Contribute

docs/docs/01_get-started/index.mdx

@@ -19,6 +19,13 @@ sidebar_position: 0
 
 To help you get started and better navigate the research landscape of social media monitoring (SMM) and researching online discourse more broadly, we’ve collected a first set of chapters that aim to provide orientation and guidance to researchers and practitioners exploring various options and dimensions of independently researching social media. We welcome additional content and have added a selection of chapters below that would make for great contributions.
 
+<details>
+  <summary>Overview: Tools for working with digital trace data</summary>
+
+  The purpose of this entry is to provide an overview of tools that can be used across the research data cycle when working with digital trace data: From data collection to preprocessing, analysis and visualization, reporting, and archiving and sharing data.
+
+  **[Learn more](01_01_overview)**
+</details>
 <details>
   <summary>Ethical considerations</summary>
 

docs/docs/03_data-collection/03_00_platform-specific guidelines/03_04_data-collection_meta_ads.mdx

@@ -31,7 +31,9 @@ includes metadata such as the ad’s text, the advertiser’s page, the time
 period it ran, the amount spent (as a range), impressions delivered
 (also as a range), and breakdowns of the audience by age, gender, and
 region. Importantly, many metrics are given as ranges (min–max) rather
-than precise values. This tutorial will demonstrate how to use R (with
+than precise values. 
+
+This tutorial will demonstrate how to use R (with
 the `tidyverse` ecosystem) and the `Radlibrary` R package to access the
 Meta Ad Library via its official API. We will walk through obtaining API
 access, constructing queries to find ads (by keyword or page id),
@@ -69,17 +71,10 @@ API. Meta requires a few one-time setup steps:
     confidential and treat it like a password – anyone with this token
     could potentially query the API on your behalf until it expires.
 
-> Token expiration: By default, tokens from the Explorer are
-> [short-lived](https://developers.facebook.com/docs/facebook-login/guides/access-tokens#termtokens)
-> (usually ~1-2 hours). For short analysis sessions that might be
-> sufficient, but in most cases you will likely need longer access. You
-> can exchange the short-lived token for a 60-day token using your App’s
-> App ID and App Secret. In this tutorial, we will proceed with a
-> short-lived token for simplicity, but it is strongly encouraged to get
-> a long-term token for your analysis (holds for 60 days). For
-> instructions on how to do this, refer to the [official Meta
-> documentation on access
-> tokens](https://facebookresearch.github.io/Radlibrary/articles/Radlibrary.html#generating-persistent-tokens).
+:::hub-note Note
+**Token expiration:** By default, tokens from the Explorer are [short-lived](https://developers.facebook.com/docs/facebook-login/guides/access-tokens#termtokens)(usually ~1-2 hours). For short analysis sessions that might be
+sufficient, but in most cases you will likely need longer access. You can exchange the short-lived token for a 60-day token using your App’s App ID and App Secret. In this tutorial, we will proceed with a short-lived token for simplicity, but it is strongly encouraged to get a long-term token for your analysis (holds for 60 days). For instructions on how to do this, refer to the [official Meta documentation on access tokens](https://facebookresearch.github.io/Radlibrary/articles/Radlibrary.html#generating-persistent-tokens).
+:::
 
 ### Step 2: Installing and Loading R Packages
 
@@ -212,12 +207,9 @@ query <- adlib_build_query(
 )
 ```
 
-> **Note**: You might encounter the following warning:  
-> *Warning: Unsupported fields supplied:* followed by a list of
-> parameters.  
-> This warning can be safely ignored. The `Radlibrary` package, despite
-> being developed by the Facebook team, may not be up to date with the
-> newest parameters.
+:::hub-note Note
+You might encounter the following warning: *Warning: Unsupported fields supplied:* followed by a list of parameters. This warning can be safely ignored. The `Radlibrary` package, despite being developed by the Facebook team, may not be up to date with the newest parameters.
+:::
 
 ------------------------------------------------------------------------
 
@@ -706,8 +698,9 @@ gender, and percentage. Each row might say, for example, ad X – age
 women aged 18-24). The percentages for a given ad across all age/gender
 categories sum up to 100%.
 
-> Important: if an ad did not reach a particular demographic group, it may
-not have an entry for that group.
+:::community[Important]
+If an ad did not reach a particular demographic group, it may not have an entry for that group.
+:::
 
 Now, what can we learn from this? Here are a couple of insights we might
 extract:
@@ -720,10 +713,9 @@ or simply fail to engage older audiences. Similarly, we could examine
 how many ads target women vs. men, or the average percentage of
 impressions to women vs. men.
 
-> *Note of caution:* A more robust analysis would weight the data by
-impressions or spending. Averaging percentages across ads without doing
-so treats a low-reach ad the same as a high-reach one. For simplicity,
-however, we proceed with the unweighted approach.
+:::community[Note of caution]
+A more robust analysis would weight the data by impressions or spending. Averaging percentages across ads without doing so treats a low-reach ad the same as a high-reach one. For simplicity, however, we proceed with the unweighted approach.
+:::
 
 For a quick view, we can calculate the overall gender split in relative
 impressions, assuming equal weight per ad (again, caution advised):

docs/docs/03_data-collection/index.mdx

@@ -16,7 +16,7 @@ sidebar_position: 0
 </style>
 
 # Data collection methods
-<LastUpdatedByChip authorLastUpdate="Cathleen Berger" updatedOn="06.12.2024" />
+<LastUpdatedByChip authorLastUpdate="Clara Ruthardt" updatedOn="01.08.2025" />
 
 Data collection on social media and digital platforms comes with a range of specificities and nuances that, unfortunately, differ across each platform. To facilitate research and give you an idea of what’s possible on which platform, this section outlines data collection approaches and introduces examples for TikTok, X (Twitter), and blogs (including code). Additional examples or contributions to data collections methods are welcome, suggestions are listed under (4) call for contributions.
 
@@ -39,9 +39,9 @@ Data collection on social media and digital platforms comes with a range of spec
 <details>
   <summary>Platform-specific guidelines, e.g. X API, Streaming data</summary>
 
-  X (Twitter) with its vast user base and real-time data, has always been a fertile ground for researchers and developers alike. This chapter provides a comprehensive guide to making the most out of the API, with specific attention to a newly developed Python library, underscoring its flexibility and scalability.
+  This chapter outlines platform-specific guidelines for data collection across X (Twitter), YouTube, Rumble, and Meta Ads on Facebook and Instagram. Each guideline includes step-by-step tutorials with code samples and an introduction to potential analysis methods.
 
-  **[Learn more](03_00_code-samples/twitter/streaming-database)**
+  **[Learn more](03_00_platform-specific guidelines)**
 </details>
 
 <details>
@@ -55,6 +55,6 @@ Data collection on social media and digital platforms comes with a range of spec
 
 :::contribute Open for contributions
 
-Contributions are welcome, particularly case studies on Facebook, Instagram, YouTube and other platforms as well as additional data collection tactics and methods. 
+Contributions are welcome, particularly on additional data collection tactics and methods. 
 
 :::

docs/docs/04_data-analysis/04_05_metatargetr.mdx

@@ -42,16 +42,16 @@ perspective.
 
 Here are the key advantages over Meta’s official Ad Library API:
 
-- Advertiser-Level Data: Instead of looking at single ads, we get a
+- **Advertiser-Level Data:** Instead of looking at single ads, we get a
   consolidated view of the entire targeting strategy of a Facebook page
   or Instagram account.
 
-- Exact Spending per Criterion: The data provides the exact percentage
+- **Exact Spending per Criterion:** The data provides the exact percentage
   of a page’s total spend allocated to each specific targeting
   criterion. This allows for precise analysis of budget allocation, a
   feature not available through the official API.
 
-- Additional Audience Insights: The dataset goes beyond the demographic
+- **Additional Audience Insights:** The dataset goes beyond the demographic
   targeting that is available via the API, as it reveals spending on
   powerful tools like Detailed Targeting (e.g. interest profiles that
   Meta categorizes its users in), Custom Audiences (e.g., targeting a
@@ -327,12 +327,9 @@ spending for each targeting criterion based on the new, combined total.
 # Apply the function to our DHS data
 dhs_data_agg <- aggr_targeting(dhs_data_raw)
 ```
-
-> Note: the `aggr_targeting()` function creates a variable called
-> `spend_per` which shows the spend per targeting criterion
-> across the combined period. If you pull raw data with `get_targeting_db()`, you have to compute this measure
-> yourself as *total spending* (`total_spend_formatted`) *×* *share of spending*
-> (`total_spend_pct`).
+:::hub-note Note
+The `aggr_targeting()` function creates a variable called `spend_per` which shows the spend per targeting criterion across the combined period. If you pull raw data with `get_targeting_db()`, you have to compute this measure yourself as *total spending* (`total_spend_formatted`) *×* *share of spending* (`total_spend_pct`).
+:::
 
 
 ### Visualizing "Detailed" Targeting Criteria
@@ -390,8 +387,9 @@ in Mexican culture events and music, and whose language is set to
 Spanish. This provides concrete, data-driven insights into their
 campaign strategy that would be difficult to obtain otherwise.
 
-> Note:  When multiple targeting criteria show identical spend, that likely indicates they were applied together on the same underlying ads. Because the data that is retrieved is aggregated at the advertiser level it is hard to isolate ads that ran simultaneously. For a better measurement of spend we could divide the total spending by the number of targeting criteria that have the same data (same number of ads and spending) by assuming that spending was divided about equally to each targeting criterion.
-
+:::hub-note Note
+When multiple targeting criteria show identical spend, that likely indicates they were applied together on the same underlying ads. Because the data that is retrieved is aggregated at the advertiser level it is hard to isolate ads that ran simultaneously. For a better measurement of spend we could divide the total spending by the number of targeting criteria that have the same data (same number of ads and spending) by assuming that spending was divided about equally to each targeting criterion.
+:::
 
 ### Visualizing Location Targeting Criteria
 
@@ -510,11 +508,11 @@ the targeting criteria used as proxies for the Latino community are also
 employed by a diverse range of other major advertisers. The top 10
 spenders using these methods include:
 
-- Public Health & Advocacy Groups: Organizations like Planned
+- **Public Health & Advocacy Groups:** Organizations like Planned
   Parenthood, Tobacco Free Florida, and The California Endowment use
   this targeting for outreach and awareness campaigns.
 
-- Non-Profits and Charities: Pages such as Catholic Relief Services,
+- **Non-Profits and Charities:** Pages such as Catholic Relief Services,
   Operation Smile, and The International Fellowship of Christians and
   Jews appear to use these criteria for fundraising and supporter
   engagement.

docs/docs/04_data-analysis/index.mdx

@@ -17,7 +17,7 @@ sidebar_position: 0
 </style>
 
 # Data analysis projects
-<LastUpdatedByChip authorLastUpdate="Cathleen Berger" updatedOn="06.12.2024" />
+<LastUpdatedByChip authorLastUpdate="Clara Ruthardt" updatedOn="01.08.2025" />
 
 To help you get started and inspired with social media analyses, we’ve collected a first set of practical projects that provide a starting point. There are a myrdiad of projects and methods discussion around social media data. Therefore, we welcome additional content and have added a selection of chapters below that would make for great contributions.
 
@@ -53,6 +53,14 @@ To help you get started and inspired with social media analyses, we’ve collect
   **[Learn more](04_04_disarm-framework)**
 </details>
 
+<details>
+  <summary>Analyzing Ad Targeting Insights: An Introduction to the metatargetr R Package</summary>
+
+ The metatargetr R package offers an alternative, unofficial route to this information, by retrieving and archiving ad data directly from Meta’s public-facing Ad Library. This tutorial focuses on how to use metatargetr to retrieve and, most importantly, analyze this targeting data. 
+
+  **[Learn more](04_05_metatargetr)**
+</details>
+
 :::contribute Open for contributions
 
 We welcome contributions on a rolling basis. Have you done an analysis or a research project you want to share? Do you want to contribute a primer for a specific method? Get in touch with us at [upgrade.democracy@bertelsmann-stiftung.de](mailto:upgrade.democracy@bertelsmann-stiftung.de)

docs/docs/05_literature/05_03_articles-and-research.mdx

@@ -5,12 +5,19 @@ title: Community articles and research
 
 # Community articles and research
 
-<LastUpdatedByChip authorOriginal="Clara Ruthardt" createdOn="30.09.2024" authorLastUpdate="Cathleen Berger" updatedOn="06.12.2024" />
+<LastUpdatedByChip authorOriginal="Clara Ruthardt" createdOn="30.09.2024" authorLastUpdate="Clara Ruthardt" updatedOn="01.08.2025" />
 
 Social media research is a rapidly changing field. In this chapter you can find the latest research reports, tools, policy papers and articles from our community. 
 
 _Please note: Despite careful review of the content, the Data Knowledge Hub accepts no liability for the content of external links. The providers of the web pages to which the links lead are solely responsible for their contents._
 
+<ArticleCard 
+  heading="Social Media Monitoring Toolkit"
+  author="Democracy Reporting International"
+  teaser="The Digital Democracy Monitor Toolkit helps civil society, journalists, researchers and anyone trying to research social media and democracy." 
+  link="https://digitalmonitor.democracy-reporting.org/toolkit/"
+/>
+
 <ArticleCard 
   heading="Custom Portal for Innovative Data Collection and Disinformation Analysis"
   author="Inform Africa"

docs/docs/06_contribute/06_02_community.mdx

@@ -99,6 +99,16 @@ The Data Knowledge Hub for researching online discourse is a community effort. W
   mastodon="https://masto.ai/@correlaid"
 />
 
+<AuthorCard
+  name="Fabio Votta"
+  avatar="FV"
+  avatarSrc={require("@site/static/img/contributors/votta.jpg").default}
+  position="ASCoR | University of Amsterdam"
+  website="https://www.favstats.eu/"
+  bluesky="https://bsky.app/profile/favstats.eu"
+  linkedin="https://www.linkedin.com/in/dr-fabio-votta-81a862131"
+/>
+
 <AuthorCard
   name="Heather Dannyelle Thompson"
   avatar="HT"
@@ -126,6 +136,15 @@ The Data Knowledge Hub for researching online discourse is a community effort. W
   website="https://www.unibw.de/jasmin.riedl"
 />
 
+<AuthorCard
+  name="Johannes Breuer"
+  avatar="JB"
+  avatarSrc={require("@site/static/img/contributors/breuer.png").default}
+  position="Senior Researcher, GESIS - Leibniz Institute for the Social Sciences"
+  website="https://www.johannesbreuer.com/"
+  mastodon="https://fediscience.org/@JohannesBreuer"
+/>
+
 <AuthorCard
   name="Josef Holnburger"
   avatar="JH"
@@ -161,6 +180,15 @@ The Data Knowledge Hub for researching online discourse is a community effort. W
   linkedin="https://de.linkedin.com/in/philipp-darius"
 />
 
+<AuthorCard
+  name="Richard Schwenn"
+  avatar="RS"
+  avatarSrc={require("@site/static/img/contributors/schwenn.jpg").default}
+  position="polisphere"
+  website="https://www.polisphere.eu/de/"
+  linkedin="https://www.linkedin.com/in/richard-schwenn-6b787623a/"
+/>
+
 <AuthorCard
   name="Valerie Hase"
   avatar="VH"