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Found 28 papers, 7 papers with code
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic
Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states.
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection.
ChatGPT and Bard Responses to Polarizing Questions
Broadly, our results indicated a left-leaning bias for both ChatGPT and Bard, with Bard more likely to provide responses around polarizing topics.
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore
While several countries have adopted total defence as their defence policy, very few studies have investigated its effectiveness.
Decoding the Underlying Meaning of Multimodal Hateful Memes
Recent studies have proposed models that yielded promising performance for the hateful meme classification task.
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation
A key concern is that these explanations, generated by LLMs, may lead to erroneous judgments about the nature of flagged content by both users and content moderators.
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer
Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled data required for the specific task.
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
To address the calculation errors and improve the quality of generated reasoning steps, we extend PS prompting with more detailed instructions and derive PS+ prompting.
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
To enable further research on PEFT methods of LLMs, this paper presents LLM-Adapters, an easy-to-use framework that integrates various adapters into LLMs and can execute these adapter-based PEFT methods of LLMs for different tasks.
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection
However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages.
Prompting for Multimodal Hateful Meme Classification
Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pre-trained RoBERTa language model for hateful meme classification.
Predicting Hate Intensity of Twitter Conversation Threads
Tweets are the most concise form of communication in online social media, wherein a single tweet has the potential to make or break the discourse of the conversation.
On Explaining Multimodal Hateful Meme Detection Models
For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i. e., image and text) of the hateful memes.
Will You Dance To The Challenge? Predicting User Participation of TikTok Challenges
This paper investigates social contagion of TikTok challenges through predicting a user's participation.
Improving Text Auto-Completion with Next Phrase Prediction
Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion task.
AutoChart: A Dataset for Chart-to-Text Generation Task
The analytical description of charts is an exciting and important research area with many applications in academia and industry.
Syntax Matters! Syntax-Controlled in Text Style Transfer
Existing text style transfer (TST) methods rely on style classifiers to disentangle the text's content and style attributes for text style transfer.
Disentangling Hate in Online Memes
Our experiment results show that DisMultiHate is able to outperform state-of-the-art unimodal and multimodal baselines in the hateful meme classification task.
On Orthogonality Constraints for Transformers
Orthogonality constraints encourage matrices to be orthogonal for numerical stability.
DeepStyle: User Style Embedding for Authorship Attribution of Short Texts
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications.
DeepHate: Hate Speech Detection via Multi-Faceted Text Representations
Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies.
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection
Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community.
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection
We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.
Text Style Transfer: A Review and Experimental Evaluation
This article aims to provide a comprehensive review of recent research efforts on text style transfer.
On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities.
Graph-to-Tree Learning for Solving Math Word Problems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.
Ranked #7 on
Math Word Problem Solving
on MAWPS
On Analyzing Annotation Consistency in Online Abusive Behavior Datasets
In this study, we proposed an analytical framework to study the annotation consistency in online hate and abusive content datasets.
Keen2Act: Activity Recommendation in Online Social Collaborative Platforms
The Act step then recommends to the user which activities to perform on the identified set of items.
