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Found 68 papers, 25 papers with code
Identifying Collaborative Conversations using Latent Discourse Behaviors
In this work, we study collaborative online conversations.
Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts
Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access.
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory
Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic.
MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media
The way information is generated and disseminated has changed dramatically over the last decade.
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks
We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework.
Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks
Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in today’s society.
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy
The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion.
Uncovering Latent Themes of Messaging on Social Media by Integrating LLMs: A Case Study on Climate Campaigns
Furthermore, this method efficiently maps the text and the newly discovered themes, enhancing our understanding of the thematic nuances in social media messaging.
Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
Understanding the dynamics of counseling conversations is an important task, yet it is a challenging NLP problem regardless of the recent advance of Transformer-based pre-trained language models.
"We Demand Justice!": Towards Social Context Grounding of Political Texts
Social media discourse frequently consists of 'seemingly similar language used by opposing sides of the political spectrum', often translating to starkly contrasting perspectives.
"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction
We convert the text to a graph by breaking it into structured elements and connect it with the social network of authors, then structured prediction is done over the elements for identifying perspectives.
Interactively Learning Social Media Representations Improves News Source Factuality Detection
The rise of social media has enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs.
An Interactive Framework for Profiling News Media Sources
The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs.
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.
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
Climate change is the defining issue of our time, and we are at a defining moment.
Towards Few-Shot Identification of Morality Frames using In-Context Learning
Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge.
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair
KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates Abstract Syntax Trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases.
Towards Explaining Subjective Ground of Individuals on Social Media
Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior from text is far from being resolved.
Weakly Supervised Learning for Analyzing Political Campaigns on Facebook
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions.
Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision
In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world.
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.
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents
We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP.
Identifying Morality Frames in Political Tweets using Relational Learning
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions.
Twitter User Representation Using Weakly Supervised Graph Embedding
Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions.
Modeling Human Mental States with an Entity-based Narrative Graph
Understanding narrative text requires capturing characters' motivations, goals, and mental states.
Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model
Multiview representation learning of data can help construct coherent and contextualized users' representations on social media.
Randomized Deep Structured Prediction for Discourse-Level Processing
In this paper, we explore the use of randomized inference to alleviate this concern and show that we can efficiently leverage deep structured prediction and expressive neural encoders for a set of tasks involving complicated argumentative structures.
Understanding Politics via Contextualized Discourse Processing
In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that attempts to capture and leverage such information to generate more effective representations for entities, issues, and events.
Using Natural Language Relations between Answer Choices for Machine Comprehension
We use a stand-alone question answering (QA) system to perform QA task and a Natural Language Inference (NLI) system to identify the relations between the choice pairs.
Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information
Leveraging social media data to understand people's lifestyle choices is an exciting domain to explore but requires a multiview formulation of the data.
Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information
Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state related to yoga in social media.
``where is this relationship going?'': Understanding Relationship Trajectories in Narrative Text
We examine a new commonsense reasoning task: given a narrative describing a social interaction that centers on two protagonists, systems make inferences about the underlying relationship trajectory.
Predicting Stance Change Using Modular Architectures
The ability to change a person{'}s mind on a given issue depends both on the arguments they are presented with and on their underlying perspectives and biases on that issue.
Cross-Lingual Document Retrieval with Smooth Learning
Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language.
Semi-supervised Autoencoding Projective Dependency Parsing
We describe two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing.
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations
Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types.
"where is this relationship going?": Understanding Relationship Trajectories in Narrative Text
We examine a new commonsense reasoning task: given a narrative describing a social interaction that centers on two protagonists, systems make inferences about the underlying relationship trajectory.
Modeling Content and Context with Deep Relational Learning
Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies.
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media
In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics.
Semi-supervised Parsing with a Variational Autoencoding Parser
We propose an end-to-end variational autoencoding parsing (VAP) model for semi-supervised graph-based projective dependency parsing.
Understanding the Language of Political Agreement and Disagreement in Legislative Texts
While national politics often receive the spotlight, the overwhelming majority of legislation proposed, discussed, and enacted is done at the state level.
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors.
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness
We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements.
Multi-Relational Script Learning for Discourse Relations
Modeling script knowledge can be useful for a wide range of NLP tasks.
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task.
Sentiment Tagging with Partial Labels using Modular Architectures
Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label.
Modeling Behavioral Aspects of Social Media Discourse for Moral Classification
Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U. S. politics.
Improving Natural Language Interaction with Robots Using Advice
Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain.
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics.
Structured Representation Learning for Online Debate Stance Prediction
Online debates can help provide valuable information about various perspectives on a wide range of issues.
Classification of Moral Foundations in Microblog Political Discourse
Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text.
Semi-supervised Structured Prediction with Neural CRF Autoencoder
In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems.
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
Our proposed system consists of two subsystems and one regression model for predicting STS scores.
Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter
Politicians carefully word their statements in order to influence how others view an issue, a political strategy called framing.
Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter
Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues.
``All I know about politics is what I read in Twitter'': Weakly Supervised Models for Extracting Politicians' Stances From Twitter
During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events.
Understanding Satirical Articles Using Common-Sense
Automatic satire detection is a subtle text classification task, for machines and at times, even for humans.
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding.
