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Stance detection, which aims to determine whether an individual is for oragainst a target concept, promises to uncover public opinion from large streamsof social media data. Yet even human annotation of social media content doesnot always capture "stance" as measured by public opinion polls. We demonstratethis by directly comparing an individual's self-reported stance to the stanceinferred from their social media data. Leveraging a longitudinal public opinionsurvey with respondent Twitter handles, we conducted this comparison for 1,129individuals across four salient targets. We find that recall is high for both"Pro" and "Anti" stance classifications but precision is variable in a numberof cases. We identify three factors leading to the disconnect between text andauthor stance: temporal inconsistencies, differences in constructs, andmeasurement errors from both survey respondents and annotators. By presenting aframework for assessing the limitations of stance detection models, this workprovides important insight into what stance detection truly measures.

(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys