Self-Supervised Claim Identification for Automated Fact Checking (2020)

Abstract: We propose a novel, attention-based self-supervised approach to identify “claim-worthy” sentences in a fake news article, an important first step in automated fact-checking. We leverage aboutness of headline and content using attention mechanism for this task. The identified claims can be used for downstream task of claim verification for which we are releasing a benchmark dataset of manually selected compelling articles with veracity labels and associated evidence. This work goes beyond stylistic analysis to identifying content that influences reader belief. Experiments with three datasets show the strength of our model.

## introduction the explosion of fake news on social media has resulted in global unrest and has been a major concern for governments and societies worldwide 2 . according to a recent pew research study, americans rate it as a larger problem than racism, climate change, or illegal immigration 3 . since, it's inexpensive to create a website and easily disseminate content on the social media platforms, there is a rising need for automated fake news detection. furthermore, ai solutions are also required to follow good practices, specifically avoiding censorship, violation of fundamental rights such as freedom of expression, and ensuring data privacy @xcite . however, to date, ai models proposed for fake news detection do not 1 data and code available at: https://github.com/architapathak/self-supervisedclaimidentification 2 https://www.reuters.com/article/us-singapore-politicsfakenews-factbox/factbox-fake-news-laws-around-theworld-iduskcn1re0xn 3 https://www.journalism.org/2019/06/05/manyamericans-say-made-up-news-is-a-critical-problem-thatneeds-to-be-fixed/ scale for detecting real-time fake news 4 . much of the research on automated text-based fake news detection can be classified into three broad categories: (1) linguistic approach, which focuses on lexical, stylometric and pattern learning mechanisms @xcite @xcite @xcite ; (2) network-based approach, which leverages features such as the speed and volume of propagation of fake news articles on social media platforms @xcite @xcite @xcite @xcite ; and (3) automated fact-checking approach, which is an effort to assist manual fact-checkers by automating some of their tasks such as detection and verification of claims @xcite . while most work in automated fact-checking has been focused on claim verification task, very few methods have been proposed for detection of claims @xcite @xcite . the approaches in these efforts are majorly related to political discourse. however, our focus is on fake news, which are broader than political discourse since (i) they are deliberately written with a divisive agenda to cause social unrest, (ii) they are not constrained to only politics, and (iii) the headline plays an equally important role in compelling people to read the article. in this paper, we focus on articles where there is a deliberate intent to influence readers through fabricated or manipulated claims in the headline and the content. such articles have a compelling writing style similar to the mainstream media. hence, we build datasets containing these type of compelling articles along with veracity labels and associated evidence supporting the label of each arti-cle. we, then, use these datasets to identify "claimworthy" sentences. in our work, we define "claim" as "statements which are important to the point of the article but one would require to have them verified." our working hypothesis is that in fake news which are created to cause harm, these are the sentences most relevant to the headline. exploiting the hypothesis that the essence of a news article is encapsulated in its headline @xcite @xcite , we propose a self-supervised method to explore the aboutness of the content with the headline of the article to extract the most relevant sentences. @xcite defines aboutness as: an information carrier i will be said to be about information carrier j if the information borne by j holds in i. the idea is taken from information retrieval domain where it is used to signify implications between query and document, specifically to explore the underlying meaning or concept within the document and the query @xcite . in our work, headline is modelled as a query while each of the sentences of the article acts as a document, and we use the concept of aboutness to find the relevant sentences. we show that attention-based mechanisms are able to successfully capture this concept in the news article. contribution: in this work: (i) we introduce a self-supervised representation learning model that eliminates the prerequisite that requires human to annotate data, which is a time consuming and costly task; (ii) the proposed headline-to-sentence attention-based approach for claim identification is novel; previous unsupervised approach for this task use weak supervisory signal which does not capture the context of the article efficiently; and (iii) we propose a benchmark dataset for evidence-based fake news detection. our dataset contains evidence for each of the fake news articles that contributes to the overall degree of veracity of the article. ## related work claim identification/detection: the task of claim identification/detection was first introduced by @xcite who, with the help of human annotators, provided a dataset and a fundamental approach in identifying context dependent claims. in their dataset, which was originally developed by @xcite , each statement indicates whether it should be considered as a context dependent claim (cdc) or not. @xcite reported encouraging results obtained through a supervised learning algorithm using a cascade of classifiers. a rule-based model was introduced by @xcite to bifurcate claim and premise statements in an argumentative discourse environment. however, these methods were generic to only a small set of corpora. furthermore, @xcite also introduced an unsupervised approach to detect claims, which involves a weak supervisory signal "that" for training. however, this approach does not capture the aboutness of the article to understand the context of "claim-worthy" sentences. in 2017, @xcite introduced claim-buster, a platform developed by training a supervised learning model on a large annotated corpus of televised debates in the usa. their model used svm classifier to detect claim-worthy factual claims and produced a score of how important a claim is to factcheck. the 20,000 sentences in the corpus were annotated by human coders to distinguish between claim-worthy factual claims from opinions and boring statements. however, annotating a sentence as an important or unimportant claim is a non-trivial task as this decision changes depending on who's asking, political context and annotator's background @xcite . the model proposed by @xcite only learns the labelled instances and does not explore the contextual information of the written text. a context-aware approach in the political discourse environment was introduced by @xcite who created a rich representation of the sentences from 2016 us presidential debates. their dataset was compiled by taking the outputs of factchecking of the debates from 9 factchecking organizations. their models were created to predict if the claim would be highlighted by at least one or by a specific organization. however, the authors don't have any formal definition of claim in their paper, and their model is specific to certain organizations which led to several false positives. another context-aware approach for claim detection was proposed by @xcite who used sentence embeddings, pre-trained on a large dataset of nli. this work also created a crowd-sourced annotated dataset of sentences from uk political tv shows, annotated across 7 classes. however, their classifiers for the fine-grained clas-sification to detect 7 classes of sentences did not yield good results due to lack of enough annotated data, thus requiring more annotations which is a costly and time consuming task. we build a model that can be trained in a selfsupervised setting to overcome the challenges associated with annotated dataset of claims. we also use attention-based approach to capture aboutness and rich contextual information between headline and all the sentences of the article. the performance on manually created test sets demonstrate promising results in identifying "claim-worthy" sentences even when no sentence-level annotation was used for training. fake news dataset: a variety of fake news datasets have been released in the recent years, most notably buzzfeed foot_1 and stanford @xcite datasets containing list of popular fake news articles from 2016 us presidential elections. however, these datasets only contain webpage urls of the original article and majority of them don't exist anymore. following this, several other datasets were published such as fake news challenge dataset foot_2 which was used for the task of stance detection; getting real about fake news kaggle dataset foot_3 which was created by using bs detector tool; and fakenewscorpus foot_4 which is an open-source large scale collection of fake news articles. however, these articles are labelled as fake based on the domain of the websites they come from. since, the content of these articles are not verified for degree of veracity, using them directly for training may lead to several false positives. this problem was overcome by recently released large dataset, nela-gt-2018 @xcite , which contains articles with ground truth ratings retrieved from 8 different assessment sites. however, the label definitions are not generic and dependent on the external organizations. @xcite also introduced intuitive ground truth labels based on the degree of veracity of the fake news articles, however, the dataset is not publicly available. additionally, they also do not specify the relationship of their labels with the labels used by established fact-checking organizations. furthermore, due to lack of evidence in these datasets, they cannot be used for downstream task of evidence-based verification, which is one of the motivations of this paper. we overcome all these limitations in our datasets described in the following section. ## datasets we introduce two datasets of compelling fake news articles which have writing style similar to mainstream media. the first dataset, dnf-700, where dnf stands for disinformation, contains articles on politics published within 4 months of 2016 us presidential elections from questionable sources (non-mainstream). to compile this dataset, we first extracted fake news articles from working web page urls of stanford dataset @xcite . however, majority of webpage urls in this dataset are expired and we could extract only 26 fake news articles. therefore, we then used "getting real about fake news" kaggle 8 dataset to sample more articles on politics. since, most of the articles in this dataset contain anomalies (eg: incomplete article, social media comments labelled as fake etc.), we manually verified the writing style and discarded obvious fakes -articles with poor grammar and excessive usage of punctuations. however, the degree of veracity of each article in this dataset is not checked and some articles may contain personal opinions. the second dataset, dnf-300, is more sophisticated subset of dnf-700, containing 290 compelling articles on politics and 10 on health/medical news. unlike other fake news datasets in which veracity and evidence for articles are not provided, dnf-300 contains articles associated with veracity labels as well as corresponding evidence. the process of annotating this dataset involves identifying sentences from each article based on their persuasive tone and relevance with the headline. these sentences were then queried on the web and top 10 results were considered to gather evidence from credible sources 9 . based on the evidence found, we label the entire article into four categories: {(0) false; (1) partial truth; (2) opinions stated as fact; (3) true}. these labels are inspired by @xcite ; table-1 shows the description and distribution of these labels while the comparison with two popular factchecking websites is displayed in figure -1 claims here are the sentences manually selected based on their persuasive tone and relevance with the headline. interestingly, some of the articles, which were labelled as fake in other datasets due to the domain of publishing website, turned out to be true news. table-2 partial truth false opinion stated as fact mostly false mixture unproven false half true mostly false false pants on fire snopes dnf-300 politifact this dataset is also a key contribution of this paper as the articles are manually read and verified. additionally, the dataset contains two novel features which are essential for the fake news verification task: (i) generic veracity-based label set, independent of any external organization, and (ii) ground truth evidence corresponding to each label. in addition to these two datasets, we also train our model for claim identification on the dataset introduced for context dependent claim detection (cdcd) by @xcite . although this dataset (cdc) does not contain fake news articles, it has manually annotated sentences based on their relevance to a certain topic. these annotations were utilized for the evaluation of our self-supervised learning model described in the following section. more details on the datasets and examples can be found in appendix. for this, we build several layers in our architecture (see figure -2 ), which are delineated as follows: ## conclusion and future work in this work, we build a novel, self-supervised approach to identify "claim-worthy" sentences -an important task for automated fact checking. the focus of this work is on fake news articles where there is a deliberate intent to influence people or cause social unrest. we have introduced novel datasets of such articles with features essential for the downstream task of fake news verification. using powerful attention models, we explore the notion of aboutness of the headline and the content of the article to identify "claim-worthy" sentences. experiments with three datasets show the strength of our model architecture in overcoming humaninduced biases, which is quite common when using sentence-level claim-annotated datasets. based on the comparison with the baseline, which was implemented using annotated dataset, we show that our models do not require annotated claims for training to identify claim-worthy sentences efficiently. we have also showed that our model is scalable to any dataset with topic and content. future work involves increasing the robustness of the models presented in this paper. we plan to use crowdsourced annotation on the dataset released with this paper to measure the influence of the article on general readers and then use these indicators to fine-tune our models. experimentation with more robust sentence encoders is another avenue of future work. additionally, going forward, we plan to identify a maximum of 3 claims per article which will be used for evidence-based fake news detection. we also plan to expand the dataset, presented in this work, to include fake news articles on topics other than politics and health. headline: clinton received debate questions week before debate the first presidential debate was held and hillary clinton was proclaimed the winner by the media. indeed clinton was able to turn in a strong debate performance, but did she do so fairly? multiple reports and leaked information from inside the clinton camp claim that the clinton campaign was given the entire set of debate questions an entire week before the actual debate. earlier last week an nbc intern was seen hand delivering a package to clintons campaign headquarters, according to sources. the package was not given to secretarial staff, as would normally happen, but the intern was instead ushered into the personal office of clinton campaign manager robert mook. members of the clinton press corps from several media organizations were in attendance at the time, and a reporter from fox news recognized the intern, but said he was initially confused because the nbc intern was dressed like a fed ex employee. the reporter from fox questioned campaign staff about the intern, but campaign staff at first claimed ignorance and then claimed that it was just a fed ex employee who had already left. no reporters present who had seen the intern dressed as a fed ex employee go into mooks office saw him leave by the same front entrance. the fox reporter who recognized the intern also immediately looked outside of the campaign headquarters and noted that there were no fed ex vehicles parked outside. clinton seemed to have scripted responses ready for every question she was asked at the first debate. she had facts and numbers memorized for specific questions that it is very doubtful she would have had without being furnished the questions beforehand. the entire mainstream media has specifically been trying to portray trump as a racist and a poor candidate. by furnishing clinton with the debate questions nbc certainly hoped to make clinton appear much more knowledgeable and competent than trump. and though it is unlikely that anyone will be able to conclusively prove that clinton was given the debate questions, it seems both logical and likely. type: 0 (false) authors:baltimore gazette urls: http://www.freemarketcentral.com/index.php/post/2503/report-clinton-received-debatequestions-a-week-before-debate evidence: [ https://www.snopes.com/fact-check/clinton-received-debate-questions-week-beforedebate/, https://www.truthorfiction.com/hillary-clinton-received-debate-questions-advance/ ] reason: based on snopes rating 'false' which means 'the primary elements of a claim are demonstrably false.' headline: allergens in vaccines are causing life-threatening food allergies it would probably surprise few people to hear that food allergies are increasingly common in u.s. children and around the world . according to one public health website , food allergies in children aged 0-17 in the u.s. increased by 50% from 1997 to 2011. although food allergies are now so widespread as to have become almost normalized, it is important to realize that millions of american children and adults suffer from severe rapid-onset allergic reactions that can be life-threatening. foods represent the most common cause of anaphylaxis among children and adolescents. the united kingdom has witnessed a 700% increase in hospital admissions for anaphylaxis and a 500% increase in admissions for food allergy since 1990. the question that few are asking is why life-threatening food allergies have become so alarmingly pervasive. a 2015 open access case report by vinu arumugham in the journal of developing drugs , entitled " evidence that food proteins in vaccines cause the development of food allergies and its implications for vaccine policy ," persuasively argues that allergens in vaccinesand specifically food proteinsmay be the elephant in the room. as arumugham points out, scientists have known for over 100 years that injecting proteins into humans or animals causes immune system sensitization to those proteins. and, since the 1940s, researchers have confirmed that food proteins in vaccines can induce allergy in vaccine recipients. arumugham is not the first to bring the vaccine-allergy link to the publics attention. heather fraser makes a powerful case for the role of vaccines in precipitating peanut allergies in her 2011 book, the peanut allergy epidemic: whats causing it and how to stop it. type: 1 (partial truth) authors:admin -orissa urls: galacticconnection.com evidence: https://www.ncbi.nlm.nih.gov/pmc/articles/pmc3890451/ reason: the key claim is written in such a way so that it misleads people in thinking all the food related allergies in us are caused by vaccines. found evidence which says these type of allergies are rare. headline: george soros: trump will win popular vote by a landslide but clinton victory a 'done deal' in recent weeks, democrats have attempted to paint republican presidential nominee donald j. trump as a lunatic for claiming that the election is going to be rigged in favor of his democratic rival, hillary clinton. even republican politicians and former politicians are telling trump to knock off such talk. but, as usual, trump's shrewdness and defiance of standard political decorum in which the "opposition" party merely rolls over and surrenders in the face of democratic pressure is winning the day. none other than billionaire investor and longtime democratic supporter george soros has said that the fix is literally in for the election, in favor of clinton no matter how much of the popular vote, and from which battleground states, trump captures. as reported by top right news and other outlets, during a recent interview with bloomberg news, soros a democrat mega-donor openly admitted that trump will win the popular vote in a "landslide." however, he said that none of that would matter, because a president hillary clinton is already a "done deal." in the interview, which is now going viral, soros says with certainty that trump will take the popular vote, despite what the polls say now (which are completely rigged to oversample democrats), but not the electoral college, which will go to clinton. when the reporter asks if that is already a "done deal" that clinton will be our next president no matter what soros says "yes," and nods his head. is soros just making a prediction out of overconfidence? or does he truly know something most of us don't know? type: 2 (opinion stated as fact) authors:j. d. heyes urls: https://www.naturalnews.com/055789 george soros hillary clinton electoral college.html evidence: 1. https://www.snopes.com/fact-check/george-soros-trump-will-win-popular-vote-bya-landslide-but-clinton-victory-a-done-deal/, 2. https://www.bloomberg.com/news/videos/2016-01-22/soros-clinton-to-win-popular-vote-inlandslide reason: the key claim is false (based on snopes rating), however, the article also contains opinions stated as fact. headline: donald trump: minnesota has suffered enough accepting refugees in a pitch to suspend the nations syrian refugee program , donald trump said minnesotans have "suffered enough" from accepting somali immigrants into their state. "here in minnesota you have seen first hand the problems caused with faulty refugee vetting, with large numbers of somali refugees coming into your state, without your knowledge, without your support or approval," trump said at a minneapolis rally sunday afternoon. he said his administration would suspend the syrian refugee program and not resettle refugees anywhere in the united states without support from the communities, while hillary clintons "plan will import generations of terrorism, extremism and radicalism into your schools and throughout your communities." type: 3 (true) authors:henry wolff urls: amren.com evidence: 1. https://time.com/4560078/donald-trump-minnesota-somali-refugees/ , 2. https://www.buzzfeednews.com/article/claudiakoerner/trump-vs-somali-refugees reason: evidence found to support key claims.