ToxiCloakCN introduces a novel dataset designed to evaluate the robustness of offensive language detection models against cloaking techniques such as homophone substitutions and emoji transformations. Targeting Chinese-language content, this work highlights the vulnerabilities of large language models (LLMs) in detecting cloaked offensive texts, particularly in cases involving racism, sexism, regional bias, and anti-LGBTQ+ rhetoric. The study reveals significant performance declines for state-of-the-art models, including GPT-4, when confronted with perturbed data. ToxiCloakCN underscores the need for advanced techniques to combat evolving evasion strategies, contributing to safer online environments and more robust content moderation systems. Explore the findings to learn how this work advances offensive language detection in complex linguistic settings.

12 November 2024

MultiHateClip introduces a novel multilingual benchmark dataset designed for hateful video detection, focusing on gender-based hate speech across English and Chinese contexts. By annotating 2,000 videos from YouTube and Bilibili for hatefulness, offensiveness, and normalcy, it provides insights into cross-cultural differences in multimodal hate speech dynamics. Leveraging state-of-the-art models like GPT-4V and mBERT ⊙ MFCC ⊙ ViViT, the study highlights the complexities in differentiating hateful from offensive content and the limitations of existing models in non-Western contexts. MultiHateClip paves the way for a more inclusive, culturally nuanced approach to online hate detection. Explore the dataset and findings to advance multimodal analysis for combating online hate speech.

28 October 2024

InstructAV introduces a groundbreaking approach to authorship verification (AV) by fine-tuning large language models with instructions and leveraging a parameter-efficient fine-tuning (PEFT) method. This framework is distinct in its dual focus on improving classification accuracy and generating clear, detailed linguistic explanations, addressing a significant gap in the AV domain. By incorporating datasets with explanatory labels and employing the LoRA fine-tuning technique, InstructAV achieves state-of-the-art performance, as demonstrated across diverse datasets like IMDB, Twitter, and Yelp Reviews. This innovative methodology not only enhances the transparency and reliability of AV systems but also paves the way for advancements in explainable AI. Explore how InstructAV is shaping the future of authorship verification.

10 July 2024

SGHateCheck is a pioneering framework designed to address the unique challenges of hate speech detection in Singapore's multilingual and culturally diverse landscape. By building on existing methodologies like HateCheck and MHC, SGHateCheck introduces functional tests for four key languages—Singlish, Mandarin, Malay, and Tamil. This project highlights the limitations of state-of-the-art models in accurately moderating content in Southeast Asian contexts, driving the development of more inclusive and effective hate speech detection systems. Explore how SGHateCheck is shaping the future of online trust and safety in low-resource language settings. This project is support by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 2.

20 June 2024

MemeCraft is an innovative project that harnesses the power of generative AI to create impactful memes for social good. Developed in SUTD, this tool uses advanced language and visual models to generate memes that support important social movements like climate action and gender equality. By ensuring that the generated content is both humorous and respectful, MemeCraft aims to engage online audiences in meaningful discourse. This versatile technology can also be applied in other social activism and marketing campaigns, making it a powerful tool for spreading awareness and promoting positive change across various causes.

13 May 2024

AutoChart represents a significant advancement in natural language generation by addressing the underexplored area of chart-to-text generation. By introducing a novel framework that automatically generates analytical descriptions for various types of charts, AutoChart enables scalable and efficient data interpretation. The dataset, consisting of over 10,000 chart-description pairs, facilitates research in both natural language processing and computer vision, offering applications in academic writing, accessibility for visually impaired users, and automated report generation. AutoChart's integration of linguistic rhetorical moves further ensures that the generated descriptions are not only informative but also coherent and contextually relevant—paving the way for innovative applications in education, journalism, and automated content generation. Preliminary research in this area is funded by Living Sky Technologies.

16 Aug 2021

In the wake of the COVID-19 pandemic, online platforms saw an alarming rise in antisocial behaviors, including hate speech and xenophobia. Our project tackles this issue head-on by developing one of the largest annotated datasets of over 40 million COVID-19-related tweets. Using a novel automated annotation framework, we analyzed toxic content targeting vulnerable communities, shedding light on new abusive lexicons that emerged during the pandemic. This research opens up pathways for developing more robust tools to monitor and curb harmful online behaviors during global crises. We also partner with the Saskatchewan Human Rights Commission in a sub-project to investigate the online xenophobia against Asian communities during the COVID-19 pandemic. This sub-project is funded by the Social Science and Humanities Research Council of Canada Partnership Engage Grants.

21 July 2020

Online social platforms (OSPs), such as Facebook, Twitter, and Instagram, have grown monumentally over recent years. It was reported that as of August 2017, Facebook has over 2 billion monthly active users, while Instagram and Twitter have over 700 million and 300 million monthly active user accounts, respectively. The vast amount of user-generated content and social data gathered in these behemoth platforms have made them rich data sources for academic and industrial research. However, most of the existing research work has focused on analyzing and modeling user behaviors in a single platform setting, neglecting the inter-dependencies of user behaviors across multiple OSPs. In this project, we design novel techniques that enable the analysis and modeling of user behaviors in multiple OSPs. In particular, we have developed algorithms that (i) link users' profiles across multiple social platforms, (ii) analyze users' topical interests and platform preferences across multiple OSPs, and (iii) model influential users in multiple OSPs.