12 accepted papers
Current text style transfer task mainly focuses on short texts, while the field has not been fully developed for long texts. Considering the richer semantics and more complex sentence structures in long text sequences, existing methods that employ traditional style-content disentanglement ways and l
Emotion and intent joint understanding in multimodal conversations (MC-EIU) aims to infer emotion and intent information by leveraging the semantic dependencies among multimodal data. However, prior works largely ignore redundancy interference and suffer from insufficient interaction and inter-task
Predicting the future popularity of information in online social networks is a crucial yet challenging task, due to the complex spatiotemporal dynamics underlying information diffusion. Existing methods typically use structural or sequential patterns within the observation window as direct inputs fo
Source localization has been widely studied in recent years due to its crucial role in controlling the spread of harmful information. Existing methods only achieve satisfactory performance within a specific propagation model, which restricts their applicability and generalizability across different
Graph generation is essential in social network analysis, particularly for modeling information flow and user interactions. However, existing probabilistic diffusion models face challenges when applied to social propagation graphs. The continuous noise does not apply to the discrete nature of graph
Large Language Models (LLMs) demonstrate strong capabilities in video understanding. However, it exhibits hallucinations and factual errors in video description. On the one hand, existing Multimodal Large Language Models (MLLMs) are primarily trained by combining language models and vision models, w
With the rapid development of AI large model technology, large language models (LLMs) provide a new solution for source localization tasks due to the deep linguistic understanding and generation capabilities. However, it is difficult to understand complex propagation patterns and network structures
Multimodal Emotion Recognition in Conversation (MERC) focuses on detecting the emotions expressed by speakers in each utterance. Recent research has increasingly leveraged graph-based models to capture interactive relationships in conversations, enhancing the ability to extract emotional cues. Howev
Information diffusion prediction is crucial for understanding how information spreads within social networks, addressing both macroscopic and microscopic prediction tasks. Macroscopic prediction assesses the overall impact of diffusion, while microscopic prediction focuses on identifying the next us…
With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial. Previous works mainly focus on obtaining social relationships of news via retweets, limiting the a…
Multivariate Temporal Point Processes (MTPPs) play an important role in diverse domains such as social networks and finance for predicting event sequence data. In recent years, MTPPs based on Ordinary Differential Equations (ODEs) and Stochastic Differential Equations (SDEs) have demonstrated their
Different social media are widely used in our daily lives. Inspired by the fact that similar topics have similar propagation characteristics, we mine the implicit knowledge of cascades with similar topics from different platforms to enhance the localization performance for scenarios where limited pr…