Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020)

Abstract: Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users’ consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters’ followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020 .

## introduction the rampant spread of biased news, partisan stories, false claims and misleading information has raised heightened societal concerns in recent years. many reports pointed out that fabricated stories possibly caused citizens' misperception about political candidates @xcite , manipulated stock prices @xcite and threatened public health @xcite . the proliferation of misinformation has provoked the rise of fact-checking systems worldwide. since 2014, the number of fact-checking outlets has totally increased 400% in 60 countries @xcite . however, fabricated stories and hoaxes are still pervading our cyberspace. fig. 1 shows an example of a fake quote related to barack obama. the quote had been debunked by snopes @xcite on september 08, 2016 but two months later, it appeared again inside an original tweet posted by a twitter user (called an original poster) and was retweeted over 28 thousand times. perhaps, the original poster and people who shared the original tweet did not know if it was fact-checked or they might share it simply because it was suitable for their personal preferences or ideologies @xcite . in other words, existing fact-checking systems mainly focus on detection but neglect online users who play the critical role in spreading fake news. after detecting fake news, what are the next steps to discourage people from sharing it? recent studies @xcite tried to curb the above weakness. however, these approaches are not proactive since they rely on fact-checkers who may be unreliable. recent works showed that when seeing factchecked information, users' likelihood to delete fake news's shares went up 400% @xcite and 95% of the time users did not further consume or go through fake news @xcite . observing the downside of existing methods and impacts of broadcasting verified news, our goal is to search for fact-checking articles (fc-articles) which address the content of original tweets (i.e. confirming, supporting, debunking or refuting). we show a mock-up of how a relevant fc-article is linked/displayed given an original tweet in fig. 1 . by searching for fc-articles and incorporating factchecked information into social media posts, we can warn users (e.g. followers of original posters) about fake news to which they are exposed. the search also proactively scales up volume of verified content on social media. however, achieving the goal is challenging since we need to solve two problems: (p1) what information in original tweets should we use to find correct fc-articles? and (p2) how can we design a framework to retrieve and rank fc-articles? with the first problem (p1), we can use original tweets' text to find fc-articles. however, this approach is suboptimal since fake news can appear in many forms (e.g. text, images, videos) @xcite as shown in fig. 1 . thus, we propose to use both text and images of original tweets to search for fc-articles. regarding the second problem (p2), we propose a framework consisting of two key steps: (1) using a basic retrieval (i.e. bm25) to find initial lists of candidate fc-articles and then (2) re-ranking the initial lists by using advanced models for ranking refinement. in the first step, since original tweets' text may be insufficient to find correct articles as shown in fig. 1 where there is no meaningful information in the text but in the image, we propose to expand original tweets' text by using text inside original tweets' images. for the second step, we propose an attention mechanism to focus on key textual matching signals and jointly integrate them with visual information to boost ranking quality. by tackling these issues, our contributions are as follows: our work is closely related to evidence-aware fact-checking. thorne et al. (2018); nie et al. (2019) built a pipeline to find documents and sentences to fact-check mutated claims generated from wikipedia pages, @xcite aimed to find webpages related to given fc-articles and predict their stances on claims in the fc-articles. @xcite only focused on fact-checking and @xcite detected previously factchecked claims. our paper deviates from these work since we aim to find fc-articles given multimodal fake news in social media posts. as our goal is to increase users' awareness of verified news, studies about fact-checkers @xcite @xcite are close to ours. neural ranking models for text search. neural ranking models for text search mainly fall into two groups: semantic matching and relevance matching models. the former one seeks to learn representations of a query and a document, and measure their similarity @xcite @xcite @xcite , while the later one @xcite @xcite @xcite @xcite aims to capture relevant matching signals between a query and a document based on word interactions. there are methods unifying two categories such as @xcite ; @xcite . our model can be viewed as a relevance matching method in which a novel attention mechanism is designed to focus on crucial word interactions. neural models for multimodal retrieval. multimodal data (e.g. text and images) are used in cross-modal retrieval @xcite @xcite , visual q&a task @xcite , product search @xcite and so on. our work is the first using multimodal data in social media posts to search for verified information. ## our framework given an original tweet q and a fc-article d, where every original tweet q contains text and images and the article d contains text and/or images, we aim to derive function f (q, d) which determines their relevancy foot_0 . we use f (q, d) to rank all fc-articles. following @xcite , we adopt the re-ranking methodology as follows: (1) quickly retrieving candidate fc-articles/documents foot_1 for each original tweet/query foot_2 by a basic retrieval and (2) reranking the candidates by our man (multimodal attention network) as shown in fig. 2 . we describe our input representations, basic retrieval and man in following subsections. ## data analysis topics of original tweets/queries. since the topic of an original tweet is related to the topic of a corresponding fc-article, we extracted topics of relevant fc-articles to understand the topical distribution of tweets. by analyzing each fc-article, top 5 topics of tweets in snopes are as follows: politics (42.3%), fauxtography (22.7%), junk news (8.1%), uncategorized (6.8%), quotes (4.8%). for politifact, tweets' topics are mostly about politics due to its political mission. in conclusion, our datasets captured various topics. similarity of text in tweets and text in images. as we utilize text in images to enhance ranking performance, we seek to understand how similar text in tweets and text in images. for each query/tweet having text in its images, we transformed its text in tweet and its text in images into two vectors of tf-idf values, and computed their cosine similarity. from all queries of a dataset, we computed mean cosine similarity. the mean similarity is 0.083 and 0.102 for snopes and politifact respectively, indicating that text in tweets is less similar to text in images. the number of tweets/queries containing text in images is 8,494 (76%) and 1,742 (86%) for snopes and politifact respectively. ## experiments 6.1 neural ranking baselines we compare with 9 state-of-the-art neural ranking baselines, divided into 3 groups as follows: (1) multimodal retrieval methods including dvsh @xcite and transearch @xcite , (2) semantic matching models including esim @xcite and nsmn @xcite , and (3) relevance matching methods including match-pyramid @xcite , knrm @xcite , convknrm @xcite , copacrr @xcite and @xcite . please see the appendix for details of the baselines. ## discussion since snopes and politifact are the most popular fact-checking sites, building two models for them is an acceptable cost. when facing a real-life social media post, we run two trained models sequentially. if there is no found fc-article, we can inform users that the post is unverified and suggest related pages from verified sites (e.g. governments' sites). when tweets do not have any images, we can use ctm which may find less relevant articles compared with man. however, ctm still performed better than the baselines as shown in tables 2 and 3 . we also built our best model (man-a) on the full dataset but observed some reduction in ndcg@1 and ndcg@3, but not hit@3 compared with results of sc2 on separate datasets maybe because of the false negatives described in section 4. however, our model still outperformed the baselines. there are a few things our work could be improved. first, our basic retrieval bm25-ti does not consider images' similarities. to improve bm25-ti, we may combine images' similarities and bm25's score. we leave it as future work. second, we create train/test data based on unique original tweets. though there are no retweets and quotes, it is hard to completely ensure all queries's content are unique. however, our settings are applied to all models for fair comparisons. in addition, as shown in fig. 1 , online users tend to re-post fake news. therefore, it may be reasonable to have similar original tweets' content. third, we tried to fine tune bert but did not achieve good results perhaps because we did not have enough data. interestingly, prior work @xcite also had a similar observation when fine-tuning bert. ## conclusions in this paper, we propose a novel method to alleviate the spread of fake news. by searching for fc-articles and incorporating fact-checked information into social media posts, we can warn users about fake news and discourage them from spreading misinformation. our framework uses text and images to search for fc-articles, achieving an average increase of 4.8% over best baselines with the maximum improvement of 11.2%. complementary to fake news detection methods, our method proactively scales up verified content on social media. our framework can be used for other multimodal retrieval tasks (e.g. searching for verified sites as we suggested in the previous section).