In recent years, the advent of deep learning for NLP is enabling the accurate retrieval of semantically similar content. Such retrieval, along with check-worthiness and stance detection, is crucial for identifying misinformation and linking it to relevant verified information. While sources of credibility signals (and methods to retrieve them) are abundant on the web, they vary greatly in terms of quality and relevance and can be quite scarce for specific claims. These special requirements for such IR systems suggest the need for good abstractions to represent relevant aspects like the credibility of retrieved information and the confidence of automated systems that retrieved the information. In this paper, we (i) summarise Linked Credibility Reviews, existing work that provides a conceptualisation and exchange format for representing the credibility of retrieved verified information and (ii) discuss the role this conceptualisation can play in information retrieval systems for reducing online misinformation.
The Web and social media have ushered an era of ultra-fast spreading of messages without the need for centralized media like publishing houses, newspapers, radio or TV channels. However, this lack of centralization also entails a lack of editorial and quality control over the messages that spread online. This misinforming capacity of the web and social media is increasingly being exploited and is having a detrimental efect on society as evidenced by problems like political polarization and interference in democratic and policymaking processes.
Information retrieval is bound to play a crucial role in reducing online misinformation as it has the potential to bring back some of the quality control that was lost in the transition from traditional media to the web. However, traditional information retrieval metrics and approaches cannot be directly applied if they are to be used to spot and limit the spread of misinformation. Additional aspects like credibility and harmfulness have to be taken into account when determining relevancy of results and other requirements like explainability, reproducibility, trust and decentralisation have to be taken into account. All of this means that datasets and information retrieval systems need to be aware of the wider online context where they are deployed and this requires a well designed conceptual model and vocabulary to publish and share such artifacts on-line.
In this paper, we summarize existing work on such conceptual models and vocabularies produced recently by the semantic web research community. Rather than presenting new research, our intention is to build a bridge between the information retrieval and semantic web communities as we believe such collaboration is necessary to successfully tackle misinformation online.
As mentioned above, information retrieval systems for online misinformation reduction have several new requirements in addition to standard IR systems. In this section, we enumerate and describe these additional requirements.
1. New relevancy aspects: In standard information similarity, content (and metadata) similarity are the main aspects that determine relevancy of retrieved results. In the context of misinformation, aspects like credibility, stance and harmfulness also need to be considered. 2. Subjectivity: Aspects like credibility and harmfulness are subjective as they depend on the content being evaluated, the evaluation method and the background information used to evaluate both aspects. 3. Explainability: IR systems should be able to explain the reason why retrieved content was deemed relevant, credible or harmful. 4. Reproducibility: When possible, the methods used should be reproducible by third parties, especially end users. This allows end users to verify the methods and results even if they are not aligned with their subjective views on, for example, credibility. 5. Trust: Since full reproducibility or explainability are not always possible (or desirable), the final credibility sources should be inspectable. This allows end users to build (or remove) trust with those sources. 6. Decentralizable: Systems that aim to aid in online misinformation reduction should be designed with web-scale in mind. Similarly, the subjectivity and trust requirements means that a single source of credible information is not possible and should be avoided.
All of this is best achieved if such systems are designed to be decentralizable.
All these requirements mean that IR systems to be used for misinformation reduction should
be designed to address these needs. In particular, providing a list of document identifiers (and
their credibility) as the output of a IR system clearly is insuficient. Recent work by Denaux
and Gomez-Perez [
Linked Credibility Reviews (LCR) [
A Credibility Review (CR) is an extension of the generic Review data model defined in Schema.org. A Review R can be conceptualised as a tuple (, , ) where R: • reviews a data item , via property itemReviewed, this can be any data node (e.g. an article, claim or social media post). • assigns a numeric or textual rating to (some, often implicit, reviewAspect of) , via property reviewRating • optionally provides provenance information , e.g. via properties author and isBasedOn.
A Credibility Review (CR) is a subtype of Review, defined as a tuple ⟨, , ,
CR:
⟩, where the
• must have reviewAspect credibility and is recommended to be expressed as a
numeric value in range [
While schema.org provides most of the vocabulary needed to describe CRs, Denaux and Gomez-Perez had to extend it slightly to be able to express the proposed model. The relevant fragment of schema.org and the proposed extensions are depicted in figure 1, focused on CRs for textual web content (although notice that the model is also applicable to other modalities).
Besides the conceptual model and vocabulary, in [
Acred is an example implementation for the Linked Credibility Review architecture, also
presented in [
For information retrieval, acred uses a sentence encoder trained on STS-B [6] (similar to
Sentence BERT [7]) and FAISS [8] to index the 85K sentences as well as a Solr instance to store
further metadata about associated ClaimReviews or sites where the sentence was published.
A second model is used to perform stance detection, which provides polarity which is
necessary for correctly propagating credibility values. Heuristic rules are used to combine the
credibility confidence and the similarity between the query and database sentences. Given an
input document, a third model is used to select query sentences which are factual and
checkworthy [9] and further heuristics are used to select the best combination of highest confidence
and lowest credibility. See the original paper [
Acred was evaluated on the Clef’18 CheckThat! Factuality Task [10] task and achieved state
of the art results with a 0.6475 MAE (compared to the previous score of 0.7050 [11]). Similarly,
acred obtained an accuracy of 0.716 on the PolitiFact fragment of FakeNewsNet, which is also
state of the art when not using social signals (shares, replies and comments on Twitter to
the original article). Both of these results are remarkable when considering that acred does
not require a training step since all its steps rely on pre-trained models. Note as well that the
representation of credibility score and confidence can be mapped to diferent labeling schemes:
FakeNewsNet only has labels “fake” or “real”, while Clef’18 has labels “true”, “half-true” and
“false” (a third dataset presented in [
2https://www.newsguardtech.com/, https://www.mywot.com/. See https://misinfo.me/misinfo/credibility/ origins for a full list of such sources.
3https://github.com/rdenaux/acred (a) Tweet with label and feedback buttons (b) Credibility Review with explanation
The output of acred is Credibility Review which can be simplified as a single label (see fig. 2a). However, the provenance and authorship relations can be exploited to render a full (or partial) “evidence graph” (see fig. 3) including intermediate steps (facilitating verifiability and reproducibility) or to generate explanations (see fig. 2b).
In this paper, we summarized recent work on a conceptual model and vocabulary for
describing and sharing retrieved documents, their credibility and its implication for the credibility of
potentially misinforming documents online. We believe that the IR community working on
retrieval of content for reducing online misinformation should be aware of this work and can
benefit from adopting this conceptual model. As stated in section 2, these IR systems need to
deal with a variety of requirements and such conceptual models and vocabularies are a way to
ensure these requirements are met. The proposed conceptual model makes it easier to
propagate and document results and steps in IR systems, thus facilitating explainability and
reproducibility. It also makes explicit where the boundaries are between the subjective credibility
and similarity analyses are and the trust in ground credibility sources like fact-checkers and
organizations like NewsGuard. The fact that the sample implementation relies on third-party
services like ClaimsKG, MisinfoMe, various fact-checkers and site reviewing organizations
further shows that the conceptual model facilitates decentralization and handling of subjective
reviews. Note finally that acred only uses a small set of “ground credibility signals”
(ClaimReviews and website reviews), there are several such sources of signals as identified by the W3C
Credibilty Signals working group.4. Some of the open issues with acred are discussed in [
Although the conceptual model in [
Work supported by the European Comission under grant 770302 – Co-Inform – as part of the Horizon 2020 research and innovation programme. 4https://credweb.org/signals-beta/ 5And more recently the Open Claims model (under review) [4] M. Mensio, H. Alani, MisinfoMe: Who’s Interacting with Misinformation?, in: 18th
International Semantic Web Conference: Posters & Demonstrations, 2019. [5] M. Mensio, H. Alani, News Source Credibility in the Eyes of Diferent Assessors, in:
Conference for Truth and Trust Online, In Press, 2019. [6] D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, L. Specia, SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation, in: Proc. of the 10th International Workshop on Semantic Evaluation, 2018, pp. 1–14. arXiv:1708.00055. [7] N. Reimers, I. Gurevych, Sentence-bert: Sentence embeddings using siamese bertnetworks, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 3973–3983. [8] J. Johnson, M. Douze, H. Jégou, Billion-scale similarity search with gpus, arXiv preprint arXiv:1702.08734 (2017). [9] K. Meng, D. Jimenez, F. Arslan, J. D. Devasier, D. Obembe, C. Li, Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims (2020). arXiv:2002.07725. [10] P. Nakov, A. Barrón-Cedeño, R. Suwaileh, L. M. . Arquez, W. Zaghouani, P. Atanasova, S. Kyuchukov, G. Da, S. Martino, Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, in: International Conference of the Cross-Language Evaluation Forum for European Languages, 2018, pp. 372—-387. [11] D. Wang, J. G. Simonsen, B. Larsen, C. Lioma, The Copenhagen Team Participation in the Factuality Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 Fact Checking Lab., CLEF (Working Notes) 2125 (2018). [12] R. Denaux, F. Merenda, J. Manuel, Towards crowdsourcing tasks for accurate misinformation detection, in: Advances in Semantics and Linked Data: Joint Workshop Proceedings from ISWC 2020, SEMIFORM: Semantics for Online Misinformation Detection, Monitoring, and Prediction, volume 2722, CEUR-WS, 2020. URL: http://ceur-ws.org/Vol-2722/ semiform2020-paper-2.pdf. [13] K. Boland, P. Fafalios, A. Tchechmedjiev, Modeling and Contextualizing Claims, in: 2nd International Workshop on Contextualised Knowledge Graphs, 2019.