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We identify three gaps that limit the utility and obstruct the progress of computational text analysis methods (CTAM) for social science research. First, we contend that CTAM development has prioritized technological over validity concerns, giving limited attention to the operationalization of social scientific measurements. Second, we identify a mismatch between CTAMs' focus on extracting specific contents and document-level patterns, and social science researchers' need for measuring multiple, often complex contents in the text. Third, we argue that the dominance of English language tools depresses comparative research and inclusivity toward scholarly communities examining languages other than English. We substantiate our claims by drawing upon a broad review of methodological work in the computational social sciences, as well as an inventory of leading research publications using quantitative textual analysis. Subsequently, we discuss implications of these three gaps for social scientists' uneven uptake of CTAM, as well as the field of computational social science text research as a whole. Finally, we propose a research agenda intended to bridge the identified gaps and improve the validity, utility, and inclusiveness of CTAM. [ABSTRACT FROM AUTHOR]

Media exposure is one of the most important concepts in the social sciences, and yet scholars have struggled with how to operationalize it for decades. Some researchers have focused on the effects of variously worded self-report measures. Others advocate the use of aggregate and/or behavioral data that does not rely on a person’s ability to accurately recall exposure. Our study illustrates how an experimental design can be used to improve measures of exposure. In particular, we show how an experimental benchmark can be employed to (1) compare actual (i.e., manipulated) and self-reported values of news exposure; (2) assess how closely the self-reported items approximate the performance of “true” exposure in an empirical application; and (3) investigate whether a variation in question wording improves the accuracy of self-reported exposure measures. [ABSTRACT FROM PUBLISHER]