The project is supported by LMU Munich.
Description of the project
In the dynamic world of online media and algorithmically curated media environments, understanding the intricate online content consumption and user interaction is crucial. M3 offers a nuanced resource to social science in which it provides an up-to-date corpus of textual online media content as seen through regularly updated patterns of online media use.
M3 thereby combines media use with media content. The project monitors online exposure to textual media content through multiple lenses of how people actually use online media. Stored in a large-scale database, the project continuously presents its data to scholars from various fields via a free API that has explicitly been designed for the use cases of social scientists. M3 is divided into three core pillars:
The content pillar is dedicated to collecting media content. From running through news outlets to scraping social media platforms, M3 aggregates and analyzes textual data to furnish a comprehensive view of the prevailing media landscape. Here, the focus is to provide as much information on the content as possible to researchers while not storing actual full texts due to legal limitations.
The use pillar seeks to categorize media-use patterns of the broader population. Based on surveys and tracking data, the nuances of user behavior, preferences, and consumption patterns serve as guidelines for collecting media content. Incorporated into the M3 database, the platform can offer a combinatory view into what a particular group of a population likely saw when navigating online.
The encounter pillar builds a vital connection between the content and its users. Thinking of this as a given user’s exposure to a given content, this pillar allows us to draw a descriptive picture of what media content particular media-use patterns yield. This data is compiled through the use of emulated digital agents, thus allowing us to also systematically vary usage times, devices, or engagement times.
Keywords
Research infrastructure | Research data | Scraping
Leader of the Research Project
Prof. Dr. Mario Haim
Professor
Computational Communication Science • Political Communication • Computational Journalism
Meet the team
Patrick Parschan (née
Schwabl), M.A.
Academic Staff
Natural language processing • Ideal point estimation • Algorithmic bias
External partners
- Blueshoe
LMU research areas involved
The project includes several research projects within LMU. That's because it's great and very interdisciplinary.