Automated Evidence Collection for Fake News Detection (2021)

Abstract: Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society, especially when dealing with an epidemic like COVID-19. The task of Fake News Detection aims to tackle the effects of such misinformation by classifying news items as fake or real. In this paper, we propose a novel approach that improves over the current automatic fake news detection approaches by automatically gathering evidence for each claim. Our approach extracts supporting evidence from the web articles and then selects appropriate text to be treated as evidence sets. We use a pre-trained summarizer on these evidence sets and then use the extracted summary as supporting evidence to aid the classification task. Our experiments, using both machine learning and deep learning-based methods, help perform an extensive evaluation of our approach. The results show that our approach outperforms the state-of-the-art methods in fake news detection to achieve an F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared Task. We also release the augmented dataset, our code and models for any further research.

## introduction the ability to consume readily available information from the internet is alarming for both individuals and organizations. the quality of content on social media platforms has been significantly affected due to the spread of fake news, misinformation and unverifiable facts. the current tally of internet users stands at 4.66 billion foot_1 @xcite ; and many of these users generate, post and consume content without any regulation, in a large number of countries 3 . due to the unrestricted nature of online platforms, there is a significant increase in the amount of misinformation on social media @xcite , especially in developing nations @xcite . studies show that events such as the presidential election of the united states in 2016 were affected due to moderated fake news campaigns @xcite . @xcite propose that fake news is intentionally written, verifiably false, and is created in a way that makes it look authentic. manual efforts by other online platforms such as poynter 4 , factcheck 5 , altnews 6 etc. to detect fake news, requires a lot of human effort and can prove to be cumbersome. such manual efforts can be time-consuming, challenging, and at times, can also be ineffective as fake news can spread faster than verified claims over social media platforms. automatic fake news detection is a task that aims to mitigate the problem of misinformation with the help of evidence supported by various sources. most of the approaches in this recently devised task aim to use the classical machine learning-based methods or the recent deep learning-based methods to help classify news items as fake or as real. initially proposed methods for the task applied machine learning-based techniques but cited insufficient data as a major concern @xcite . recent deep learning and ensemble approaches @xcite were proposed on the fever @xcite and liar (wang, 2017) datasets, and have been shown to perform very well. studies have proposed a combination of evidence detection with textual entailment 3 internet censorship in countries 4 poynter: online 5 factcheck: online 6 altnews: online concerning the claim @xcite . fever shared tasks @xcite have helped the automatic fact verification task gather attention towards the problem and helped generate approaches to mitigate the issues with previously proposed solutions. this study shows that our novel approach improvises over state-of-the-art approaches and helps detect fake news related to covid-19. our approach performs web-search for evidence collection and uses bert-score similarity to match the unverified claim with the top-k searches. further, we propose the use of summarization to mitigate problems with the evidence collection. we summarize the top-n selected lines from these articles and use them as evidence to support or reject the news item claim. our experiments perform an extensive evaluation of the approach over the datasets released as a part of the constraint-2021 shared task @xcite shared task. our summarized contributions with this paper are: ## related work automatic detection and classification of fake news, especially in epidemic situations like covid-19, is a significant issue for society. most of the recent works have identified that fake news is written intentionally and factually false @xcite . several datasets have been released for the ai community in the field of fake news detection, such as liar (wang, 2017), fake news challenge-1, and fever @xcite . some recent techniques extract the evidence from wikipedia to classify a claim as supported, refuted or notenoughinfo @xcite . they formulate the problem as a three-step process (i) first the top-k documents are identified based on the tf-idf based approaches (ii) then top-k sentences are identified from the documents, and (iii) finally the textual entailment based approaches @xcite are used to classify the claim. team papelo @xcite used the transformer-based approach for the textual entailment and selected the evidence-based on tf-idf and entities present in the title. @xcite selects the documents and sentence using the entity mentions and recognizes textual entailment using enhanced sequential inference model (esim) @xcite . despite the several attempts, fake news detection is a challenging problem and countering fake news is a typical issue that requires continuous studies. recently, some researchers released the datasets related to covid-19 fake news detection. shahi and nandini (2020) (shahi and nandini, 2020) proposed the first multi-lingual cross-domain dataset for covid-19 that consists of 5182 factchecked news articles from jan-2020 to may-2020. they collected data from 92 different websites and manually classified them into 23 classes. kar et al. @xcite ) also released a multi-indiclingual dataset besides english to detect the fake news in social media tweets. they obtained 480 tweets in bengali and 460 tweets in hindi. in addition to tweets, they also included several features related to tweets such as retweet count, favourite count, total url in description, url, friend list, followers, etc. recently, a very relevant dataset was released by patwa et al. @xcite ) which consists of 10,700 tweets or claim collected from various sources such as twitter, politifact, snopes, boomlive. they experimented with various machine learning techniques like decision trees, logistic regression, svm, gradient boosting dt and achieved the f1-score of 93.32. most of the previous work on covid-19 dataset proposed an ensemble approach of various models such as bert, roberta, xlnet, etc. @xcite @xcite . chen et al. @xcite trained the model with additional words such as covid-19, coronavirus, pandemic, indiafightscorona since the bert tokenizer will split these words into separate tokens. some works leveraged the fine-tuned models like covid-twitter-bert (ct-bert) @xcite and demonstrated a boost in performance @xcite @xcite . the fake news detection methods described above mainly uses the claim for the classification. our method focuses on extracting and summarizing the evidence from the external source and uses it to classify the claim on the covid-19 fake news detection dataset @xcite . ## dataset for our work, we use the pre-released covid-19 fake news dataset as a part of the constraint-2021 shared task @xcite . this goldstandard manually annotated dataset comprises social media posts and articles which are related to covid-19. each post or tweet contains content in the english language and is classified in either of the two categories-(1) real: where tweets or articles which are factually correct and verified from authentic sources, for example, "wearing mask can protect you from the virus. (twitter)"; or (2) fake: where tweets or posts related to covid-19 which are factually incorrect and verified as false, for example, "if you take crocin thrice a day you are safe. (facebook)". the authors collect fake news from two different sources-social media platforms and public factchecking platforms. the social media posts include text from facebook posts, instagram posts, and twitter posts, whereas the fact-checking websites such as politifact, snopes, and boomlive are used to collect fact-checked news items. to further collect real news, they sample tweets from official government channels, news channels, and medical institutes. overall, a total of 14 such sources were used to prepare this dataset. the dataset comprises 10700 manually annotated samples and is split into (60%) train, (20%) validation and (20%) test sets. we provide the exact numbers for each split/class in ## our approach in this section, we provide details of our novel approach to augment the dataset with evidence from web search and the use of this evidence to complement the task of fact verification. the algorithm for our approach can be seen in algorithm 1. as discussed above, we collect this evidence and prune to top-k related news items based on semantic similarity via bertscore @xcite . we also select top-n lines from each article for further building an evidence repository, as detailed below in further subsections. ## experiment setup in this section, we discuss the experiment setup in detail. we perform the task of fake news detection as a binary classification task in a supervised setting. we choose to perform our experiments with both conventional machine learningand deep learning-based classifiers. from the machine learning-based approaches, we choose logistic regression (lr) and support vector machines (svm) with the gridsearch implementation for best results over multiple hyperparameters (values of c, different kernels, etc.) we also utilize lstms with various contextual language models from the deep learning methods. from the deep learning-based approaches, we use a simple lstm implementation with pre-trained glove 10 vectors, bert base , roberta base , and xlnet base -based classifiers. our lstm implementation uses adam optimizer with a learning rate of 0.001, and 256 as the batch size. for classifiers based on bert base , roberta base , and xlnet base , we use the hug-gingface implementations with a batch size of 32, l2 regularization and cross-entropy loss. the regularization parameter λ was set to 0.1. each classification method is iterated (1) without evidence (-), (2) with augmented summarized evidences from s1, (3) and then with s2, thus giving us three sets of results for each method; as shown in as an input to the classifier, we use the claim asis from the dataset as described above. we have a dataset @xmath0, y n ) n @xmath1. here x @xmath2, e n ), where c n represents the claim, and e n represents evidence gathered using our approach. @xmath3, and @xmath4, 1} are the corresponding labels. thus, given a claim c and evidence e, the aim of this task is to train a classifier such that the claim c is predicted as fake news or not, i.e f θ : @xmath5, 1}. f (c, e; θ) = 1, if c is the f ake news 0, otherwise however, this is not the only key takeaway from these results. we observe that by using our novel approach, a consistent improvement is seen in the task results. the efficacy of our approach can be seen from we acknowledge that the constraint dataset is saturated in terms of possible improvements. however, with this paper, our aim is to show the efficacy of our summarization technique which can help the evidence detection for news. we chose this dataset at an early stage of our work, and our experiments do show that improvements can, in fact, still be shown on this dataset. our best-performing system surpasses the state-of-the-art by 0.23% points. ## conclusion and future work in this paper, we present an automated method to collect evidence for the fake news detection task. we use our novel approach to augment the dataset, released in the constraint-2021 shared task, with evidence sets collected from the web. our method helps process these evidence sets, clean them and use them to generate summarized evidence based on two different methodologies. we use either of the summarized evidence as an additional input to the fake news classification task and perform an evaluation of our approach. we discuss the results of the classification task and conclude that our approach helps outperform the previous sota approaches by a small margin, however, helping generate evidence for a crucial dataset. we show that a summarization module can help collect evidence more effectively. we augment this dataset with the summarized evidence and release it along with the code and generated models for further research. we would also like to conclude that our method is generalizable; since it uses pre-trained metrics (bertscore) and models (t5), it can be used to gather evidence for other datasets. the overall pipeline is also not very time-consuming (2 seconds per sample) once fine-tuned models are included in it. we hope our method and the resources are helpful to the nlp community. in future, we would like to use our method to gather evidence for other fact detection/verification datasets as well. our initial aim is to reproduce this study with other datasets and ensure that our method performs well in a real-world scenario. we would also like to apply this method and gather further evidence for existing fake news datasets, and perform our experiments to evaluate this approach over multiple exisiting datasets, including existing multilingual datasets.