text-mediated and, lately, more common with automated agents.
Enterprise to Computer: Star Trek chatbot
In this work, we propose a design for a chatbot that captures the "style" of Star Trek by incorporating references from the show along with peculiar tones of the fictional characters therein.
Domain Aware Neural Dialog System
We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains.
Personality Driven Differences in Paraphrase Preference
Personality plays a decisive role in how people behave in different scenarios, including online social media.
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage.
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.
EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks
Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives.
Beyond Binary Labels: Political Ideology Prediction of Twitter Users
Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US.
Semantic Word Clusters Using Signed Spectral Clustering
For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights.
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use.
Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering
Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains.
An Empirical Exploration of Moral Foundations Theory in Partisan News Sources
News sources frame issues in different ways in order to appeal or control the perception of their readers.
Semantic Word Clusters Using Signed Normalized Graph Cuts
Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other.
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform
We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations ($p \gg n$).
New Subsampling Algorithms for Fast Least Squares Regression
We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$).
