Below, you can find a selection of projects that have been completed in the last years by making use of Port.

Behind the screens: Exploring Netflix via Data Donations

  • Principal investigators: Dennis Nguyen and Karin van Es, Utrecht University
  • Summary: Netflix is often hailed as a disruptor of the traditional media industry, primarily due to its extensive data collection and analysis capabilities, as well as the effectiveness of its recommendation system. As a subscription-based service, Netflix has historically been reticent to share viewing data with external parties. However, under increasing public pressure and with the introduction of an ad-supported tier, Netflix has begun to show greater transparency. Nonetheless, as Lotz (2023) observes, “Netflix is only sharing information it wants to share.” This selective data sharing results in a reliance on Netflix’s own narratives about aspects like binge-watching, content popularity and diversity, and the intricacies of their recommendation algorithms. Such narratives contribute to perpetuating myths, limiting our understanding of streaming platforms and obscuring their true disruptive impact (van Es 2023).
  • References
    • Lotz, A. D. (2023). “Netflix Data Dump: Cautions Regarding Gifts of Data.” Retrieved from here.
    • Van Es, K. (2023). “Netflix, (Re)Claiming Television: Myths and Horseless Carriages.” Keynote at ECREA TV Studies Section Conference, Potsdam, Germany, 26 October 2023.
  • Data collection period: January – February 2024
  • Participant recruitment: Participants were recruited through Ipsos I&O (previously I&O Research).
  • Instructions: Instructions to perform the data access can be found here. Instructions to download the data can be found here.
  • Codebook: This study included a questionnaire that was carried out by Ipsos I&O. The codebook of the questionnaire is available upon request at the PI.
  • Ethical review: The invitation letter, consent form, and privacy policy were reviewed by the Ethical Review Board of the Faculty of Humanities of Utrecht University. Click on invitation letter, consent form, or privacy policy to access the respective document.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the Netflix data is available on Github.
  • Data: Please contact the PI of this study.
  • Acknowledgements: This project was funded by PDI-SSH D3I.

The Google Family Home - Exploring the Use of Google Assistant in Families via Data Donations

  • Principal investigators: Rebecca Wald, Jessica Piotrowski, Theo Araujo, Johanna M.F. van Oosten Amsterdam School of Communication Research, University of Amsterdam
  • Summary: Smart speakers, such as Google Home or Alexa Echo, have become increasingly popular in family houesholds over the past years – also in the Netherlands (Wald, Piotrowski, van Oosten, et al., 2024). Given that media use in families is known to impact children’s further upbringing and development (Arora & Arora, 2022; Valkenburg & Piotrowski, 2017) and that the integration of such smart devices in intimate spaces like families has broader implications for the design and regulation of the technology (Wald et al., 2023), it is crucial to learn more about how families concretely use these devices at home. Thus far, two main types of measurements have most often been used to assess smart speaker use: self-reports of users and actual registered interactions with the smart speaker. Yet, both measurements of smart speaker use have not yet been compared in research, leaving open to what extent they align, are feasible to obtain, and based on that are best suitable in what research scenario. The overarching aim of ‘The Google Family Home’ study is therefore to understand to what extent there is a systematic difference between self-reported (via survey) and observed smart speaker use (via data donations), and how Dutch families with young children actually use smart speakers at home, that is how frequently the device is used (use-frequency), for what activity (use-purpose), and by whom in the family (use-form) (Wald, van Oosten, Piotrowski, et al., 2024).
  • References
    • Arora, A., & Arora, A. (2022). Effects of smart voice control devices on children: Current challenges and future perspectives. Archives of Disease in Childhood, archdischild-2022-323888. https://doi.org/10.1136/archdischild-2022-323888
    • Valkenburg, P. M., & Piotrowski, J. T. (2017). Plugged in: How media attract and affect youth. Yale University Press.
    • Wald, R., Piotrowski, J. T., Araujo, T., & Van Oosten, J. M. F. (2023). Virtual assistants in the family home. Understanding parents’ motivations to use virtual assistants with their child(dren). Computers in Human Behavior, 139, 107526. https://doi.org/10.1016/j.chb.2022.107526
    • Wald, R., Piotrowski, J. T., Van Oosten, J. M. F., & Araujo, T. (2024). Who are the (non-)adopters of smart speakers? A cross-sectional survey study of Dutch families. Tijdschrift Voor Communicatiewetenschap, 51. https://doi.org/10.5117/TCW2023.X.001.WALD
    • Wald, R., Van Oosten, J. M. F., Piotrowski, J. T., & Araujo, T. (2024). Smart Speaker Data Donations in Families: The Project Rosie Perspective. In Interaction Design and Children (IDC ’24), June 17–20, 2024, Delft, Netherlands. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3628516.3659374
  • Data collection period: May – June 2024
  • Participant recruitment: Participants were recruited through Motivaction.
  • Instructions: Instructions to perform the data access and download will be shared upon completion of the data collection and once the dataset is anonymized.
  • Survey: This study included a questionnaire that was distributed by Motivaction. The contens of the questionnaire will be made available on OSF upon completion of the data collection.
  • Ethical review: The study was approved by the university’s Ethics Review Board under reference FMG-8809 on 26-04-2024. For each participants, invitation letter, consent form, and privacy policy were accessible in the survey as well as the data donation interface.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the Google Assistant data will be made available on GitHub (linked to within the OSF-project) upon completion of the data collection.
  • Data: The anonimized dataset will be made available on OSF upon completion of the data collection. Please contact the PI of this study for questions/data requests.
  • Acknowledgements: This project was funded by the Communication in the Digital Society Initiative.

2024 Harnessing digital data to study 21st-century adolescence

  • Principal investigators: Amy Orben (PI), Laura Boeschoten, Daniel Oberski, Sebastian Kurten, Amanda Ferguson, Valerie Yap, Amelia Leyland-Craggs
  • Summary: The online world is still understudied. In particular, large cohort or household panel studies have failed to gather comprehensive digital data. Due to the extensiveness of their data collection efforts, it is often seen as too risky or burdensome to collect digital data about participants’ online lives, for example from platforms or phones. As a stop-gap solution many studies have, therefore, added select questionnaire items about digital behaviours. But these are unreliable and uninformative. In the last five years there has been much progress in designing new data collection methodologies that make digital data collection easier. These have now reached a level of maturity that could see them introduced in large data collection efforts. They could even be developed further to become a net asset to such studies by boosting participant engagement and retention. This project will take one of these methodological innovations developed internationally (the Digital Data Donation infrastructure and its software port) and test its feasibility for deployment in the MRC UK Adolescent Health Study. It will also explore whether this method can be augmented to increase adolescent involvement and engagement, as well as provide ways to motivate teachers and schools to sign up to the MRC Adolescent Health Study in the first place.
  • Data collection period: March – May 2024
  • Participant recruitment: We visited 6 secondary schools in person to present this study opportunity. These schools were identified through searches on the Gov.uk website and through existing partnerships through previous studies with our lab and collaborators.
  • Instructions: Instructions to download TikTok data can be found here. Instructions to download Instagram data can be found here. We send participants an email with a unique link to the Next platform to perform the data access which can be found here.
  • Codebook: -
  • Ethical review: The ethics form, information sheet and consent forms were reviewed by Cambridge Psychology Research Ethics Committee. Participant information sheet can be found here and participant consent form can be found here.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the TikTok and Instagram data are available on Github.
  • Data: Please contact the PI of this study.
  • Acknowledgements: This project was funded by SUAG/091 and SUAH/031.

2024 An investigation of Facebook groups dedicated to rare diseases

  • Principal investigators: Annemiek Linn and Kaiyang Qin, University of Amsterdam
  • Summary: Nowadays, it is estimated that a billion individuals are affected by rare diseases (RDs) and the scarcity of knowledgeable physicians has led caregivers to turn to social media for support and information sharing. This shared knowledge may offer more insight than what is known in the medical literature and is highly unexploited in research. In this study, we addressed this knowledge gap by mapping the collective wisdom of Facebook groups dedicated to a rare disease: FOXP1 syndrome. Utilizing a data donation approach, this study aims to capture the dynamics of information exchange and community support within the FOXP1 Facebook group. Participants voluntarily donated their Facebook activity data, such as posts and comments, related to the Facebook group: Friends and Family of FOXP1. This approach was complemented by an online survey to gather information on 1) the use of Facebook and other information sources (i.e., Facebook use, the use of other sources to gather information regarding FoxP1, privacy concerns regarding their Facebook information, Facebook community belonging), 2) caregivers’ characteristics (i.e., healthy literacy, coping style, intolerance of uncertainty, perceived social support, and demographic variables). We are now in the phase of data analysis. For the data analysis, we will first conduct correlation analysis to find associations among the group engagement measure (i.e., comment and post frequencies) and the survey measures (i.e., use of Facebook and caregivers’ characteristics). Next, we will use a mixed method approach whereby we will combine topic modeling with thematic analysis. We will first conduct keyword-assisted topic modeling and use the topic modeling results as input for further qualitative analysis. The qualitative results are anticipated to provide a nuanced understanding of how caregivers of individuals with FOXP1 syndrome leverage online platforms for knowledge sharing and community support. This research not only aims to enhance our comprehension of the online behaviors of rare disease communities but also sheds light on studying similar dynamics in other rare disease groups across social media.
  • Data collection period: January – February 2024
  • Participant recruitment: Participants were recruited from the Friends and Family of FOXP1 Facebook group.
  • Instructions: Instructions to perform the data access and download can be found here.
  • Codebook: This study included a questionnaire that was carried out by Qualtrics. The codebook of the questionnaire is available upon request at the PI.
  • Ethical review: This study was approved by the Ethics Review Board, University of Amsterdam (Reference: FMG-4422). The informed consent can be found here, and the privacy policy can be found here.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the Netflix data is available on Github.
  • Data: Please contact the PI of this study.
  • Acknowledgements: This project was funded by NWO.

2024 Social Media Use of Individuals in the Context of Organizational Posts

  • Principal investigators: Sarah Marschlich
  • Summary: This study investigates how individuals engage with organizational posts on social media and whether their user engagement with organizational posts differs depending on the social media platform (i.e., Facebook, Twitter, Instagram). Moreover, this study aims to understand what role individuals’ motivations to use social media in the context of organizational content and the perceived affordances (incl. platform features and properties) play in user engagement with organizational posts. The following research questions guide this study: RQ1: How do individuals make sense of their engagement with organizational content on social media through the lens of their digital trace data, and how does this differ across social media platforms? RQ2: Which motivations and perceived platform affordances are relevant in the individuals’ engagement with organizational content?
  • Data collection period: January – August 2024
  • Participant recruitment: Participants were recruited from a lab recruitment system from UvA.
  • Instructions: Instructions on how to perform the data access and download can be found here.
  • Interview Guide: This study employed a semi-structured interview to discuss the visualizations of the social media archive data (from Twitter/X, Facebook, and/or Instagram). The interview guide can be found here.
  • Ethical review: This study was approved by the Ethics Review Board, University of Amsterdam (Reference: FMG-2664_2023). The informed consent can be found here.
  • Software: The data donations were visualized using Port.
  • Local processing: The Python script for local processing of the Netflix data is available on GitHub.
  • Data: Please contact the PI of this study.
  • Acknowledgements: This project was funded by the Digital Communication Methods Lab: digicomlab, University of Amsterdam.

2023 Assessing WhatsApp Networks with Donated Data

  • Principal investigators: Rense Corten
  • Summary: Although Mobile Instant Messenger Services (MIMS) play an increasingly important role in social life, we know surprisingly little about these networks. Most research on social media focuses on “traditional” social media platforms such as X (formerly Twitter) and Facebook, because in contrast to MIMSs, data from these platforms are relatively accessible to research. In this project, we rely on the innovative method of data donation to collect information on the WhatsApp network in the Netherlands, by asking LISS respondents to donate own user data in a way that is both user-friendly and respecting their privacy. Specifically, we collect the number of WhatsApp contacts and groups per respondent, as well as metadata about one group chat conversation per respondent. The result is one of the first datasets on WhatsApp usage on a broad and high-quality sample, promising novel insights on questions about mobilization, participation and (mis)information.
  • Data collection period: February – April 2023
  • Participant recruitment: Participants were recruited through the LISS Panel
  • Consent, Instructions and Codebook: See here.
  • Ethical review: The study was approved by the Ethical Review Board of the Faculty of Social and Behavioral Sciences of Utrecht University and of the LISS panel.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the WhatsApp data is available on Github.
  • Data: Please contact the LISS panel.
  • Acknowledgements: This project was funded by the 2022 ODISSEI-LISS panel grant and the NWO VIDI (195.152 “Valid measures derived from incidental data”) awared to Daniel L. Oberski.

2023 Mapping the digital food environment using YouTube data

  • Principal investigators: Kaiyang Qin, Saar Mollen, Wilma Waterlander, and Eline Smit
  • Summary: Social media represents an emerging dimension of the digital food environment. The digital food environment on social media is mainly built upon two sources: food-relevant advertisements from companies (ads) and food-relevant posts from consumers. Together, we call this food-relevant content. This food-relevant content may vary in healthfulness. In our research, we examine whether people are exposed to more unhealthy food-relevant content than healthy food-relevant content on social media platforms. And to what extent this is due to companies pushing (unhealthy) food content to social media users or because of users’ own interaction with food-related content that may lead to self-selection of more of such content. In sum, the present study aims to map the digital food environment on social media by exploring users’ interactions with two sources of (unhealthy) food content: food ads and food posts. To this end, we collected participants’ YouTube data, and we tested their social media interactions with exposure to the food content, as well as with the number of food companies that have targeted participants as their customers. In the present study, we investigated the following questions:
    1. What does the digital food environment look like?
    2. How do digital food content interactions (e.g., liking, searching) affect digital food content exposure?
    3. How do food companies affect digital food content exposure?
  • Data collection period: April – November 2023
  • Participant recruitment: Participants were recruited from Panelinzicht (an online panel company) and a lab recruitment system from UvA.
  • Instructions: Instructions to perform the data access and download can be found here.
  • Codebook: This study included a questionnaire that was carried out by Qualtrics. The codebook of the questionnaire is available upon request at the PI.
  • Ethical review: This study was approved by the Ethics Review Board, University of Amsterdam (Reference: FMG-472). The informed consent can be found here, and the privacy policy can be found here.
  • Software: The data donations were collected using Port.
  • Local processing: The Python script for local processing of the Netflix data is available on Github.
  • Data: Please contact the PI of this study.
  • Acknowledgements: This project was funded by the Healthy Future initiative of the University of Amsterdam.