Author: Diana Ignatovich

Spotlight: The Data Workers’ Inquiry 

Diana Ignatovich

AI applications are growing in popularity, everyday digital tasks are intuitively streamlined, and social media platforms are flooded with automated media that emulate the clarity of actual events. Naturally, this inspires discussions of future opportunities and concerns, such as the possibility of computers overtaking jobs that once relied upon humans. But amidst this consideration of AI into our routine behaviors, how much do we really know about the foundation of these tools? What are the invisible costs of this innovation, and who bears the consequences? The answer is revealed in this article, unsettling accounts behind the scenes of our usage are presented by the data workers’ inquiry.

This community-based initiative fights for fair working conditions and adequate recognition of data workers’ expertise. Since 2022, workers behind AI applications have been investigating their own workplaces to address labor conditions and build workplace power. Derived from the principles of 1880s Marxist thinking, workers conduct research tailored to their political and environmental concerns, with support from trained qualitative researchers. This team of researchers includes lead researcher Milagros Miceli with the Weizenbaum Institute, Adio Dinika, Krystal Kauffman, Camilla Salim Wagner, and Laurenz Sachenbacher. Without compromising the workers’ epistemic authority, they provide training in methods for data collection and analysis to create a methodology for workers to use within investigations. They also diligently monitor ethical and legal boundaries throughout the duration of projects. 

The inquiries take place across Venezuela, Kenya, Syria and Germany. Whether in essays, artwork or documentaries, data workers creatively share their perspective working under various AI industries. The striking truths are outlined in the inquiries below. Ultimately, this research will provide structure for collective action, establishing future ethical guidelines in regard to the treatment of data workers. 

Read More

Special Issue: Open Research Infrastructures and Resources for Communication and Media Studies

Diana Ignatovich

Despite the advantages of accessible and reproducible research practices for scholars in media and communication research, few journals present opportunities to examine these resources. Therefore the journal of Media and Communication plans to publish a Special Issue on “Open Research Infrastructures and Resources for Communication and Media Studies” in 2026 to encourage an exchange of feedback between researchers on the implications of relevant resources and infrastructures. The Call for Papers on this issue invites papers to discuss and pursue resources that adhere to open science principles. The Methods Lab lead, Christian Strippel is a co-editor of this issue. 

In regards to submissions, open science principles emphasize non-commercial tools that may apply to both quantitative and qualitative methods. Articles that present datasets, evaluate research software or compare instruments involved in data analysis are encouraged. The scope also extends to papers discussing developments or challenges to the operation of open research infrastructure, and investigates the potential areas for improvement. Notably, this publication considers implications for researchers of different socioeconomic and cultural backgrounds to address research inequalities and promote sustainability. Thus, papers are encouraged to reflect this dimension of diversity. In conclusion, contributions to this publication equip researchers with greater access and ease of operation to these valuable resources, ultimately advancing and promoting inclusivity within open research practices. 

Submission of Abstracts: 1-15 September 2025

Submission of Full Papers: 15-31 January 2026

Publication of the Issue: July/December 2026

New preprint article: Extracting smartphone use from Android event log data

Diana Ignatovich

With smartphones now more prevalent in everyday life than ever before, understanding their use and its implications becomes increasingly necessary. While self-reporting in surveys is the method typically used to assess smartphone use, it is affected by various problems such as distorted retrospection, social desirability bias, and high aggregation. More advanced methods include the Experience Sampling Method (ESM), which presents multiple short surveys per day to limit the degree of retrospection, and logging (Android only), which accesses an internal log on the device itself that documents each user activity in extremely high resolution. Although the latter is the most precise and objective method available for assessing smartphone use, the raw data received from the log file requires extensive transformation to extract actual human behavior rather than technical artifacts. Still, this transformation was never documented systematically and researchers working with this input implemented arbitrary steps to extract the data they required. 

The preprint article Extracting Meaningful Measures of Smartphone Usage from Android Event Log Data: A Methodological Primer, authored by former Methods Lab fellow Douglas Parry and Methods Lab member Roland Toth, aims to provide a detailed step-by-step guide to extracting different levels of smartphone use from Android log data. Specifically, the guide helps identify glances (short checks without unlocking the device), sessions (uses from unlocking to locking), and episodes (single app uses) from such log files, allowing for further investigation. All steps are presented as pseudo-code as well as described in text. In addition, the Online Supplementary Material (OSM) contains the full pseudo-code, a rendition in the R programming language, a sample data set containing raw log data, and more helpful material.

This guide ultimately enhances our understanding of how humans interact with these versatile devices, particularly beneficial for projects within the social sciences and neighboring disciplines. While survey methods are recognized for their economical advantages and ease of administration, access to objective high-resolution data contributes a more refined perspective. We hope this article helps researchers identify valuable measures from raw android event log data, thereby making this rich data source more accessible and manageable than it has previously been.