Browsing by Author "Araujo, Theo"

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Augmenting Data Download Packages – Integrating Data Donations, Video Metadata, and the Multimodal Nature of Audio-visual Content
(2024) Wedel, Lion; Ohme, Jakob; Araujo, Theo
This research explores the potential of augmented Data Download Packages (aDDPs) as a novel approach to analyze digital trace data, using TikTok as a use case to demonstrate the broader applicability of the method. The study demonstrates how these data packages can be used in social science research to understand better user behavior, content consumption patterns, and the relationship between self-reported preferences and actual digital behavior.We introduce the concept of aDDPs, which extend the conventional Data Download Packages (DDPs) by augmenting the collected data with survey data, metadata, content data, and multimodal content embeddings, among other possibilities - rendering aDDPs an unprecedentedly rich data source for social science research. This work provides an overview and guidance on collecting, augmenting DDPs, and analyzing the resulting aDDPs.In a pilot study on 18 aDDPs, we use the combination of data components in aDDPs to facilitate research on user engagement behavior and content classification. We showcase the potential of the information breadth and depth that aDDPs depict by exploiting the combination of multimodal content embeddings, the users’ watch history, and survey data. To do so, we train and compare uni- and multimodal classifiers, classify the 18 aDDPs’ videos, and investigate the extent to which user engagement behavior impacts future content suggestions. Furthermore, we compare the users retrieved content with the users’ self-reported content consumption.
Bursting Self-reports? Comparing Sampling Frequency Effects of Mobile Experience Sampling Method on Compliance, Attrition, and Sample Biases
(2025) Ohme, Jakob; Charlton, Timothy; Toth, Roland; Araujo, Theo; De Vreese, Claes H.
In-situ measurements, using the experience sampling method (ESM), can provide insight into behaviors and contextual factors by allowing individuals to self-report them via text or push messages on a smartphone close to the behavior of interest. However, more is needed to know about the data quality of these measures, particularly the impact of sampling frequency. This study aims to examine the effects of different sampling frequencies on compliance, sample biases, and reactivity of measures in the context of digital media use. In July 2021, a group of Dutch citizens (n=250) was randomly assigned to either a standard daily-intensive burst measure (DI-BM; seven surveys across the day) or hourly-intensive burst measure (HI-BM; 12 surveys over two hours per day) condition and surveyed across seven consecutive days, resulting in a total number of 16,135 surveys sent. Results indicate higher compliance in the standard ESM condition than in the burst ESM condition.
Digital data donations. A quest for best practices
(2022) Ohme, Jakob; Araujo, Theo
This preview article discusses PORT—a data donation software newly developed by Boeschoten et al.—toward the background of three core data donation principles: privacy protection, meaningful data extraction, and securing user agency.
Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking
(2023) Ohme, Jakob; Araujo, Theo; Boeschoten, Laura; Freelon, Deen; Ram, Nilam; Reeves, Byron B.; Robinson, Thomas N.
In social media effects research, the role of specific social media content is understudied, in part attributable to the fact that communication science previously lacked methods to access social media content directly. Digital trace data (DTD) can shed light on textual and audio-visual content of social media use and enable the analysis of content usage on a granular individual level that has been previously unavailable. However, because digital trace data are not specifically designed for research purposes, collection and analysis present several uncertainties. This article is a collaborative effort by scholars to provide an overview of how three methods of digital trace data collection - APIs, data donations, and tracking - can be used in studying the effects of social media content in three important topic areas of communication research: misinformation, algorithmic bias, and well-being. We address the question of how to collect raw social media content data and arrive at meaningful measures with multiple state-of-the-art data collection techniques that can be used to study the effects of social media use on different levels of detail. We conclude with a discussion of best practices for the implementation of each technique, and a comparison of their advantages and disadvantages.
Frequencies, Drivers, and Solutions to News Non-Attendance: Investigating Differences Between Low News Usage and News (Topic) Avoidance with Conversational Agents
(2022) Ohme, Jakob; Araujo, Theo; Zarouali, Brahim; de Vreese, Claes H.
Low levels of news seeking can be problematic for an informed citizenry. Previous research has discussed different types of news non-attendance but conceptual ambiguities between low news usage, general news avoidance, and news topic avoidance still exist. By using a longitudinal design conducted with a chatbot survey among Dutch users (n = 189), this study provides first empirical evidence that helps clarify conceptual differences. First, it estimates the prevalence of these different types of news non-attendance. Second, it tests to what extend cognitive restrictions, quality assessments, and personal relevance are relevant predictors in explaining engagement in three types of non-attendance to news. Third, the study investigates how news usage behaviors (e.g., news curation, news snacking, and verification engagement) may serve as potential user-driven counter strategies against news avoidance. We find evidence for the conceptual differences. Only small shares of news non-attendance are explained by avoidance motivations. Especially news curation and verification engagement can mitigate common drivers of news avoidance, while news snacking reinforces them.
Fulfilling data access obligations: How could (and should) platforms facilitate data donation studies?
(2024) Hase, Valerie; Ausloos, Jef; Boeschoten, Laura; Pfiffner, Nico; Janssen, Heleen; Araujo, Theo; Carrière, Thijs; de Vreese, Claes; Haßler, Jörg; Loecherbach, Felicia; Kmetty, Zoltán; Möller, Judith; Ohme; Schmidbauer, Elisabeth; Struminskaya, Bella; Trilling, Damian; Welbers, Kasper; Haim, Mario
Research into digital platforms has become increasingly difficult. One way to overcome these difficulties is to build on data access rights in EU data protection law, which requires platforms to offer users a copy of their data. In data donation studies, researchers ask study participants to exercise this right and donate their data to science. However, there is increasing evidence that platforms do not comply with designated laws. We first discuss the obligations of data access from a legal perspective (with accessible, transparent, and complete data as key requirements). Next, we compile experiences from social scientists engaging in data donation projects as well as a study on data request/access. We identify 14 key challenges, most of which are a consequence of non-compliance by platforms. They include platforms’ insufficient adherence to (a) providing data in a concise and easily accessible form (e.g. the lack of information on when and how subjects can access their data); (b) being transparent about the content of their data (e.g. the lack of information on measures); and (c) providing complete data (e.g. the lack of all available information platforms process related to platform users). Finally, we formulate four central recommendations for improving the right to access.
Mobile data donations: Assessing self-report accuracy and sample biases with the iOS Screen Time function
(2021-05) Ohme, Jakob; Araujo, Theo; de Vreese, Claes H.; Piotrowski, Jessica Taylor
With digital communication increasingly shifting to mobile devices, communication research needs to explore ways to retrieve, process, and analyze digital trace data on people’s most personal devices. This study presents a new methodological approach, mobile data donations, in which smartphone usage data is collected unobtrusively with the help of mobile log data. The iOS Screen Time function is used as a test case for gathering log data with the help of screenshots. The study investigates the feasibility of the method, sample biases, and accuracy of smartphone usage self-reports on a general population sample of Dutch citizens (n=404). Importantly, it explores how mobile data donations can be used as add-ons or substitutes for conventional media exposure measures. Results indicate that (a) users’ privacy concerns and technical skills are crucial factors for the willingness to donate mobile log data and (b) there is a strong tendency for underreporting of smartphone usage duration and frequency.
OSD2F: An Open-Source Data Donation Framework
(2022) Araujo, Theo; Ausloos, Jef; van Atteveldt, Wouter; Loecherbach, Felicia; Moeller, Judith; Ohme, Jakob; Trilling, Damian; van de Velde, Bob; De Vreese, Claes H.; Welbers, Kasper
The digital traces that people leave through their use of various online platforms provide tremendous opportunities for studying human behavior. However, the collection of these data is hampered by legal, ethical, and technical challenges. We present a framework and tool for collecting these data through a data donation platform where consenting participants can securely submit their digital traces. This approach leverages recent developments in data rights that have given people more control over their own data, such as legislation that now mandates companies to make digital trace data available on request in a machine-readable format. By transparently requesting access to specific parts of this data for clearly communicated academic purposes, the data ownership and privacy of participants is respected, and researchers are less dependent on commercial organizations that store this data in proprietary archives. In this paper we outline the general design principles, the current state of the tool, and future development goals.