10 research outputs found
Beyond Facts 2024 - 4th International Workshop on Computational Methods for Online Discourse Analysis: Companion Proceedings of the ACM Web Conference 2024, May 13–17, 2024, Singapore, Singapore
No full textInternational audienceExpressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting. Computational methods, mostly from the field of NLP, have emerged that tackle a wide range of tasks in this context, including argument and opinion mining, claim detection, checkworthiness detection, stance detection or fact verification. However, computational models require robust definitions of classes and concepts under investigation. Thus, these computational tasks require a strong interdisciplinary and epistemological foundation, specifically with respect to the underlying definitions of key concepts such as claims, arguments, stances, check-worthiness or veracity. This requires a highly interdisciplinary approach combining expertise from fields such as communication studies, computational linguistics and computer science. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities. From a computational perspective, in order to address this complexity, the synergy of multiple approaches, coming both from symbolic (knowledge representation) and statistical AI seem to be promising to tackle such challenges. This workshop aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientific or journalistic studies into the analysis of societal debates on the Web. Beyond research into information and knowledge extraction, data consolidation and modeling for knowledge graphs building, the workshop targets communities focusing on the analysis of online discourse, relying on methods from machine learning, natural language processing, large language models and Web data mining
ClaimsKG: A Knowledge Graph of Fact-Checked Claims
No full textInternational audienceVarious research areas at the intersection of computer and social sciences require a ground truth of contextualized claims labelled with their truth values in order to facilitate supervision, validation or reproducibility of approaches dealing, for example, with fact-checking or analysis of societal debates. So far, no reasonably large, up-to-date and queryable corpus of structured information about claims and related metadata is publicly available. In an attempt to fill this gap, we introduce ClaimsKG, a knowledge graph of fact-checked claims, which facilitates structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. ClaimsKG is generated through a semi-automated pipeline, which harvests data from popular fact-checking websites on a regular basis, annotates claims with related entities from DBpedia, and lifts the data to RDF using an RDF/S model that makes use of established vocabularies. In order to harmonise data originating from diverse fact-checking sites, we introduce normalised ratings as well as a simple claims coreference resolution strategy. The current knowledge graph, extensible to new information, consists of 28,383 claims published since 1996, amounting to 6,606,032 triples
Beyond Facts - a Survey and Conceptualisation of Claims in Online Discourse Analysis
Get PDFInternational audienceAnalyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model — Open Claims — for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis
Truth or Dare: Investigating Claims Truthfulness with ClaimsKG
No full textInternational audienceSearching and exploring online information is fundamental for our society. However, it is common to find inaccurate information on the Internet, that can quickly spread and be hard to identify. Fortunately, today, many fact-checking sources verify online information to provide online users with a means to recognize its truthfulness. These sources use different languages and scoring systems, which makes fact validation challenging and time-consuming. To address this issue, we propose a new release of ClaimsKG, a knowledge graph of about 59,580 claims, which covers 13 different fact-checking sources and provides a structured way to retrieve verified online claims. ClaimsKG is built using a pipeline that makes use of entity linking and disambiguation tools to fetch entities from DBpedia and an ad-hoc scoring normalization system. ClaimsKG is used as a showcase to provide the public with interesting and verified information about events of our times
Truth or Dare: Investigating Claims Truthfulness with ClaimsKG
No full textInternational audienceSearching and exploring online information is fundamental for our society. However, it is common to find inaccurate information on the Internet, that can quickly spread and be hard to identify. Fortunately, today, many fact-checking sources verify online information to provide online users with a means to recognize its truthfulness. These sources use different languages and scoring systems, which makes fact validation challenging and time-consuming. To address this issue, we propose a new release of ClaimsKG, a knowledge graph of about 59,580 claims, which covers 13 different fact-checking sources and provides a structured way to retrieve verified online claims. ClaimsKG is built using a pipeline that makes use of entity linking and disambiguation tools to fetch entities from DBpedia and an ad-hoc scoring normalization system. ClaimsKG is used as a showcase to provide the public with interesting and verified information about events of our times
Exploring Fact-checked Claims and their Descriptive Statistics
No full textInternational audienceClaimsKG is a RDF knowledge graph of fact-checked claims and related metadata, such as their truth values, authors or dates. It gathers information from popular fact-checking websites, annotates claims with related entities from DBpedia, and lifts the data into RDF by using a dedicated RDFS model. We present two open source, user-friendly Web-platforms operating on top of ClaimsKG: (1) the ClaimsKG Explorer an engine to conduct ad-hoc/faceted search over the graph, and (2) the ClaimsKG Statistical Observatory-a tool allowing to extract and visualize detailed statistics of the ClaimsKG data
Truth or Dare: Investigating Claims Truthfulness with ClaimsKG
No full textInternational audienceSearching and exploring online information is fundamental for our society. However, it is common to find inaccurate information on the Internet, that can quickly spread and be hard to identify. Fortunately, today, many fact-checking sources verify online information to provide online users with a means to recognize its truthfulness. These sources use different languages and scoring systems, which makes fact validation challenging and time-consuming. To address this issue, we propose a new release of ClaimsKG, a knowledge graph of about 59,580 claims, which covers 13 different fact-checking sources and provides a structured way to retrieve verified online claims. ClaimsKG is built using a pipeline that makes use of entity linking and disambiguation tools to fetch entities from DBpedia and an ad-hoc scoring normalization system. ClaimsKG is used as a showcase to provide the public with interesting and verified information about events of our times
ClaimsKG - A Knowledge Graph of Fact-Checked Claims (August, 2022)
No full textClaimsKG 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 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
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 4, January 2021 - August 2022)
No full textTweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.
We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:
1. Tweet Id: Long.
2. Username: String. Encrypted for privacy issues.
3. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).
4. #Followers: Integer.
5. #Friends: Integer.
6. #Retweets: Integer.
7. #Favorites: Integer.
8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".
9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").
10. Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".
11. Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".
12. URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"
To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.
You can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.
We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:
1. Tweet Id: Long.
2. Username: String. Encrypted for privacy issues.
3. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).
4. #Followers: Integer.
5. #Friends: Integer.
6. #Retweets: Integer.
7. #Favorites: Integer.
8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".
9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").
10. Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".
11. Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".
12. URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"
To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.
You can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523
ClaimsKG - A Knowledge Graph of Fact-Checked Claims (January, 2023)
No full textClaimsKG 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.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
