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Found 50 papers, 21 papers with code
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
The promise of ICL is that the LLM can adapt to perform the present task at a competitive or state-of-the-art level at a fraction of the cost.
Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators.
How Susceptible are Large Language Models to Ideological Manipulation?
Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information.
Whose Emotions and Moral Sentiments Do Language Models Reflect?
We define the problem of affective alignment, which measures how LMs' emotional and moral tone represents those of different groups.
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities.
Characterizing Online Eating Disorder Communities with Large Language Models
We argue that social media platforms create a feedback loop that amplifies the growth of content and communities that promote eating disorders like anorexia and bulimia.
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Social media platforms are rife with politically charged discussions.
Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Previous studies have shed light on the pitfalls of label aggregation and have introduced a handful of practical approaches to tackle this issue.
Discovering collective narratives shifts in online discussions
Narrative is a foundation of human cognition and decision making.
CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities
Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions.
A Data Fusion Framework for Multi-Domain Morality Learning
Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life.
Data-Driven Estimation of Heterogeneous Treatment Effects
In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect.
ALCAP: Alignment-Augmented Music Captioner
Music captioning has gained significant attention in the wake of the rising prominence of streaming media platforms.
Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit
We collect discussions from a wide variety of topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc.
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
In this work, we study how we can build a single model that can transition between these different configurations by leveraging multilingual models and Demux, a transformer-based model whose input includes the emotions of interest, enabling us to dynamically change the emotions predicted by the model.
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM).
Noise Audits Improve Moral Foundation Classification
Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance.
Infusing Knowledge from Wikipedia to Enhance Stance Detection
Stance detection infers a text author's attitude towards a target.
Zero-shot meta-learning for small-scale data from human subjects
While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled.
Emergent Instabilities in Algorithmic Feedback Loops
In this work, we explore algorithmic confounding in collaborative filtering-based recommendation algorithms through teacher-student learning simulations.
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry.
Speaker Turn Modeling for Dialogue Act Classification
Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue.
Ranked #2 on
Dialogue Act Classification
on Switchboard corpus
DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data
Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data.
Pattern Discovery in Time Series with Byte Pair Encoding
The growing popularity of wearable sensors has generated large quantities of temporal physiological and activity data.
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings
We apply our method to a dataset of news articles about the COVID-19 pandemic.
Individualized Context-Aware Tensor Factorization for Online Games Predictions
Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time.
Inherent Trade-offs in the Fair Allocation of Treatments
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups.
Origins of Algorithmic Instabilities in Crowdsourced Ranking
We use this data to construct a model that quantifies how judgement heuristics and option quality combine when deciding between two options.
Social and Information Networks Human-Computer Interaction
Challenges in Forecasting Malicious Events from Incomplete Data
As we show in this paper, the process of filtering reduces the predictability of cyber-attacks.
TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers
We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings.
COVID-19: The First Public Coronavirus Twitter Dataset
At the time of this writing, the novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around the world.
Social and Information Networks Populations and Evolution
Learning Behavioral Representations from Wearable Sensors
Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors.
Learning Fair and Interpretable Representations via Linear Orthogonalization
To reduce human error and prejudice, many high-stakes decisions have been turned over to machine algorithms.
Follow the Leader: Documents on the Leading Edge of Semantic Change Get More Citations
However, simply knowing that a word has changed in meaning is insufficient to identify the instances of word usage that convey the historical or the newer meaning.
A Survey on Bias and Fairness in Machine Learning
With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them.
Discovering Hidden Structure in High Dimensional Human Behavioral Data via Tensor Factorization
In recent years, the rapid growth in technology has increased the opportunity for longitudinal human behavioral studies.
Friendship Paradox Biases Perceptions in Directed Networks
For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i. e., its global popularity---can be dramatically different from how people see it in their social feeds---i. e., its perceived popularity---where the feeds aggregate their friends' posts.
Social and Information Networks Physics and Society
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships.
Characterizing Activity on the Deep and Dark Web
The deep and darkweb (d2web) refers to limited access web sites that require registration, authentication, or more complex encryption protocols to access them.
Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors.
Discovering Signals from Web Sources to Predict Cyber Attacks
Cyber attacks are growing in frequency and severity.
Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data
We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data.
Predicting Cyber Events by Leveraging Hacker Sentiment
We propose a novel approach to predict cyber events using sentiment analysis.
Can you Trust the Trend: Discovering Simpson's Paradoxes in Social Data
We present a statistical method to automatically identify Simpson's paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups.
Computers and Society
Network Vector: Distributed Representations of Networks with Global Context
We propose a neural embedding algorithm called Network Vector, which learns distributed representations of nodes and the entire networks simultaneously.
The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange
Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept.
The DARPA Twitter Bot Challenge
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases.
Social and Information Networks Computers and Society
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media
We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data.
Spectral Clustering with Epidemic Diffusion
Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs.
