Facebook and its Disappearing Posts: Data Collection Approaches on Fan-Pages for Social Scientists

Abstract

Facebook fan-pages are channels of institutional self-representation that allow organizations to post content to virtual audiences. Occasionally, posts seem to disappear from fan-pages, puzzling page administrators and posing reliability risks for social scientists who collect fan-page data. This paper compares three approaches to data collection (manual real-time, manual retrospective, and automatic via NVIVO 10®) in order to explore the different frequencies of posts collected from six institutional fan-pages. While manual real-time collection shows the highest frequency of posts, it is time consuming and subject to manual mistakes. Manual retrospective collection is only effective when filters are activated and pages do not show high posting frequency. Automatic collection seems to be the most efficient path, provided the software be run frequently. Results also indicate that the higher the posting frequency is, the less reliable retrospective data collection becomes. The study concludes by recommending social scientists to user either real-time manual collection, or to run a software as frequently as possible in order to avoid biased results by ‘missing’ posts.