Search Results for author:
Found 40 papers, 13 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.
All in an Aggregated Image for In-Image Learning
This paper introduces a new in-context learning (ICL) mechanism called In-Image Learning (I$^2$L) that combines demonstration examples, visual cues, and chain-of-thought reasoning into an aggregated image to enhance the capabilities of Large Multimodal Models (e. g., GPT-4V) in multimodal reasoning tasks.
MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation
Online memes have emerged as powerful digital cultural artifacts in the age of social media, offering not only humor but also platforms for political discourse, social critique, and information dissemination.
Modularized Networks for Few-shot Hateful Meme Detection
We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
Prompting Large Language Models for Topic Modeling
Topic modeling is a widely used technique for revealing underlying thematic structures within textual data.
MATK: The Meme Analytical Tool Kit
The rise of social media platforms has brought about a new digital culture called memes.
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models
The proliferation of social media has given rise to a new form of communication: memes.
SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis
In this paper, we propose the SBTRec algorithm: a BERT-based Trajectory Recommendation with sentiment analysis, for recommending personalized sequences of POIs as itineraries.
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts
Visual question answering (VQA) is the task of answering questions about an image.
BTRec: BERT-Based Trajectory Recommendation for Personalized Tours
An essential task for tourists having a pleasant holiday is to have a well-planned itinerary with relevant recommendations, especially when visiting unfamiliar cities.
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care
The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk.
Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification
Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection, and identification of deceptive content.
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
The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e. g., ChatDoctor) or instruction data (e. g., Alpaca).
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.
Ranked #3 on
Hateful Meme Classification
on HarMeme
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.
Ranked #4 on
Hateful Meme Classification
on HarMeme
On Orthogonality Constraints for Transformers
Orthogonality constraints encourage matrices to be orthogonal for numerical stability.
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.
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.
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 #10 on
Math Word Problem Solving
on Math23K
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.
