In light of issues such as increasing unit nonresponse in surveys, several studies argue that social media sources such as Twitter can be used as a viable alternative. However, there are also a number of shortcomings with Twitter data such as questions about its representativeness of the wider population and the inability to validate whose data you are collecting. A useful way forward could be to combine survey and Twitter data to supplement and improve both. To do so, consent within a survey is first needed. This study explores the consent decisions in three large representative surveys of the adult British population to link Twitter data to survey responses and the impact that demographics and survey mode have on these outcomes. Findings suggest that consent rates for data linkage are relatively low, and this is in part mediated by mode, where face-to-face surveys have higher consent rates than web versions. These findings are important to understand the potential for linking Twitter and survey data but also to the consent literature generally.

Linked survey and Twitter data present an unprecedented opportunity for social scientific analysis, but the ethical implications for such work are complex—requiring a deeper understanding of the nature and composition of Twitter data to fully appreciate the risks of disclosure and harm to participants. In this article, we draw on our experience of three recent linked data studies, briefly discussing the background research on data linkage and the complications around ensuring informed consent. Particular attention is paid to the vast array of data available from Twitter and in what manner it might be disclosive. In light of this, the issues of maintaining security, minimizing risk, archiving, and reuse are applied to linked Twitter and survey data. In conclusion, we reflect on how our ability to collect and work with Twitter data has outpaced our technical understandings of how the data are constituted and observe that understanding one’s data is an essential prerequisite for ensuring best ethical practice.

Calendar instruments are hypothesized to promote data quality through the increased use of retrieval cues and conversational probes intended to clarify meanings. this research explores these hypotheses by examining the associations between retrieval and conversational verbal behaviors and data-quality measures. a verbal behavior coding scheme was applied to transcripts of 165 calendar interviews that collected lifecourse information on residence, marriage, employment, and unemployment from respondents in the panel Study of income Dynamics (pSiD). Confirmatory factor analyses revealed three latent factors for interviewers (retrieval probes, rapport behaviors, and conversational behaviors intended to satisfy questionnaire objective) and three latent factors for respondents (retrieval strategies, rapport, and conversational behaviors indicative of difficulty being interviewed). Ratios of discrepancies in annual totals between retrospective calendar reports and reports collected for up to thirty years in the pSiD over the total number of available years were used as measures of data quality. Regression analyses show that the level of behavior and the level of experiential complexity interact in their effect on data quality. both interviewer and respondent retrieval behaviors are associated with better data quality when the retrieval task is more difficult but poorer accuracy when experiential