Social media sources such as Twitter have proven to be a valuable medium for obtaining real-time information on breaking events, as well as a tool for campaigning. When tweeters can be characterised in terms of location (e.g., because they geotag their updates, or mention known places) or topic (e.g., because they refer to thematic terms in an ontology or lexicon) their posts can provide actionable information. Such information can be obtained in a passive mode, by collecting data from Twitter's APIs, but even greater value can be gained from an active mode of operation, by engaging with particular tweeters and asking for clarifications or amplifications. Doing so requires knowledge of individual tweeters as "sensing assets". In this paper we show how the use of social media as a kind of sensor can be accommodated within an existing framework for sensor-task matching, by extending existing ontologies of sensors and mission tasks, and accounting for variable information quality. An integrated approach allows tweeters to be "accessed" and "tasked" in the same way as physical sensors (unmanned aerial and ground systems) and, indeed, combined with these more traditional kinds of source. We illustrate the approach using a number of case studies, including field trials (obtaining eyewitness reports from the scene of organised protests) and synthetic experiments (crowdsourced situational awareness).