Following Topics Across All Apps and Media Formats: Mobile Keyword Tracking as a Privacy-Friendly Data Source in Mobile Media Research

Mining detailed content from mobile human-computer interactions often relies on broad log files, which limits the specificity of analyses. Our study presents a unique approach capitalizing on Optical Character Recognition to continuously detect keywords across all applications and media formats, correlating this with system logs for context and duration tracking. Such a strategy even allows tracing of topics within pictorial content like memes on social media. A privacy concept based on a whitelist for keywords and topics and anonymized log files addresses typical concerns of potential study participants regarding their personal data. In our four-month study involving 25 participants, we generated an expansive nine-million-point dataset. This detailed dataset not only validates the efficacy of our approach but also exemplifies its capacity for rich, cross-app, long-term interaction analysis. In order to clarify data protection issues, we conducted qualitative interviews with eight of the participants on a voluntary basis.