Analyzing short texts infers discriminative and coherent latent topics that is a critical and
fundamental task since many real-world applications require semantic understanding of …

Background Internet provides different tools for communicating with patients, such as social
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …

Due to the advent and increase in the popularity of the Internet, people have been producing
and disseminating textual data in several ways, such as reviews, social media posts, and …

Micro-video classification plays a central role in online content recommendation platforms,
such as Kwai and Tik-Tok. Existing works on video classification largely exploit the …

Short text stream clustering has become an important problem for mining textual data in
diverse social media platforms (eg, Twitter). However, most of the existing clustering …

With the explosive growth of short documents generated from streaming textual sources (eg,
Twitter), latent topic discovery has become a critical task for short text stream clustering …

Recent years have witnessed the unprecedented growth of online social media, resulting in
short texts being the prevalent format of information on the Internet. Given the sparsity of …

Logs are one of the most valuable data sources for large-scale service (eg, social network,
search engine) maintenance. Log parsing serves as the the first step towards automated log …

Computational social science uses computational and statistical methods in order to
evaluate social interaction. The public availability of data sets is thus a necessary …

Clustering short text streams is a challenging task due to its unique properties: infinite
length, sparse data representation and cluster evolution. Existing approaches often exploit …