Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics.

We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time.

The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. By using a real-world dataset collected from Sina Weibo, one case study with a domain expert is conducted to evaluateīreaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them.

We propose well-coordinated visualizations to provide users with three levels of details of suspected rumors: an overview displays both spatial distribution and temporal evolution of suspected rumors a projection view leverages a metaphor-based glyph to represent each suspected rumor and further enable users to gain a quick understanding of their overall characteristics and similarity with each other a propagation view visualizes the dynamic spreading details of a suspected rumor with a novel circular visualization design, and facilitates interactive analysis and validation of rumors in a compact manner. RumorLens integrates natural language processing (NLP) and other data processing techniques with visualization techniques to facilitate interactive analysis and validation of suspected rumors. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. Thus, it has become a critical task for social media platforms to deal with suspected rumors. With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society.

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