What do people do online?
Using data donation to understand digital behavior.

a workshop at the SPP Junior Researcher Meeting

Frieder Rodewald

University of Mannheim & Institute for Employment Research

Sebastian Prechsl

Institute for Employment Research & LMU Munich

October 22, 2025

Our Agenda

frodew.github.io/workshop_spp_annual_meeting/

1️⃣ What is digital trace data?

2️⃣ What is data donation? - The participant’s perspective.

3️⃣ What is data donation? - The researcher’s perspective.

Who are you?

Please raise your hand 🤚 if you …

  • are familiar with the term digital trace data
  • have worked with APIs
  • have worked with data donation
  • have worked with automated content analysis
  • regularly use programming languages (e.g., R, Python)

About me: Frieder Rodewald

🎓 PhD, University of Mannheim & Institute for Employment Research

🧐 Research interests: "I study what people do online."


More info: github.com/frodew & frieder-rodewald.de

About me: Sebastian Prechsl

🎓 Postdoc at Institute for Employment Research & LMU

🧐 Research interests: Social inequalities (in the labor market), potentials of digital trace data for labor market research

👉 part of the SPP project Integrating Data Donations in Survey Infrastructure

Our Team

A huge thanks to Valerie Hase, for contributing to the conceptualizion of a previous data donation workshop at CompText in Vienna.

What is the goal of this workshop?

  • ✅ Understanding digital data traces as a type of data
  • ✅ Understanding data donation as a method of data access
  • ✅ Working through key steps of data donation methods (participant & researcher view)
  • ❓ Discussing when (not) to use data donation studies
  • ❌ Detailed implementation (e.g., server set-up, coding data extraction scripts)

1️⃣ What is digital trace data?

🤔 Which examples for digital trace data you know?

What is digital trace data?

💡 Definition: The recording and storing of activities on digital platforms to draw conclusions about digital and analog phenomena.

This might include:

  • Tweets, likes, shares on social media
  • Geo data (locations, movements)
  • Digital payments
  • Spotify playlists

Where can we find/collect digital trace data?

  • Apps (e.g., running apps)
  • Social media platforms (e.g., Instagram)
  • Payment systems (e.g., Paypal)
  • Wearable devices (e.g., smart watch)

Which (latent) constructs can we measure?

⚠️ Be careful: These "advantages" are often claimed, but not empirically proven.

👉 Digital traces are neither necessarily less biased, cheaper, or larger.

(Dis-)advantages of digital trace data

  • ✅ More fine-grained, often longitudinal measures due to timestamps
  • ✅ Partly measurement of new variables (e.g., algorithmic inference)
  • ❌ Bias due to errors in representation and measurement
  • ❌ Implementation can be expensive and cumbersome
  • ❌ More data does not mean better data!

How can we collect digital traces?

Platform- and user-centric methods

🤔 Questions?

2️⃣ What is data donation?

The participant’s perspective.

🤔 Please raise your hand ✋

(Before a week ago…) Who has ever tried to request their data from an online platform?

What are data donation studies?

💡 Definition: Data donation studies are a user-centric method for collecting digital traces.

  • Users have the right to request, access, and download data that platforms collect about them.
  • They can make their data download packages (DDPs) available to science, often in the context of web surveys.
  • Researchers use CSS methods to filter, anonymize, and aggregate this data locally on participants’ devices.
  • Participants can inspect/delete their data before any data is transferred.

Which types of data do DDPs contain?

For platforms like YouTube, Instagram, or LinkedIn, for example… (Hase et al. 2024)

  • User profiles (e.g., privacy settings)
  • Activities (e.g., friends, likes, searches, exposure, analog movements)
  • Content and context (e.g., ads watched, algorithmically inferred interests)

How is data from DDPs different?

Compared to APIs (Ohme et al. 2024)…

  • Control & informed consent of users
  • Longitudinal data without "rate limits"
  • Partly additional measurements (e.g., exposure data; non-public data)

👉 but can be burdensome for participants!

Survey Request & Download Data Extract Data Inspect Data Consent

Survey

Survey start page

Request & Download Data

Different degrees in standardization for data requests (Hase et al. 2024)…

  • Verification procedure (password requirements)
  • Specification of data (metrics, observation period)
  • Notification on provision of DDP
  • Duration of DDP availability

Request manual for LinkedIn on computer

Request manual for Instagram on computer

Extract Data

Inspect Data

Data overview on data donation platform

Data donation

🤔 You might have already requested and downloaded your data in preparation for today. Did you encounter any difficulties in requesting and downloading your data?
We will dive into the content of your data a bit later.

🤔 Questions?

3️⃣ What is data donation?

The researcher’s perspective.

🤔 What are methodological decisions researchers have to take in data donation studies?

🔑 Key decisions

  • Which theoretical questions do I want to answer?
  • How do I operationalize key variables via my data donation tool?
  • How do I integrate the tool in surveys & recruit participants?

Survey Request & Download Data Extract Data Inspect Data Consent

Decisions we took

  • Goal: Feasability to implement data donation in a labor market panel, develop best-use practices, and understand non-response bias
  • Issues: Data availability, low response rates and bias (e.g., (Keusch et al. 2024))
    • Behavioral intentions as "willingness to donate" high (79-52% of survey respondents)
    • Actual behavior as "participation in data donation" low (37-12% of survey respondents)
    • Well known intention-behavior gap; where 🤑 seems to help (Kmetty et al. 2025)
  • Sample: A non-probability panel (online access panel)

Survey

  • Survey concerning…
    • online platform usage (YouTube, Instagram, and LinkedIn),
    • labor market characteristics,
    • and common indicators for non-participation.

Data request & download

Data extraction

🛠️ Strategy to make the extraction work

  1. Take a look at the DDP; download it, best for multiple time periods and for different languages
  2. Understand the structure of the JSON or CSV.
  3. Get an example file running.
  4. Write the code for the extraction script.
  5. Test your script, first locally and then in the wild.
  6. Adapt your script.

📢 Task: Try it yourself.

Take a look at your downloaded data. What do you see; anything caught your eye?

Feel free to work in groups of 2-3 people for 5 minutes.

Different degrees in standardization for DDP content (Hase et al. 2024)…

  • Documentation
    • DDP structure?
    • Measurements?
  • Completeness & scope
    • Missing data?
    • Limited time frames?
    • Language sensitive?

🚨 Key issues (Hase et al. 2024)

  • Missing documentation by platforms (e.g., file structure)
  • Sudden changes in DDPs
  • Differences across languages & devices
  • Insufficient in-tool classification (e.g., LLM integration)

Example: Extract list of subscriptions

subscriptions.csv (before processing)

...
    "subscriptions": {
        "extraction_function": ef.extract_subscriptions,
        "possible_filenames": ["Abos.csv", "subscriptions.csv"],
        "title": {
            "en": "Which channels are you subscribed to?",
            "de": "Welche Kanäle haben Sie abonniert?",
            "nl": "Op welke kanalen ben je geabonneerd?",
        },
    },
...
def extract_youtube_content_from_zip_folder(zip_file_path, possible_filenames):
    """Extract content from YouTube data export zip file using filenames"""

    try:
        with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
            # Get the list of file names in the zip file
            filenames = zip_ref.namelist()
            # Look for matching files
            for possible_filename in possible_filenames:
                for filename in filenames:
                    if possible_filename in filename:
                        try:
                            # Process based on file extension
                            if filename.endswith(".json"):
                                with zip_ref.open(filename) as json_file:
                                    json_content = json.loads(json_file.read())
                                    return json_content
                            elif file_name.endswith(".csv"):
                                with zip_ref.open(file_name) as csv_file:
                                    csv_content = pd.read_csv(csv_file)
def extract_subscriptions(subscriptions_csv):
    """Extract YouTube channel subscriptions"""

    # Define column name
    if "Kanaltitel" in subscriptions_csv.columns:
        channel_column = "Kanaltitel"
    else:
        channel_column = "Channel Title"

    # Define description
    channel_name = "Subscribed Channel"

    # Create DataFrame with just the channel names
    subscriptions_df = pd.DataFrame({channel_name: subscriptions_csv[channel_column]})
    return subscriptions_df

subscriptions.csv (after processing)

Data inspection

Data donation

  • A data donation platform helps to guide them through the process
  • The process should be made as easy as possible for participants

📚 Workshop Takeaways

  • There are many ways in which researchers can learn about people’s online behavior through digital trace data
  • People can request, download and finally donate their (anonymized) data form most online platforms
  • Data donation can be burdensome for participants
  • Data quailty heavily depends on the platform and what kind of data you extract from participants

🙃 Thank you for participating; happy to talk with you about data donation (and anything else) throughout the next days.

EXTRA: Can I extend the data?

  • Manual annotation by participants during data donation
  • APIs/scraping to extend collected data
  • Text-as-data methods for classification

EXTRA: Errors in representation

For example …

  • Coverage error: Who is (not) represented in the sampling frame? (e.g., social media users vs. YouTube users)
  • Sampling error: Who is (not) represented in the sample? (e.g., non-probability samples)
  • Non-response error: Who does (not) want to participate in the data donation?
  • Compliance error: Who is (not) able to participate in the data donation?

🤔 What do you think: Which participant characteristics may correlate with non-response or non-compliance?

EXTRA: What’s next for data donation studies?

Advancing the method

  • 📸 Multimodal & cross-platform data (Wedel, Ohme, and Araujo 2024)
  • In-tool, local classification (e.g., local ML/LLMs?)
  • Workflow/UX-perspective

Data as a political tool

  • Platforms do (willingly?) not provide data according to the GDPR/DSA (Hase et al. 2024)
  • The EU has started to sanction platforms like X/TikTok
  • DSA may become the subject of larger geo-political debates with the USA (Seiling, Ohme, and De Vreese 2025)

🚀 Can we improve & apply the method?

  • Can the method actually be applied for empirical research? (few examples, like (Thorson et al. 2021; Wojcieszak et al. 2024))
  • Requires interdisciplinary perspectives (e.g., addressing bias, integration in probability-based panels)

🤔 Questions?

References

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