The empathetic dialogue systems aim to recognize user emotions and generate appropriate empathetic responses. However, existing approaches predominantly rely on dialogue history, contextual descriptions, and emotion category labels, failing to model the causal relationship between emotions and their underlying triggers. This limitation leads to generated responses that lack grounding, exhibit weak relevance, and suffer from poor interpretability in emotional expression. To address this, we propose MvP-ECR, a multi-perspective emotion cause reasoning framework that explicitly constructs emotion-cause structures to help models focus on the core emotional drivers. Additionally, we introduce an emotion-cause consistency evaluation metric to quantitatively assess a model’s ability to identify causal relationships. Experiments across multiple large language models (LLMs) demonstrate that the MvP-ECR framework can serve as a plug-and-play tool to help the model correctly infer emotions and causes in empathetic conversations, and provide more immersive responses for empathetic responses. All code and data will be publicly released to promote the development of empathy dialogue research.

Intelligent dialogue systems are increasingly deployed in emotionally and ethically sensitive settings, where failures in either emotional attunement or ethical judgment can cause significant harm.Existing dialogue models typically address empathy and ethical safety in isolation, and often fail to adapt their behavior as ethical risk and user emotion evolve across multi-turn interactions.We formulate ethical-emotional alignment in dialogue as an explicit turn-level decision problem, and propose ETHICMIND, a risk-aware framework that implements this formulation in multi-turn dialogue at inference time.At each turn, ETHIC-MIND jointly analyzes ethical risk signals and user emotion, plans a high-level response strategy, and generates context-sensitive replies that balance ethical guidance with emotional engagement, without requiring additional model training.To evaluate alignment behavior under ethically complex interactions, we introduce a risk-stratified, multi-turn evaluation protocol with a context-aware user simulation procedure.Experimental results show that ETHICMIND achieves more consistent ethical guidance and emotional engagement than competitive baselines, particularly in high-risk and morally ambiguous scenarios.

The empathy dialogue system requires understanding emotions and their underlying causes.However, existing datasets mainly focus on emotion labels, while cause annotations are added post hoc through costly and subjective manual processes.This leads to three limitations: subjective bias in cause labels, weak rationality due to ambiguous cause-emotion relationships, and high annotation costs that hinder scalability.To address these challenges, we propose ECC (Emotion-Cause Conversation Dataset), a scalable dataset with 2.4K dialogues, which is also the first dialogue dataset where conversations and their emotion-cause labels are automatically generated synergistically during creation.We create an automatic extension framework EC-DD for ECC that utilizes knowledge and large language models (LLMs) to automatically generate conversations, and train a causality-aware empathetic response model CAER on this dataset.Experimental results show that ECC can achieve comparable or even superior performance to artificially constructed empathy dialogue datasets.

Multimodal Sentiment Analysis (MSA) aims to infer human emotions by integrating complementary signals from diverse modalities. However, in real-world scenarios, missing modalities are common due to data corruption, sensor failure, or privacy concerns, which can significantly degrade model performance. To tackle this challenge, we propose Hyper-Modality Enhancement (HME), a novel framework that avoids explicit modality reconstruction by enriching each observed modality with semantically relevant cues retrieved from other samples. This cross-sample enhancement reduces reliance on fully observed data during training, making the method better suited to scenarios with inherently incomplete inputs. In addition, we introduce an uncertainty-aware fusion mechanism that adaptively balances original and enriched representations to improve robustness. Extensive experiments on three public benchmarks show that HME consistently outperforms state-of-the-art methods under various missing modality conditions, demonstrating its practicality in real-world MSA applications.

Social bots are becoming increasingly common in social networks, and their activities affect the security and authenticity of social media platforms. Current state-of-the-art social bot detection methods leverage multimodal approaches that analyze various modalities, such as user metadata, text, and social network relationships. However, these methods may not always extract additional dimensions of semantic feature information that could offer a deeper understanding of users' social patterns. To address this issue, we propose ETS-MM, a multimodal detection framework designed to augment multidimensional information from text and extract the semantic feature representation of user text information. We first analyze the user's tweeting behavior based on topic preference and emotion tendency, integrating them into the textual data. Then, we try to extract enhanced semantic representations that reveal the latent relationship between tweeting behavior and tweet content while identifying potential contextual associations and emotional changes. Additionally, to capture the complex interaction between users, we integrate the user's multimodal information, including metadata, textual features, enhanced semantic features, and social network relationships to propagate and aggregate information across various modalities. Experimental results demonstrate that ETS-MM significantly outperforms existing methods across two widely used social bot detection benchmark datasets, validating its effectiveness and superiority.