In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

The graph shows data on the types of social media regularly used by journalists from the United States in 2016. According to the source, ** percent of the respondents stated that they use social networks such as Facebook for sourcing.

The graph shows a list of problems facing journalism in the United States as of early 2016. Among responding news organizations, 29 percent reported revenue as the most important problem facing journalism in the United States.

Traditional ownership databases (like Orbis, Refinitiv, PitchBook) give corporate trees, but not outlet-level alignment or explainability. Web or registry scraping adds fragments, but no standardization.This segment blends structured feeds from trusted corporate data APIs with Starzdata Smart Queries, harmonized into one taxonomy. For consultants, regulators, and foresight units, it means moving from scattered, static ownership charts to a refreshable, explainable map of who controls media outlets — and how concentrated, cross-border, or financially resilient they are.

During a 2025 survey, ** percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just ** percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis. Social media: trust and consumption Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than ** percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than ** percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media. What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis. Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers. Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

United States - Breakdown of Revenue by Media Type: Newspapers - Print Newspapers for Newspaper Publishers, All Establishments, Employer Firms was 12824.00000 Mil. of $ in January of 2021, according to the United States Federal Reserve. Historically, United States - Breakdown of Revenue by Media Type: Newspapers - Print Newspapers for Newspaper Publishers, All Establishments, Employer Firms reached a record high of 26973.00000 in January of 2010 and a record low of 12824.00000 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Breakdown of Revenue by Media Type: Newspapers - Print Newspapers for Newspaper Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on September of 2025.

Network of 28 papers and 33 citation links related to "Selection of Universities by Students in Journalism and Mass Communication Courses".

Out-of-sample F-score and Area Under Receiver Operating Characteristic (AUROC) of random forest (RF) and k-nearest neighbors (k-NN) classifiers trained on truth scores computed by the fact checker, using either the transitive closure or solely information from infoboxes.Ideological classification results.

Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed.

The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.

We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.

In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization.

Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information:

1. A description of the use case
2. Affiliation of the researchers involved
3. How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

The graph illustrates the changes felt in news media content according to journalists in the United States as of January 2014. Whereas no journalists believed that popular search terms had influenced their editorial decisions, 50 percent stated that the potential popularity of a story sometimes trumped news value.

The graph shows the views expressed by journalists when asked about the impact of social media on their work in the United States in 2016. According to the source, 48 percent of journalists in the United States claimed that they would not be able to carry out their work without social media.

Graph and download economic data for Media and Communications Job Postings on Indeed in the United States (IHLIDXUSTPMECO) from 2020-02-01 to 2025-09-12 about communication, jobs, and USA.

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

Diluted-EPS Time Series for Polaris Media. Polaris Media ASA operates as a media house and printing company in Norway and Sweden. The company provides digital advertising services. It offers prints group and external newspapers, including editorial supplements and advertisement leaflets, civil prints, and other magazines/leaflets. In addition, the company distributes newspapers, as well as parcels, mail, and other product deliveries. Polaris Media ASA was founded in 2008 and is headquartered in Trondheim, Norway.

The MeMAD Knowledge Graph implements the EBU Core ontology and contains structured descriptions of more than 90k Radio and TV programs as well as more than 100k parts (segments) from 100 channels made available by INA (France) and Yle (Finland). This represents more than 64k hours of content in French, Finnish and Swedish.

The dataset contains:

the MeMAD ontology that extends the EBU Core ontology

the MeMAD controlled vocabularies represented in SKOS that interlink the programs genres, themes and the person roles using and extending the referenced EBU controlled vocabularies

a number of graphs encapsulating the legacy metadata coming from the INA Legal Deposit, the INA Professional Archive and the Yle archive

a graph encapsulating the automatic multimodal content analysis performed on some of those programs: this includes results from automatic speech recognition (ASR), extracting and disambiguating named entities from the ASR, face recognition from the video frames, machine translation of the ASR and visual deep captions generation

Derivatives of the Web Archive of Independent News Sites on Turkish Affairs collection from the Ivy Plus Libraries Confederation. The derivatives were created with the Archives Unleashed Toolkit and Archives Unleashed Cloud.

The ivy-12911-parquet.tar.gz derivatives are in the Apache Parquet format, which is a columnar storage format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See this notebook for examples.

Domains

.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)

Produces a DataFrame with the following columns:

• domain
• count

Web Pages

.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))

Produces a DataFrame with the following columns:

• crawl_date
• url
• mime_type_web_server
• mime_type_tika
• content

Web Graph

.webgraph()

Produces a DataFrame with the following columns:

• crawl_date
• src
• dest
• anchor

Image Links

.imageLinks()

Produces a DataFrame with the following columns:

• src
• image_url

Binary Analysis

• Audio
• Images
• PDFs
• Presentation program files
• Spreadsheets
• Text files
• Word processor files
  

The ivy-12911-auk.tar.gz derivatives are the standard set of web archive derivatives produced by the Archives Unleashed Cloud.

• Gephi file, which can be loaded into Gephi. It will have basic characteristics already computed and a basic layout.
• Raw Network file, which can also be loaded into Gephi. You will have to use that network program to lay it out yourself.
• Full text file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.
• Domains count file. A text file containing the frequency count of domains captured within your web archive.

Total-Revenue Time Series for Agile Media Network. Agile Media Network Inc. engages in the online advertising delivery agency; internet-based informational; internet-related system development, and seminars and events; and publishing businesses in Japan and other Asian regions. It also offers Ambassador platform, a fan development and activation solution. Agile Media Network Inc. was incorporated in 2007 and is based in Tokyo, Japan.

This group of files represents the RDF ontology used in the Japanese Visual Media Graph for the Anime Characters Database. Included in the upload are an explanatory PDF, the ontology in the Turtle serialization, and an HTML visualization of the ontology.