Our study aimed to approach validity issues that arise both in the case of surveys and Facebook data . We conducted a novel parallel data collection method, rarely used before, which combines a face-to-face survey with the collection of personal FB data archives. We show the research potential of using these alternative data sourcestogether. We show that we can overcome some validity issues by cross-validating the two data sources by each other. We also show that if the researcher collaborates with the users instead of the companies and gets access to a part of the users' social media data archive, the problem of the leanness of social media data is eliminated. Our chosen topic is music interest, a key indicator of cultural sociology. To our knowledge , there are no previous studies on the rate at which any population is categorized by Facebook to advertisers as interested in music genres, or on the relationship between self-reported interest, digitally expressed interest and ad-interest categories. Specific genres were detected which show remarkable different pictures when measured these different ways. We studied also dynamic patterns in FB users music interest. We hope our data collection method and the presented validity approaches will initiate a future dialogue in digital data research in social sciences .
More and more digital data is available for social science analysis. This provides new ways of measuring several concepts. But when we start using new data sources, we have to understand how the new data source could be processed and how it could be analysed effectively. It is especially for Facebook data since there is no established gold standard analysis-framework. However, researchers have in-depth knowledge on how to measure different concepts using survey data. Thus, cross-referencing Facebook data with survey data is a reasonable way to support Facebook data analysis at different decision points. In this paper, we present how music preference could be measured by Facebook data and how survey data could support the selection of main indicators. Based on our results, we provide some general suggestions for Facebook data processing and indicator operationalization .
R
Our study aimed to approach validity issues that arise both in the case of surveys and Facebook data . We conducted a novel parallel data collection method, rarely used before, which combines a face-to-face survey with the collection of personal FB data archives. We show the research potential of using these alternative data sourcestogether. We show that we can overcome some validity issues by cross-validating the two data sources by each other. We also show that if the researcher collaborates with the users instead of the companies and gets access to a part of the users' social media data archive, the problem of the leanness of social media data is eliminated. Our chosen topic is music interest, a key indicator of cultural sociology. To our knowledge , there are no previous studies on the rate at which any population is categorized by Facebook to advertisers as interested in music genres, or on the relationship between self-reported interest, digitally expressed interest and ad-interest categories. Specific genres were detected which show remarkable different pictures when measured these different ways. We studied also dynamic patterns in FB users music interest. We hope our data collection method and the presented validity approaches will initiate a future dialogue in digital data research in social sciences
More and more digital data is available for social science analysis. This provides new ways of measuring several concepts. But when we start using new data sources, we have to understand how the new data source could be processed and how it could be analysed effectively. It is especially for Facebook data since there is no established gold standard analysis-framework. However, researchers have in-depth knowledge on how to measure different concepts using survey data. Thus, cross-referencing Facebook data with survey data is a reasonable way to support Facebook data analysis at different decision points. In this paper, we present how music preference could be measured by Facebook data and how survey data could support the selection of main indicators. Based on our results, we provide some general suggestions for Facebook data processing and indicator operationalization