A Deep Learning Approach for Automatic Detection of Fake News (2019)

Abstract: Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely Fake News AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current hand-crafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.

## introduction in the emergence of social and news media, data are constantly being created day by day. the data so generated are enormous in amount, and often contains miss-information. hence it is necessary to check it's truthfulness. nowadays people mostly rely on social media and many other online news feeds as their only platforms for news consumption @xcite . a survey from the consumer news and business channel (cnbc) also reveals that more people are rely on social media for news consumption rather than news paper foot_0 . therefore, in order to deliver the genuine news to such consumers, checking the truthfulness of such online news content is of utmost priority to news industries. the task is very difficult for a machine as even human being can not understand news article's veracity (easily) after reading the article. prior works on fake news detection entirely rely on the datasets having satirical news contents sources, namely "the onion" @xcite , fact checking website like @xcite , and snopes @xcite , and on the contents of the websites which track viral news such as buzzfeed @xcite etc. but these sources have severe drawbacks and multiple challenges too. satirical news mimic the real news which are having the mixture of irony and absurdity. most of the works in fake news detection fall in this line and confine in one domain (i.e. politics). the task could be even more challenging and generic if we study this fake news detection problem in multiple domain scenarios. we endeavour to mitigate this particular problem of fake news detection in multiple domains. this task is even more challenging compared to the situation when news is taken only from a particular domain, i.e. uni-domain platform. we make use of the dataset which contained news contents from multiple domains. the problem definition would be as follows: given a news topic along with the corresponding news body document, the task is to classify whether the given news is legitimate/genuine or fake. the work described in pérez-rosas et al. (2018) followed this path. they also offered two novel computational resources, namely fakenews amt and celebrity news. these datasets are having triples of topic, document and label (legit/fake) from multiple domains (like business, education, technology, entertainment and sports etc) including politics. also, they claimed that these datasets focus on the deceptive properties of online articles from different domains. they provided a baseline model. the model is based on support vector machine (svm) that exploits the hand-crafted linguistics features. the svm based model achieved the accuracies of 74% and 76% in the fakenews amt and celebrity news datasets, respectively. we pose this problem as a classification problem. so the proposed predictive models are binary classification systems which aim to classify between fake and the verified content of online news from multiple domains. we solve the problem of multi-domain fake news detection using two variations of deep learning approaches. the first model (denoted as model 1) is a bidirectional gated recurrent unit (bigru) based deep neural network model, whereas the second model (i.e. model 2) is embedding from language model (elmo) based. it is to be noted that the use of deep learning to solve this problem in this particular setting is, in itself, very new. the technique, particularly the word attention mechanism, has not been tried for solving such a problem. existing prior works for this problem mostly employ the methods that make use of handcrafted features. the proposed systems do not depend on hand crafted feature engineering or a sophisticated nlp pipeline, rather it is an end to end deep neural network architecture. both the models outperform the state-of-the-art system. ## related work a sufficient number of works could be found in the literature in fake news detection. nowadays the detection of fake news is a hot area of research and gained much more research interest among the researchers. we could detect fake news at two levels, namely the conceptual level and operational level. @xcite defined that conceptually there are three types of fake news: viz i. serious fabrications ii. hoaxes and iii. satire. the work of @xcite fostered linguistics and fact checking based approaches to distinguish between real and fake news, which could be considered as the work at conceptual level. @xcite described that fact-checking approach is a verification of hypothesis made in a news article to judge the truthfulness of a claim. @xcite introduced a novel dataset for fact-checking and verification where evidence is large wikipedia corpus. few notable works which made use of text as evidence can be found in @xcite . the fake news challenge foot_1 organized a competition to explore, how artificial intelligence technologies could be fostered to combat fake news. almost 50 participants were participated and submitted their systems. @xcite performed retrospective analysis of the three best participating systems of the fake news challenge. the work of @xcite detected fake news through stance detection and also correlated this stance classification problem with textual entailment (te). they tackled this problem using statistical machine learning and deep learning approaches separately and with combination of both of these. this system achieved the state of the art result. another remarkable work in this line is the verification of a human-generated claim given the whole wikipedia as evidence. the dataset, namely (fact extraction and verification (fever)) proposed by @xcite served this purpose. few notable works in this line could be found in @xcite . ## proposed methods we propose two deep learning based models to address the problem of fake information detection in the multi-domain platform. in the following subsections, we will discuss the methods. ## experiments, results and discussion and comparison with state-of-the-art overall we perform four sets of experiments. in the following sub-sections we describe and analyze them one by one after the description of the datasets used. data: prior datasets and focus of research for fake information detection are on political domain. as our research focus is on multiple domains, we foster the dataset released by @xcite . they released two novel datasets, namely fakenews amt and celebrity. the fake news amt is collected via crowdsourcing (amazon mechanical turk (amt)) which covers news of six domains (i.e. technology, education, business, sports, politics, and entertainment). the celebrity dataset is crawled directly from the web of celebrity gossips. it covers celebrity news. the amt manually generated fake version of a news based on the real news. we extract the data domain wise to get the statistics of the dataset. it is observed that each domain contains equal number of instances (i.e. 80). the class distribution among each domain is also evenly distributed. the statistics of these two datasets is shown in the following the news of the fake news amt dataset was obtained from a variety of mainstream news websites predominantly in the united states such as the abcnews, cnn, usatoday, new york times, foxnews, bloomberg, and cnet among others. ## conclusion and future work in this article, we propose two deep learning based approaches to mitigate the problem of fake news detection from multiple domains platform. antecedent works in this line pay attention on satirical news or made use of the content of the factchecking websites, which was restricted to one domain (i.e. politics). to address these limitations, we focus to extend this problem into multi domain scenario. our work extends the concept of fake news detection from uni-domain to multi-domain, thus making it more general and realistic. we evaluate our proposed models on the datasets whose contents are from multiple domains. our two proposed approaches outperform the existing models with a notable margin. experiments also reveal that there is a vital role of a domain in context of fake news detection. we would like to do more deeper analysis of the role of domain for this problem in future. apart from this our future line of research would be as follows: domain topic content entertainment chris pratt responds to body shamers telling him he's too thin big or small chris pratt has heard it all. these days the "guardians of the galaxy" star 37 is taking flak for being too thin but he's not taking it lying down. pratt who has been documenting the healthy snacks he's eating while filming "jurassic world 2" in a series of "what's my snack" instagram videos fired back -in his usual tongue-in-cheek manner -after some followers apparently suggested he looked too thin. "so many people have said i look too thin in my recent episodes of #whatsmysnack he wrote on instagram thursday. some have gone as far as to say i look 'skeletal.' well just because i am a male doesn't mean i'm impervious to your whispers. body shaming hurts." business banks and tech firms battle over something akin to gold: your data the big banks and silicon valley are waging an escalating battle over your personal financial data: your dinner bill last night your monthly mortgage payment the interest rates you pay. technology companies like mint and betterment have been eager to slurp up this data mainly by building services that let people link all their various bank-account and credit-card information. the selling point is to make budgeting and bookkeeping easier. but the data is also being used to offer new kinds of loans and investment products. now banks have decided they aren't letting the data go without a fight. in recent weeks several large banks have been pushing to restrict the sharing of this kind of data with technology companies according to the tech firms. in some cases they are refusing to pass along information like the fees and interest rates they charge. both sides see big money to be made from the reams of highly personal information created by financial transactions.