  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.
Thus, it has become a critical task for social media platforms to deal with
suspected rumors. 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. 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. RumorLens integrates natural language processing (NLP) and
other data processing techniques with visualization techniques to facilitate
interactive analysis and validation of suspected rumors. 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. By using a
real-world dataset collected from Sina Weibo, one case study with a domain
expert is conducted to evaluate
