WhatsApp was the leading actively used messenger service in Germany in 2021. Almost 84 percent of users confirmed this. While WhatsApp is foremost a messaging service, certain features indicate similarities with social media networks, as sharing and posting between users still occurs, just not necessarily on a publicly accessible website. Delivering the message For most of the population, modern life is unimaginable without messenger apps. Texting has become much more varied as a form of communication thanks to extended file and content sharing options within messages. There is no doubt that WhatsApp is a popular messaging app in Germany. In 2023, almost 85 percent of people were messaging on WhatsApp every day. By 2025, it is estimated that over 53 million people will be using WhatsApp in Germany, suggesting it's popularity as a messaging app will not diminish with time. Personal data While Facebook is extremely popular in many different countries, long-term questions and concerns from users continue to arise, with personal data security being one of the leading topics of discussion. In general, the there have been many breaches of personal data online. At the same time, social media continues to enjoy rising popularity and use among the German population, both in a private and professional context.

We asked German consumers about "Social network usage by brand" and found that ********* takes the top spot, while ******** is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 39,895 consumers in Germany.

Based on forecasts, there will be **** million social network users in Germany by 2020. Numbers have been growing steadily in recent years. Likes and dislikes Social media has taken numerous countries by storm since its emergence and Germany is no exception. In 2018, more than **** of the population used social networks. Among these, the most popular were WhatsApp, YouTube and Facebook. While it’s understandable to pose the question whether WhatsApp in particular is a social media platform and not a messaging service only, it is being used by businesses and media outlets to communicate information and get closer to consumers. The general population, meanwhile, pursues a variety of activities on social networks, ********** sending private messages, as well as liking and commenting on posts by other users. Compared to other European countries and Russia, Germans do not spend the most time on social media. An open book? Facebook dominates the global social media scene and the numbers speak for themselves, with half of the world’s online population on it. Some of the most consistent, long-term concerns simultaneously arising with Facebook’s and other social networks’ staggering popularity have to do with transparency regarding data protection and user privacy options.

Description

This corpus contains multiple middle high german texts with annotations for social network analysis. It contains annotations of: named entities and entity mentions (including partial coreference resolution), direct speech, narrator's comments. See below for further description.

The annotated texts are part of my dissertation on social network analysis of arthurian romances. The research was developed in the context of the DH center CRETA at the University of Stuttgart.

Texts

Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.

Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).

Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.

Wolfram von Eschenbach: 'Willehalm', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 421–640.

'Das Rolandslied des Pfaffen Konrad', ed. by Carl Wesle, 3rd edition by Peter Wapnewski, Tübingen 1985 (Altdeutsche Textbibliothek 69).

All texts are part of the MHDBDB (Mittelhochdeutsche Begriffsdatenbank).

Annotations

The texts contain annotations of different categories, as described in the following sections.

1. Named Entities and Entity Mentions

I annotated all namend entities and entity mentions that belong to the categories PER and LOC. PER stands for 'person' and refers to real persons as well as fictional characters. LOC stands for 'location' and includes real and fictional places.

I annotated named entities (e.g. 'Parzival' as PER or 'Nantes' as LOC) as well as entity mentions referring to an instance of PER or LOC (e.g. 'the knight' for Parzival, or 'the city' for Nantes). I did not annotate pronouns. Entity references can contain multiple words, e.g. 'the lovely queen Ginover', and they can be nested, e.g. '[the son of [the king Gahmuret]]'.

The annotations follow the guidelines created for multiple categories and disciplines in the context of CRETA. They are published here.

2. Entity Grounding

All annotated entity references are mapped to the entity instance that they refer to. E.g. the refences 'Parzival', 'Herzeloyde's son', 'the young man', 'the red knight' etc. all refer to the character instance 'Parzival'. The entity grounding takes into consideration the context of the entity mentions since one and the same expression can refer to different instances (in one context 'the king' refers to Arthur, in another context to Gahmuret)

3. Direct Speech (DS)

Passages of direct speech have been annotated by detecting quotation marks. They are tagged as 'DS'. There are a few cases of embedded direct speech (passages of direct speech containing another passage of direct speech); these cases are annotated as well.

4. Narrator's comments (EK)

As additional category I annotated passages that contain statements of the narrator, narrator's comments, extensive descriptions or digressions (e.g. an excursus to a specific topic). These passages are not part of the fictional world or lead to a pause in the timeline of events. The are annotated as 'EK' ('EK': passages that aren't part of the diegesis, 'EK2': passages that lead to a pause, e.g. comments or descriptions).

5. Segmentation

The texts are subdivided in passages of 30 verses. Since some text's editions ('Parzival', 'Willehalm') contain a formal segmentation in passages of 30 verses each, the same kind of segmentation has been transfered to the other texts. This means 'segment 1' contains the first 30 verses, 'segment 2' contains verses 31-60 and so on.

According to the editions by Lachmann, 'Parzival' and 'Willehalm' are also subdivided in chapter-like books (Parzival: book 1 to 16, Willehalm: book 1 to 9). The other texts are similarly subdivided in chapter-like sections following common content-based divisions.

Social Network Analysis

The data can be used to explore and analyse the social network of the texts. SNA can be performed via gephi [4] using the gefx files.

The social network is based on co-occurrences using a) the annotated and grounded entities, and b) the text segmentation in segments of 30 verses each. A relation between two or more entities is extracted whenever they co-occur in a segment.

Data downloads

The annotated texts can be downloaded in multiple formats: conll, csv, and gexf.

1. Conll 

The files contain seven columns:

(1) token,

(2) POS-tag, tagged using a middle high german pos tagger,

(3) number of segment,

(4) Entity reference annotation indicating the intance that the entity reference refers to. '-' if there is no entity reference,

(5) EK: '1' in case there is an annotation of 'EK', '0' if not,

(6) EK2: '1' in case there is an annotation of 'EK2', '0' if not,

(7) DS: '1' if the token is tagged as direct speech, '0' if not.

2. Csv

The csv files contain all annotations of the category PER including entity grounding.

The files contain the following columns:

begin and end (start and end of the entity reference expression, character offset),

doc_id (document id),

buch (book number),

quote (entity reference expression),

coref (the entity instance that the expression refers to),

overlap (indicates if there is an overlap, relevant for embedded entities),

ek and ek2 (narrator's comment),

ds (direct speech),

space (annotations of the space where the story takes action, can be ignored here),

segnr (number of segment),

em (embedded),

klasse (entity class),

xrange (technical, relevant for annotation view).

1. Gexf

These files can be used to import the data to gephi. It is based on the annotation and grounding of entities (categorie PER). A relation between entities is based on co-occurrence (whenever two or more entities co-occur in a segment, they have a relation; with more relations, the intensitiy of their relation grows). The text segmentation is described above.

Embedded entities are excluded. Entities mentioned in direct speech (DS) or in comments (EK) can optionally be selected or deselected. These optional filters are indicated in the name of the files.

To visualize the graph dynamically, one can use the text segmentation as timeline.

release v1.0.0: data publication in the context of my dissertation.

The number of social media users in Germany was forecast to continuously increase between 2024 and 2029 by in total **** million users (+***** percent). After the ninth consecutive increasing year, the social media user base is estimated to reach ***** million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Germany - Individuals using the internet for participating in social networks was 58.02% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Individuals using the internet for participating in social networks - last updated from the EUROSTAT on November of 2025. Historically, Germany - Individuals using the internet for participating in social networks reached a record high of 58.02% in December of 2024 and a record low of 41.91% in December of 2011.

This network was extracted from over 3 million German news articles between 1949 and 2019.

In the second quarter of 2023, Instagram and TikTok were the most popular social media platforms for German Gen Z consumers to purchase products. According to a survey, ** percent of respondents aged 18 to 26 stated that they had made purchases on Instagram, and on TikTok roughly ** percent of respondents had bought something. On the other hand, among older generations, Facebook stood as the preferred social media network for making purchases.

Forecast: Individuals Participating in Online Social Networks in Germany 2024 - 2028 Discover more data with ReportLinker!

** percent of German respondents answer our survey on "Most used social media platforms by type" with "Instant messengers (e.g., WhatsApp, Facebook Messenger, WeChat)". The survey was conducted in 2023, among ****** consumers.Find this and more survey data on most used social media platforms by type in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

Description

This dataset contains images and videos of social networks (static and dynamic graphs) based on character annotations of middle high german arthurian romances.

All images and videos are part of my dissertation.

The networks can be created in gephi using the gefx files provided in the following repositories:

• zenodo: 10.5281/zenodo.7544004
• and github: https://github.com/NoraKet/MHG4SNA/tree/v1.0.0

Texts

• Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.
• Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).
• Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.

Files

The network graphs are created using gephi.

The filenames indicate:

• the name of the text ("Erec", "Iwein", "Parzival"; "Parzival" consists of 16 books; it can be devided in the story of Parzival ("PARZ") and the story of Gawan ("GAW"));
• the filters "EK" and "EK2" which indicate if narrator's comments are included or excluded from the data;
• the filter "DS" which indicates if direct speech is included or excluded from the data;
• additional filters performed in gephi, for example removed characters ("noErec"), frequence filters ("filterfreq1": all characters that only occur one time are removed), selection of books ("Parzival_3-6": contains only books 3 to 6 from "Parzival"), "Egozentriert" indicates an egocentric network;
• for videos: the playback speed which can be set in gephi's settings.

For information about the annotations please see 10.5281/zenodo.7544004.

Germany Internet Users: Use of Social Networks: Male data was reported at 52.600 % in 2019. This records a decrease from the previous number of 54.600 % for 2018. Germany Internet Users: Use of Social Networks: Male data is updated yearly, averaging 53.300 % from Mar 2011 (Median) to 2019, with 8 observations. The data reached an all-time high of 63.000 % in 2015 and a record low of 49.000 % in 2013. Germany Internet Users: Use of Social Networks: Male data remains active status in CEIC and is reported by Federal Statistics Office Germany. The data is categorized under Global Database’s Germany – Table DE.TB001: Internet Users.

Social Media Monitoring of the German Federal Election Campaign 2017

This dataset contains results from the social media monitoring of Facebook and Twitter for the German federal election campaign 2017. The project collected the tweets and Facebook posts of political candidates and organizations and the engagement of users with these contents – retweets and @-mentions on Twitter, comments, shares and likes on Facebook. Finally, all messages on Twitter containing at least one keyword denoting central political topics were collected. All data was publicly available at the time of data collection. The collected data is proprietary and owned by Facebook and Twitter. Due to this and with respect to privacy restrictions, only the following aspects of the data can be shared:

(1) A list of all candidates that were considered in the project, their key attributes and the identification of their respective Twitter accounts and Facebook pages.

Candidate dataset: Full surname, all first names of the candidate; academic title and name pre- or suffixes (if they exist); URL of the first Facebook account; URL of the second Facebook account; URL of the Twitter account; candidate is placed on a party list; candidate’s place on the party list; candidate is a direct candidate in one of the constituencies; official number and official name of the constituency in which the candidate is running for a direct mandate; state; candidate is a member of the federal parliament (Bundestag); party of the candidate; sex, age (year of birth); place of residence; place of birth; profession.

Additionally coded was: unique ID.

(2) Lists of organizations relevant during an election campaign, i.e. political parties and important gatekeepers, along with their respective Twitter and Facebook accounts.

(3) A list of tweet IDs which can be used to retrieve the tweets we collected during our research period.

Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ACL’07, pages 209–212, Prague, Czech Republic. Reimers, N., and Gurevych, I. (2017). Reportingscore distributions makes a difference: Performancestudy of lstm-networks for sequence tagging. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 338–348, September 7–11, 2017, Copenhagen, Denmark. Yin, W., Schnabel, T. and Schütze, H. (2015). Online updating of word representations forpart-of-speech tagging. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP’15, pages 1329–1334. September 17-21, 2015, Lisbon, Portugal.

This dataset contains 43735 German tweet ids and corresponding annotations for Hate Speech label and 43734 German tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.

This statistic shows the share of individuals in Germany who participated in social networks from 2011 to 2018. In 2018, ** percent of individuals used social networks in Germany.

Co-authorship network of all 1339 (postdoctoral or professorial) political scientists in Germany in 2013, retrospectively for 2009-2013.

TikTok is developing into a key platform for news, advertising, politics, online shopping, and entertainment in Germany, with over 20 million monthly users. Especially among young people, TikTok plays an increasing role in their information environment. We provide a human-coded dataset of over 4,000 TikTok videos from German-speaking news outlets from 2023. The coding includes descriptive variables of the videos (e.g., visual style, text overlays, and audio presence) and theory-derived concepts from the journalism sciences (e.g., news values).

This dataset consists of every second video published in 2023 by major news outlets active on TikTok from Germany, Austria, and Switzerland. The data collection was facilitated with the official TikTok API in January 2024. The manual coding took place between September 2024 and December 2024. For a detailed description of the data collection, validation, annotation and descriptive analysis, please refer to [Forthcoming dataset paper publication].

When asked about "Social network usage by frequency", ** percent of German respondents answer "Several times a week". This online survey was conducted in 2023, among ***** consumers.Find this and more survey data on social network usage by frequency in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

Using a neo‐institutional framework, this study investigates how German police use Twitter for their communication. This is relevant, because the police increasingly use social media to bypass the media's gatekeeping function and attain increased agency in communication activities. However, little is known about their different communication strategies and their impact on user engagement. The analysis is based on an automated content analysis of all Tweets (N = 137,771) sent out by the German police in 2019. It identifies different patterns regarding the usage of Twitter between police entities on different levels of governance and in differing types of environments and examines how these patterns translate into different levels of user engagement. We find that German police entities differ in their use of Twitter and that environmental and organizational aspects can explain these differences. Moreover, different patterns of social media usage also lead to differing levels of user engagement.