Traditional sources of information about energy consumption, such a smart meter devices and household surveys, can be costly to deploy, may lack contextual information or have infrequent updates.

Data coming from social media are becoming widely available, inexpensive to collect, and frequently updated — yet are also biased and noisy. Given that they are the byproduct of — or refer to — daily human activities, it is reasonable to assume that information about energy consumption could be embedded in their semantic signatures.

Here we introduce Social Smart Meter, a web application that extracts energy consumption-related information from social media posts, and further offers the opportunity to gain an insight into daily energy consumption behavior, calculated at the neighborhood level.