Multimodal Large Language Models (MLLMs) are advancing the ability to reason about
complex sports scenarios by integrating textual and visual information. To comprehensively …

Recent integration of Natural Language Processing (NLP) and multimodal models has
advanced the field of sports analytics. This survey presents a comprehensive review of the …

Sports-related online abuse has evolved into an inevitable issue and even those athletes
and teams honored as national icons cannot be exceptional. This study explores how …

Detecting hate speech automatically in social media forensics has emerged as a highly
challenging task due to the complex nature of language used in such platforms. Currently …

随着社会信息化程度加深, 运用自然语言处理技术从海量网络数据中筛选提取有效信息,
具有重要的实用价值. 然而, 从社交平台收集的文本数据存在有效信息类别数据量少 …

Detecting harmful speech is the subject of significant research effort both in the academia
and industry. While good progress was made on detecting explicit hate speech, detecting …

In this digital era, the rapid expansion of online communication has brought tremendous
benefits and significant challenges. Among these challenges is the pervasive issue of hate …

As social media platforms grow in popularity, this research piece discusses the significance
of creating a secure and positive online environment. The major goal is to protect users by …

Digital media object classification plays an important role in digital media, but there is a
problem of low classification accuracy. Deep learning algorithms cannot improve the …

The global phenomenon of sports fandom went into overdrive during the 2018 FIFA World
Cup, reflecting deep emotional investment by audiences worldwide. Hence, this study …