Exploring Gaze Pattern in Autistic Children: Clustering, Visualization, and Prediction

Autism Spectrum Disorder (ASD) significantly affects the social and communication abilities of children, and eye-tracking is commonly used as a diagnostic tool by identifying associated atypical gaze patterns. Traditional methods demand manual identification of Areas of Interest in gaze patterns, lowering the performance of gaze behavior analysis in ASD subjects. To tackle this limitation, we propose a novel method to automatically analyze gaze behaviors in ASD children with superior accuracy. To be specific, we first apply and optimize seven clustering algorithms to automatically group gaze points to compare ASD subjects with typically developing peers. Subsequently, we extract 63 significant features to fully describe the patterns. These features can describe correlations between ASD diagnosis and gaze patterns. Lastly, using these features as prior knowledge, we train multiple predictive machine learning models to predict and diagnose ASD based on their gaze behaviors. To evaluate our method, we apply our method to three ASD datasets. The experimental and visualization results demonstrate the improvements of clustering algorithms in the analysis of unique gaze patterns in ASD children. Additionally, these predictive machine learning models achieved state-of-the-art prediction performance ($81\%$ AUC) in the field of automatically constructed gaze point features for ASD diagnosis. Our code is available at \url{https://github.com/username/projectname}.

ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations

Detecting hate speech and offensive language is essential for maintaining a safe and respectful digital environment. This study examines the limitations of state-of-the-art large language models (LLMs) in identifying offensive content within systematically perturbed data, with a focus on Chinese, a language particularly susceptible to such perturbations. We introduce \textsf{ToxiCloakCN}, an enhanced dataset derived from ToxiCN, augmented with homophonic substitutions and emoji transformations, to test the robustness of LLMs against these cloaking perturbations. Our findings reveal that existing models significantly underperform in detecting offensive content when these perturbations are applied. We provide an in-depth analysis of how different types of offensive content are affected by these perturbations and explore the alignment between human and model explanations of offensiveness. Our work highlights the urgent need for more advanced techniques in offensive language detection to combat the evolving tactics used to evade detection mechanisms.

Towards Understanding Emotions for Engaged Mental Health Conversations

Providing timely support and intervention is crucial in mental health settings. As the need to engage youth comfortable with texting increases, mental health providers are exploring and adopting text-based media such as chatbots, community-based forums, online therapies with licensed professionals, and helplines operated by trained responders. To support these text-based media for mental health--particularly for crisis care--we are developing a system to perform passive emotion-sensing using a combination of keystroke dynamics and sentiment analysis. Our early studies of this system posit that the analysis of short text messages and keyboard typing patterns can provide emotion information that may be used to support both clients and responders. We use our preliminary findings to discuss the way forward for applying AI to support mental health providers in providing better care.

SGHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Singapore

To address the limitations of current hate speech detection models, we introduce \textsf{SGHateCheck}, a novel framework designed for the linguistic and cultural context of Singapore and Southeast Asia. It extends the functional testing approach of HateCheck and MHC, employing large language models for translation and paraphrasing into Singapore's main languages, and refining these with native annotators. \textsf{SGHateCheck} reveals critical flaws in state-of-the-art models, highlighting their inadequacy in sensitive content moderation. This work aims to foster the development of more effective hate speech detection tools for diverse linguistic environments, particularly for Singapore and Southeast Asia contexts.

Evaluating GPT-3 Generated Explanations for Hateful Content Moderation

Recent research has focused on using large language models (LLMs) to generate explanations for hate speech through fine-tuning or prompting. Despite the growing interest in this area, these generated explanations' effectiveness and potential limitations remain poorly understood. 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. For instance, an LLM-generated explanation might inaccurately convince a content moderator that a benign piece of content is hateful. In light of this, we propose an analytical framework for examining hate speech explanations and conducted an extensive survey on evaluating such explanations. Specifically, we prompted GPT-3 to generate explanations for both hateful and non-hateful content, and a survey was conducted with 2,400 unique respondents to evaluate the generated explanations. Our findings reveal that (1) human evaluators rated the GPT-generated explanations as high quality in terms of linguistic fluency, informativeness, persuasiveness, and logical soundness, (2) the persuasive nature of these explanations, however, varied depending on the prompting strategy employed, and (3) this persuasiveness may result in incorrect judgments about the hatefulness of the content. Our study underscores the need for caution in applying LLM-generated explanations for content moderation. Code and results are available at https://github.com/Social-AI-Studio/GPT3-HateEval.