A fileset to accompany an article in a special issue of Internet Archaeology. In this article, I map the structure of the web to understand the context of archaeological blogging. What is the context of our archaeological blogging? When we blog, are we merely shouting into the void? Do archaeological bloggers link only to one another, and do we shout only to each other (which, it must be admitted, is what our journals and conferences do, too, albeit at slower pace)? Assume a person knows nothing about archaeology: would that person find your blog? Your project website? Your department’s website? Does academic blogging matter? One way to answer these questions is through a mapping of the archaeological web. When a layperson finds a site, she might signal its perceived value through linking, retweeting, commenting, and writing her own blog posts about it. Therefore, various network metrics of this map of the archaeological web can be taken as a kind of proxy for evaluating the impact of our blogging. Given that these blogs are all publicly available (if one knows or can find the address), blogging is a kind of public archaeology- not necessarily an archaeology done for the public, but rather an archaeology done in view of the public. It would be interesting to know if this kind of public archaeology has an impact at all. These signals and linkages in the general noise of the internet are the subject of this paper. In order for us as archaeologists to generate the strongest possible signals on the web, we need to understand the structures that have emerged within the web to best facilitate dissemination. This can help us increase our signals’ visiblity, even though all roads eventually lead to Wikipedia.

Arcade is a powerful scripting language built into the ArcGIS platform to help you transform your data on-the-fly. This opens many doors for mapping and analysis by reducing the amount of steps needed to move from raw data to a compelling narrative. This story map provides an introduction to Arcade, along with examples and steps for getting started with Arcade within ArcGIS Online. To learn more about Arcade, check out the following resources:Getting Started with ArcadeArcGIS Arcade Documentation PagesArcGIS Blogs about Arcade ExpressionsArcade Function Reference

This vector tile layer presents the World Topographic Map (Local Language) style (World Edition) and provides a basemap for the world, symbolized with a classic Esri topographic map style. This layer includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries, designed for use with World Hillshade for added context. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Topographic (Local Language) web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

This basemap was designed with the Vizzuality team for use in the Half-Earth Project globe. The saturated palette and rich landcover tones are meant to engage an audience and to provide the sense that the earth is a charming and beautiful place worthy of thoughtful stewardship. As you zoom in, the saturated basemap is slowly replaced by imagery.This basemap is the major component of the Vibrant Map. The Vibrant Map is configured to use these basemap tiles from global to regional extents, then transition to Esri's World Imagery basemap tiles for a seamless transition from small to large scale.Find more information about this basemap, and its contributing data, here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/creating-the-half-earth-vibrant-basemap/Learn more about the Half-Earth Project here and explore highlighted areas of biodiversity here.Happy Mapping! John

This vector tile layer presents the Human Geography Dark Detail style (World Edition) and provides a detailed vector basemap for the world with a dark monochromatic style and content adjusted to support Human Geography information. This layer is a detailed reference layer including administrative boundaries, roads and highways. The map includes highways, major roads, minor roads, railways, water features, building footprints, and administrative boundaries. It is designed to be used with the Human Geography Dark Label and Human Geography Dark Base layers. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Human Geography Dark Map web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

This vector tile layer presents the World Street Map (with Relief - Community Maps) style (World Edition) and provides a basemap for the world, symbolized with a classic Esri street map style. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Where provided, data for these features, including roads and administrative lines, are from Community Map contributors. This map is designed to be used with World Hillshade. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Streets (with Relief - Community Maps) web map.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

This web map contains the Bing Maps aerial imagery web mapping service, which offers worldwide orthographic aerial and satellite imagery. Coverage varies by region, with the most detailed coverage in the USA and United Kingdom. Coverage in different areas within a country also varies in detail based on the availability of imagery for that region. Bing Maps is continuously adding imagery in new areas and updating coverage in areas of existing coverage. This map does not include bird's eye imagery. Information regarding monthly updates of imagery coverage are available on the Bing Community blog. Post a comment to the Bing Community blog to request imagery vintage information for a specific area.Tip: The Bing Maps Aerial service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Bing Maps Aerial from the Basemap control to start browsing! You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.If you need information on how to access Bing Maps, information is available in the ArcGIS Online Content Resource Center.See Bing Maps (http://www.bing.com/maps) for more information about the Bing Maps mapping system, terms of use, and a complete list of data suppliers.

This vector tile layer presents the World Navigation Map (Places) style (World Edition) and provides a basemap for the world, featuring a Navigation style designed for use during the day in mobile devices with the additional content of global Places. These shops, services, restaurants, attractions, and other points of interest are displayed with icons and labels. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.The Places data sources in this map include:United States and Canada: SafeGraphrest of the World: TomTomThis layer is used in the Navigation (Places) web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

This blog post was posted by Cara James on April 20, 2016. It was written by Cara V. James, Ph.D., Director of the Office of Minority Health at the Centers for Medicare and Medicaid Services

Mapping coronavirus coxcombs (ArcGIS Blog)._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Overview

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterisation or analyses of misinformation spreading.

Its novelty and our main contributions lie in (1) focus on medical news article and blog posts as opposed to social media posts or political discussions; (2) providing multiple modalities (beside full-texts of the articles, there are also images and videos), thus enabling research of multimodal approaches; (3) mapping of the articles to the fact-checked claims (with manual as well as predicted labels); (4) providing source credibility labels for 95% of all articles and other potential sources of weak labels that can be mined from the articles' content and metadata.

The dataset is associated with the research paper "Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims" accepted and presented at ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).

The accompanying Github repository provides a small static sample of the dataset and the dataset's descriptive analysis in a form of Jupyter notebooks.

Options to access the dataset

There are two ways how to get access to the dataset:

1. Static dump of the dataset available in the CSV format
2. Continuously updated dataset available via REST API

In order to obtain an access to the dataset (either to full static dump or REST API), please, request the access by following instructions provided below.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following papers:

@inproceedings{SrbaMonantPlatform,
  author = {Srba, Ivan and Moro, Robert and Simko, Jakub and Sevcech, Jakub and Chuda, Daniela and Navrat, Pavol and Bielikova, Maria},
  booktitle = {Proceedings of Workshop on Reducing Online Misinformation Exposure (ROME 2019)},
  pages = {1--7},
  title = {Monant: Universal and Extensible Platform for Monitoring, Detection and Mitigation of Antisocial Behavior},
  year = {2019}
}

@inproceedings{SrbaMonantMedicalDataset,
  author = {Srba, Ivan and Pecher, Branislav and Tomlein Matus and Moro, Robert and Stefancova, Elena and Simko, Jakub and Bielikova, Maria},
  booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)},
  numpages = {11},
  title = {Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims},
  year = {2022},
  doi = {10.1145/3477495.3531726},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3477495.3531726},
}

Dataset creation process

In order to create this dataset (and to continuously obtain new data), we used our research platform Monant. The Monant platform provides so called data providers to extract news articles/blogs from news/blog sites as well as fact-checking articles from fact-checking sites. General parsers (from RSS feeds, Wordpress sites, Google Fact Check Tool, etc.) as well as custom crawler and parsers were implemented (e.g., for fact checking site Snopes.com). All data is stored in the unified format in a central data storage.


Ethical considerations

The dataset was collected and is published for research purposes only. We collected only publicly available content of news/blog articles. The dataset contains identities of authors of the articles if they were stated in the original source; we left this information, since the presence of an author's name can be a strong credibility indicator. However, we anonymised the identities of the authors of discussion posts included in the dataset.

The main identified ethical issue related to the presented dataset lies in the risk of mislabelling of an article as supporting a false fact-checked claim and, to a lesser extent, in mislabelling an article as not containing a false claim or not supporting it when it actually does. To minimise these risks, we developed a labelling methodology and require an agreement of at least two independent annotators to assign a claim presence or article stance label to an article. It is also worth noting that we do not label an article as a whole as false or true. Nevertheless, we provide partial article-claim pair veracities based on the combination of claim presence and article stance labels.

As to the veracity labels of the fact-checked claims and the credibility (reliability) labels of the articles' sources, we take these from the fact-checking sites and external listings such as Media Bias/Fact Check as they are and refer to their methodologies for more details on how they were established.

Lastly, the dataset also contains automatically predicted labels of claim presence and article stance using our baselines described in the next section. These methods have their limitations and work with certain accuracy as reported in this paper. This should be taken into account when interpreting them.


Reporting mistakes in the dataset
The mean to report considerable mistakes in raw collected data or in manual annotations is by creating a new issue in the accompanying Github repository. Alternately, general enquiries or requests can be sent at info [at] kinit.sk.

Dataset structure

Raw data

At first, the dataset contains so called raw data (i.e., data extracted by the Web monitoring module of Monant platform and stored in exactly the same form as they appear at the original websites). Raw data consist of articles from news sites and blogs (e.g. naturalnews.com), discussions attached to such articles, fact-checking articles from fact-checking portals (e.g. snopes.com). In addition, the dataset contains feedback (number of likes, shares, comments) provided by user on social network Facebook which is regularly extracted for all news/blogs articles.

Raw data are contained in these CSV files (and corresponding REST API endpoints):

• sources.csv
• articles.csv
• article_media.csv
• article_authors.csv
• discussion_posts.csv
• discussion_post_authors.csv
• fact_checking_articles.csv
• fact_checking_article_media.csv
• claims.csv
• feedback_facebook.csv

Note: Personal information about discussion posts' authors (name, website, gravatar) are anonymised.

Annotations

Secondly, the dataset contains so called annotations. Entity annotations describe the individual raw data entities (e.g., article, source). Relation annotations describe relation between two of such entities.

Each annotation is described by the following attributes:

1. category of annotation (`annotation_category`). Possible values: label (annotation corresponds to ground truth, determined by human experts) and prediction (annotation was created by means of AI method).
2. type of annotation (`annotation_type_id`). Example values: Source reliability (binary), Claim presence. The list of possible values can be obtained from enumeration in annotation_types.csv.
3. method which created annotation (`method_id`). Example values: Expert-based source reliability evaluation, Fact-checking article to claim transformation method. The list of possible values can be obtained from enumeration methods.csv.
4. its value (`value`). The value is stored in JSON format and its structure differs according to particular annotation type.

At the same time, annotations are associated with a particular object identified by:

1. entity type (parameter entity_type in case of entity annotations, or source_entity_type and target_entity_type in case of relation annotations). Possible values: sources, articles, fact-checking-articles.
2. entity id (parameter entity_id in case of entity annotations, or source_entity_id and target_entity_id in case of relation

Mapping Coronavirus COVID-19 responsibly (ArcGIS Blog).Access to almost real-time data and the need to communicate drives rapid mapping. But thinking remains crucial._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This web map contains the Bing Maps aerial imagery with labels web mapping service, which provides worldwide orthographic aerial and satellite imagery with roads and labels overlaid. Coverage varies by region, with the most detailed coverage in the USA and United Kingdom. Coverage in different areas within a country also varies in detail based on the availability of imagery for that region. Bing Maps is continuously adding imagery in new areas and updating coverage in areas of existing coverage. This map does not include bird's eye imagery. Information regarding monthly updates of imagery coverage are available on the Bing Community blog. Post a comment to the Bing Community blog to request imagery vintage information for a specific area.Tip: The Bing Maps Hybrid service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Bing Maps Hybrid from the Basemap control to start browsing! You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.If you need information on how to access Bing Maps, information is available in the ArcGIS Online Content Resource Center.See Bing Maps (http://www.bing.com/maps) for more information about the Bing Maps mapping system, terms of use, and a complete list of data suppliers.

This vector tile layer presents the World Navigation Map (Dark) style (World Edition) and provides a basemap for the world, featuring a 'dark mode' version of the Navigation vector basemap. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. It is designed to be an alternative for low light conditions, or for users who prefer to work with a darker basemap. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Navigation (Dark) web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

This vector tile layer presents the World Street Map (Night - Local Language) style (World Edition) and provides a basemap for the world, symbolized with a custom street map style that is designed for use at night or in other low-light environments. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Labels are in local languages at large scale. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Streets (Night - Local Language) web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. For more information, visit: https://ai.facebook.com/blog/mapping-the-world-to-help-aid-workers-with-weakly-semi-supervised-learning

Datasets of Aboriginal missions, reserves, stations and carceral institutions in so-called Australia from 1788-2020. Includes description of data collection process for missions, stations and reserves. Sites were predominantly drawn from Find and Connect’s Map of Children’s Homes (https://www.findandconnect.gov.au/blog/map-of-childrens-homes/) and also includes a dataset from another project, NSW Aborigines Protection/Welfare Board 1883-1969 Map (DP150100247). A range of other sources were used as listed in ‘reference’ columns. Data was collected as part of Australian Research Council Discovery Project, Revitalising Indigenous-state relations in Australia (DP200101016 – the University of Melbourne).

This data was collected as part of the Map the Moment initiative, a volunteer project to document the artwork and changes to the community following the killing of George Floyd and the demonstrations that followed. This data was collected by Alan White and processed by Carleton University. White used a Nikon D3300 to scan the murals that adorned the boarded up retail units. At a time when other boardings around the city of Santa Monica were being removed, this site was one of the most intact artworks remaining. Painted in a coordinated effort by #PaintTheCityPeaceful, talented artists delivered an important message which must remain beyond the moment. These murals were painted on the corner of Wilshire Blvd. and 5th St. in Santa Monica, CA and advocate for equality, justice, and peace. External Project Link: \N Additional Info Link: https://cyark.org/about/blog/?p=map-the-moment

This data was collected as part of the Map the Moment initiative, a volunteer project to document the artwork and changes to the streetscape following the killing of George Floyd and the demonstrations that followed. This data was collected by Lisa Conte and processed by Joe Graham-Felsen. They used a Canon 5D Mark 3 to scan this data and the various murals that appeared throughout the city. New York City Hall sits in Lower Manhattan and is the oldest continuously operating city hall in the United States. During the summer????????s Black Lives Matter demonstrations, the site saw hundreds of protesters, many of whom were seeking police reform and a lower NYPD budget. External Project Link: \N Additional Info Link: https://cyark.org/about/blog/?p=map-the-moment

This web map provides a detailed vector basemap for the world symbolized with a custom street map style that is designed for use at night or in other low-light environments. The web map includes a vector tile layer that is similar in content to the popular World Street Map, which is delivered as a tile layer with raster fused map cache. This map includes a vector tile layer that provides unique capabilities for customization and high-resolution display.