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Online social media such as Twitter are widely used for mining publicopinions and sentiments on various issues and topics. The sheer volume of thedata generated and the eager adoption by the online-savvy public are helping toraise the profile of online media as a convenient source of news and publicopinions on social and political issues as well. Due to the uncontrollablebiases in the population who heavily use the media, however, it is oftendifficult to measure how accurately the online sphere reflects the offlineworld at large, undermining the usefulness of online media. One way ofidentifying and overcoming the online-offline discrepancies is to apply acommon analytical and modeling framework to comparable data sets from onlineand offline sources and cross-analyzing the patterns found therein. In thispaper we study the political spectra constructed from Twitter and fromlegislators' voting records as an example to demonstrate the potential limitsof online media as the source for accurate public opinion mining.

Quantifying discrepancies in opinion spectra from online and offline networks