“Who said it, and Why?” Provenance for Natural Language Claims (2020)

Abstract: In an era where generating content and publishing it is so easy, we are bombarded with information and are exposed to all kinds of claims, some of which do not always rank high on the truth scale. This paper suggests that the key to a longer-term, holistic, and systematic approach to navigating this information pollution is capturing the provenance of claims. To do that, we develop a formal definition of provenance graph for a given natural language claim, aiming to understand where the claim may come from and how it has evolved. To construct the graph, we model provenance inference, formulated mainly as an information extraction task and addressed via a textual entailment model. We evaluate our approach using two benchmark datasets, showing initial success in capturing the notion of provenance and its effectiveness on the application of claim verification.

## introduction never before have humans been able to generate and disseminate content so easily, leading to a contamination of information supply with irrelevant, redundant, unsolicited, and often low-value information @xcite . while significant attention has been devoted recently to identifying false claims, the age of "information pollution" we live in calls for the development of additional important insights. at the heart of these insights is the need to determine the provenance of claims -who first made a given claim, and how an original claim developed and changed over time (and potentially across contributors). consider the following claim: "facebook soon plans to charge fees to users of the social network." 1 as shown in figure 1 , a typical modern from november 1st you'll be paying $2.99 every month to access the site claim verification pipeline starts with searching for existing evidence for the given query claim, and then leverages textual entailment models to determine the veracity of the claim relative to the evidence @xcite . however, sites such as snopes.com and other fact-checking websites will not only provide their conclusion about the veracity of the claim relative to the evidence, but would also seek additional information that explains why people may think it fake. for example, snopes details how the claim originated from nationalreport.net. the original version of the claim is related to the query claim, as well as other relevant claims, but carries a different meaning. it says: "facebook could cease to exist, if they don't do something about their rising costs". subsequently, the inaccurate claim, triggered by the original one, has been repeated by other websites and retweeted on social media, as shown in figure 2 , possibly increasing the level of credibility some readers assign to it. the origins and causal derivations of data, as described above, are explicitly modeled in the context of databases @xcite and scientific workflow systems @xcite , where they are termed "data provenance." we argue that modeling and understanding the provenance of a claim made in natural language is also very important since, beyond attribution, it helps people understand the background and the context in which a claim was generated, how different aspects of the claim are combined, and how a claim has been changed over time by different agents. at the same time, provenance provides us with an explanation for why people think a claim is real or fake, by looking at its history. even if all one wants is to determine a stance relative to a claim, this may involve considering more than just its current incarnation, but rather its evolution over time and all of the sources that contributed to this evolution. similarly, one may want to consider who influenced a claim, or who influences a specific author of multiple claims, and this can be accomplished by considering the origin and evolution of these claims. figure 2 shows that our notion of provenance can not only provide us with evidence but also with the structure of-and relationships among supporting evidence and claims. in this paper, we propose and develop a computational framework for claim provenance graphs, which provide information and supporting evidence about where a claim is believed to have originated and how it has been disseminated. our challenge is to infer and reconstruct this graph using available evidence. a claim provenance graph consists of two components: like provenance graphs in other fields (including the w3c prov specification @xcite ), a claim provenance graph tracks the data, operations, and parties responsible for a claim. unlike most prior provenance graphs, claim provenance is often inferred, uncertain, and comprised of approximate relationships (e.g., "textually entailed"), as indicated in figure 2 . however, inferring the provenance graph of a claim is a difficult task. in our current implementation of this notion, given a natural language claim in a document, we search for the claim on the web, restricting our focus to content published prior to the document (eliminating many sources that could not have influenced the document). a match to the claim search may itself make a statement about the claim, or it may in turn report a statement relevant to the claim made by other sources. if a source mentioned in the article is describing the claim, one of the sub-tasks is to identify the correct source(s). therefore, we view obtaining the nodes of the provenance graph as an information extraction (ie) problem. however, in contrast to a typical ie approach that uses annotated data @xcite , wikipedia or other large scale knowledge bases @xcite , identifying sources of a statement in an article is an ie task which is very hard to annotate. the reason is that both the statement and its sources can be described implicitly in the given text, and this may require additional reasoning or coreference resolution. in this work, we tackle this ie problem as a textual entailment (te) problem, and propose a solution that leverages off-the-shelf semantic role labeling tools to generate candidates for source identification. following that, we wikifiy extracted source mentions, which further allows us to link nodes in the provenance graph and label them. as an application, we propose models that can use the provenance graph to improve the estimation of claims' veracity. the key contributions of this paper are (1) it is the first work to study and formally define the notion of a provenance graph for a natural language claim; (2) it proposes a te model to automatically extract provenance information, regardless of whether the relevant statement and the source are described explicitly or implicitly in the text; this is then used to construct a graph and label its edges; (3) it develops techniques that exploit the provenance graph to improve claim verifica-tion. we provide initial experimental support for our novel formulation by studying the effectiveness of extracting sources and the benefit of leveraging provenance graph when doing claim verification. it is important to note that we have not solved the claim provenance problem. we introduce it and explain its importance, provide an initial formulation and an implementation. we argue that, already at this point, our initial formulation and the results it supports provide a significant contribution. we point to a range of future work directions that we discuss at the end of this paper. ## provenance of claims given a target claim and a large corpus, we want to infer the provenance graph of the claim from the given corpus. this graph will represent previouslymade statements with their sources, which, with high probability, ultimately led to the target claim. in this section, we first define the claim provenance graph, and present the problems one must solve to infer it. note that to distinguish between the query claim and the claims in its previous versions, we use statements to refer those previously-made claims by other sources. ## inferring the provenance graph to infer the provenance graph for the given claim, we need to solve the three problems outlined in section 2. here, we propose a pipelined solution, and elaborate them one by one. ## application: claim verification we take claim verification as an example to demonstrate the importance of claim provenance graph. concretely, we elaborate how we can use the graph to improve the estimation of claim veracity. ## experimental evaluation we evaluate (1) the solutions to infer the provenance graph, and (2) the effectiveness of the claim evidence graph on claim verification, which is adapted from the inferred provenance graph. for each goal, we first elaborate the experimental set-tings, and then describe the results and analysis foot_1 . ## related work to the best of our knowledge, our work is the first to formally define and propose a framework to infer the provenance graph of given claims made in natural language. one line of the related work includes identifying sources of opinions in opinion analysis @xcite and quote attribution @xcite , which is related to one of the components we use to infer the provenance graph. earlier work performs information extraction via sequential tagging in a given text and collects paired sources and opinions or quotes and speakers. we do not detect all quotes or opinions stated in the text, but rather detect the sources generating statements related to the given claim, whether it is described implicitly or explicitly in the text. furthermore, we also construct a graph that depicts the history of how a claim has disseminated over time, a task that was not addressed in earlier work. another line of related work includes factchecking @xcite @xcite and claim verification @xcite . however, those works focus only on capturing discriminative linguistic features of misinformation, while we argue that determining the provenance of claims is essential for addressing the root of the problem, understanding claims and sources. ## conclusion and future work we introduce a formal definition and a computational framework for the provenance of a natural language claim given a corpus. we argue that this notion of provenance is essential if we are to understand how claims evolve over time, and what sources contributed to earlier versions of the claims. we provide initial results exhibiting that our framework can be used successfully to infer the provenance graph and, that it can be applied to boost the performance of claim verification. the framework introduces a range of important questions both from the inference and the application perspectives. for example, inferring the current version of the provenance graph depends on the ability to identify authors. this could be difficult when the authors are not mentioned in the text, which might require a deeper understanding of sources' writing style and positions. from the application perspective, it is clear that the graph contains more information than we have exploited so far. for example, the edge labels, indicating the evolution operators of a claim should also be useful. in particular, this will support a more informed study of influence of specific sources and of trustworthiness, and possibly other aspects of information spread.