Social Animal 10K Articles with NLP

Text, Shares and NLP metadata from trending news articles and blog posts around the world.

The metadata for the 10K articles is present in the articles.csv file. The article text is available as a text file inside the text directory. The file name is available as the content column in the CSV file.

CSV File Header Documentation

The following is a documentation of the headers present in the CSV file:

• url: The URL of the article.
• domain: The domain or website where the article is published.
• title: The title of the article.
• authors: The author(s) of the article.
• content: The content or text of the article. (File name of the text file in the text directory)
• content_word_count: The word count of the article's content.
• published_date: The date when the article was published.
• created_at: The timestamp indicating when the article entry was created.
• updated_at: The timestamp indicating when the article entry was last updated.
• language: The language in which the article is written.
• article_links: Any links or references present within the article.
• total_share_count: The total count of shares for the article.
• article_category: The category or topic to which the article belongs.
• keywords: The keywords associated with the article.
• entities: Any entities mentioned in the article (e.g., names of people, organizations).
• sentiment: The sentiment analysis result for the article.
• title_sentiment: The sentiment analysis result for the article's title.
• keywords_ex: Extra or additional keywords related to the article.
• content_types.listicle: Indicates if the article is a listicle.
• content_types.infographic: Indicates if the article contains an infographic.
• content_types.how_to: Indicates if the article is a how-to guide.
• content_types.case_study: Indicates if the article is a case study.
• content_types.guest_post: Indicates if the article is a guest post.
• content_types.review: Indicates if the article is a review.
• content_types.video: Indicates if the article contains a video.
• content_types.podcast: Indicates if the article contains a podcast.
• content_types.webinar: Indicates if the article contains a webinar.
• content_types.interview: Indicates if the article is an interview.
• content_types.quote: Indicates if the article contains a quote.
• content_types.meme: Indicates if the article contains a meme.
• content_types.give_away: Indicates if the article is a giveaway.
• content_types.quiz: Indicates if the article is a quiz.
• locations.cities_and_states: Cities and states mentioned or relevant to the article.
• locations.countries: Countries mentioned or relevant to the article.
• locations.geo_locations: Geographical locations associated with the article.

Reach out if you need more data

We have more than 450 million articles in our database. We have picked 10K articles from this database and shared it here. We continuously collect text articles from various sources and process them with our NLP pipeline. Our database contains near real time data on trending articles from around the world. Reach out to alex@socialanimal.io if you are interested in getting access to our API or database. www.socialanimal.com

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.

We are pleased to announce that LNDS has successfully completed its Annual Report of 2023. A fruit of rigorous and detailed work, the Annual Report highlights the achievements, challenges and prospects of its first full year in operation. Transparency is one of the key values of LNDS. As a sign of living its values, in addition to the Annual Report, LNDS has also made available all public data files associated with this report. These data files enable a more in-depth analysis and offer greater transparency on the past year's activities. This is a commendable initiative on the part of LNDS, demonstrating our commitment to openness and accountability. Each data file corresponds to a section of the Annual Report and shows the data that has been used to create the content, and most notably the data visualisations and infographics.

Introduction

ViInfographicsVQA is a Vietnamese Visual Question Answering (VQA) dataset constructed from infographics sourced from 26 different news platforms. The dataset is designed to support research in multimodal learning by providing diverse questions and answers based on real-world visual data. The detailed distribution of sources is presented in the table below.

Figure 1: The number of infographics per news source.

Developed by: @Namronaldo2004, @Kiet2302… See the full description on the dataset page: https://huggingface.co/datasets/Namronaldo2004/ViInfographicsVQA.

The coronavirus is moving the world. The media landscape is also dominated by this topic. Up-to-date information on the corona situation is given out almost every minute in live tickers. But what is missing are current data visualizations on the cases of illness. Although there are data applications, such as the representations of the WHO, these have the global distribution in view. Static infographics, which are often no longer up-to-date at the time of publication, cannot do justice to the rapidly changing situation. The virus has arrived in Europe, now visualizations that the individual states have in view are in demand and as up-to-date as possible. Our developed data dashboard shows the current distribution on a state and district basis in maps, diagrams and lists. We try to keep the presentations as up-to-date as possible and research current data from the website of the Ministry of Social Affairs, via data.gv.at or search in Austrian news portals.

This is a dataset containing the population of each Member State from the 2011 and 2021 censuses held by Eurostat, and the current distribution of seats in the European Parliament (EP). The population data was downloaded from the official website of Eurostat (2011: https://ec.europa.eu/eurostat/web/population-demography/population-housing-censuses/database; 2021: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_and_housing_census_2021_-_population_grids&stable=1#Distribution_of_European_population). The seat data was obtained from the official website of the EP (https://www.europarl.europa.eu/news/en/headlines/eu-affairs/20180126STO94114/infographic-how-many-seats-does-each-country-get-in-in-the-european-parliament).

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