Linked social media and survey data have the potential to be a unique source of information for social research. While the potential usefulness of this methodology is widely acknowledged, very few studies have explored methodological aspects of such linkage. Respondents produce planned amounts of survey data, but highly variant amounts of social media data. This study explores this asymmetry by examining the amount of social media data available to link to surveys. The extent of variation in the amount of data collected from social media could affect the ability to derive meaningful linked indicators and could introduce possible biases. Linked Twitter data from respondents to two longitudinal surveys representative of Great Britain, the Innovation Panel and the NatCen Panel, show that there is indeed substantial variation in the number of tweets posted and the number of followers and friends respondents have. Multivariate analyses of both data sources show that only a few respondent characteristics have a statistically significant effect on the number of tweets posted, with the number of followers being the strongest predictor of posting in both panels, women posting less than men, and some evidence that people with higher education post less, but only in the Innovation Panel. We use sentiment analyses of tweets to provide an example of how the amount of Twitter data collected can impact outcomes using these linked data sources. Results show that more negatively coded tweets are related to general happiness, but not the number of positive tweets. Taken together, the findings suggest that the amount of data collected from social media which can be linked to surveys is an important factor to consider and indicate the potential for such linked data sources in social research.

关联的社交媒体和调查数据有可能成为社会研究的独特信息来源。虽然这种方法的潜在用途已被广泛承认，但很少有研究探讨这种联系的方法学方面。受访者生成计划数量的调查数据，但社交媒体数据的数量差异很大。本研究通过检查可用于链接到调查的社交媒体数据量来探索这种不对称性。从社交媒体收集的数据量的变化程度可能会影响得出有意义的关联指标的能力，并可能引入可能的偏差。将来自受访者的 Twitter 数据与代表英国的两项纵向调查、创新小组和 NatCen 小组联系起来，表明发布的推文数量以及受访者的粉丝和朋友数量确实存在很大差异。对这两个数据源的多变量分析表明，只有少数受访者特征对发布的推文数量有统计上的显着影响，其中关注者数量是两个面板中发布的最强预测因子，女性发布少于男性，并且一些证据表明受过高等教育的人发布的信息较少，但仅限于创新小组。我们使用推文的情绪分析来提供一个示例，说明收集的 Twitter 数据量如何影响使用这些链接数据源的结果。结果表明，更多负面编码的推文与总体幸福感有关，但与正面推文的数量无关。综合起来，