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This demonstration presents Apollo, a new sensor information processing tool for uncovering likely facts in noisy participatory sensing data. Participatory sensing, where users proactively document and share their observations, has received significant attention in recent years as a paradigm for crowd-sourcing observation tasks. However, it poses interesting challenges in assessing confidence in the information received. By borrowing clustering and ranking tools from data mining literature, we show how to group data into sets (or claims), corroborating specific events or observations, then iteratively assess both claim and source credibility, ultimately leading to a ranking of described claims by their like-lihoold of occurrence. Apollo belongs to a category of tools called fact-finders. It is the first fact-finder designed and implemented specifically for participatory sensing. Apollo uses Twitter as the underlying engine for sharing participatory sensing data. Twitter is widely popular, can be interfaced to cell-phones that share sensor data, and comes with a powerful search API, as well as a publish-subscribe mechanism. We evaluate it using a participatory sensing application that collects and posts noisy vehicular traffic data on Twitter, as well as a set of 60,000 (human) tweets collected during the Haiti tsunami and a set of 500,000 tweets collected about Cairo during its recent unrest. Viewers of the demonstration will interact with Apollo for various fact-finding tasks.