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The availability of annotated data is an important prerequisite for the development of machine learning
algorithms for sentiment analysis. However, as manually labeling large datasets is time-consuming
and expensive, few datasets are available and most of them represent a small sample of a very narrow
domain, e.g. movie reviews or reviews of a certain product type. Additionally, many annotated datasets
are available for English texts only. However, the influence of different characteristics of the input
dataset on the performance of algorithms for sentiment analysis remains unclear if only training data
from one specific domain is available or if specific domains are mixed in the test corpus. We therefore
introduce a new dataset for German product reviews of various product types and investigate whether
even small variances in this specific domain (different product types) already exhibit different characteristics,
e.g. with regard to the difficulty of sentiment annotation. The annotation of this corpus lays
the basis for future enhanced annotations of similar corpora and for the extension of our annotations
to corpora of inherently different domains. These will then serve to investigate the influence of different
corpus characteristics on different algorithms for sentiment analysis and as a basis to apply machine
learning methods for sentence-wise sentiment analysis for German texts.

ClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics.

The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org). 

The latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ .

The first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations.

ClaimsKG is a knowledge graph of metadata information for fact-checked claims scraped from popular fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonized and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model, query examples and statistics.

The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).

The latest release of ClaimsKG covers 74066 claims and 72127 Claim Reviews. This is the fourth release of the dataset where data was scraped till Jan 31, 2023 containing claims published between 1996 and 2023 from 13 fact-checking websites. The websites are Fullfact, Politifact, TruthOrFiction, Checkyourfact, Vishvanews, AFP (French), AFP, Polygraph, EU factcheck, Factograph, Fatabyyano, Snopes and Africacheck. The claim-review (fact-checking) period for claims ranges between the year 1996 to 2023. Similar to the previous release, the Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. Improvements have been made in the web scraping and data preprocessing pipeline to extract more entities from both claims and claims reviews. Currently, ClaimsKG contains 3408386 entities detected and referenced with DBpedia.

This latest release of ClaimsKG supersedes the previous versions as it contained all the claims from the previous versions together in addition to the additional new claims as well as improved entity annotation resulting in a higher number of entities.