[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121734-en":3,"doc-seo-121734-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121734,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning Technique Based Fake News Detection","False news attracts both public attention and scholarly research because misleading information can distort public perception and enable harmful groups to influence major events such as elections. People can share fake claims for personal gain or to create trouble, while misinformation impact varies across regions. This paper trains a classification model using the 1876-news dataset, applying NLP preprocessing to clean and filter texts. Three Machine Learning and two Deep Learning algorithms are evaluated, and the best Naive Bayes results reach 56% accuracy with an F1-macro around 32%.","Machine Learning Technique Based Fake News Detection  \nBiplob Kumar Sutradhar 1, a) , Md. Zonaid 1, b) , Nushrat Jahan Ria 1, c) and Sheak  \nRashed Haider Noori 1, d)  \nAuthor Affiliations  \n1 Daffodil International University, Dhaka, Bangladesh  \nAuthor Emails  \na) [biplob15-3923@diu.edu.bd](biplob15-3923@diu.edu.bd)  \nb) [zonaid15-3927@diu.edu.bd](zonaid15-3927@diu.edu.bd)  \nc) [nushratria.cse@diu.edu.bd](nushratria.cse@diu.edu.bd)  \nd) [drnoori@daffodilvarsity.edu.bd](drnoori@daffodilvarsity.edu.bd)  \nAbstract: False news has received attention from both the general public and the scholarly world. Such false information has the ability to affect public perception, giving nefarious groups the chance to influence the results of public events like elections. Anyone can share fake news or facts about anyone or anything for their personal gain or to cause someone trouble. Also, information varies depending on the part of the world it is shared on. Thus, in this paper, we have trained a model to classify fake and true news by utilizing the 1876 news data from our collected dataset. We have preprocessed the data to get clean and filtered texts by following the Natural Language Processing approaches. Our research conducts 3 popular Machine Learning (Stochastic gradient descent, Naïve Bayes, Logistic Regression,) and 2 Deep Learning (LongShort Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms. After we have found our best Naive Bayes classifier with 56% accuracy and an F1-macro score of an average of 32% .  \nINTRODUCTION  \nThe consumption of news among people is increasing because of low cost and easily accessible technology. Now it’s becoming easier to share news instantly via social media as people spend most of their time on social media. Thus, fake news also takes this advantage of technology and can have large scale negative effects on political (USA election, 2015), social and economic levels [1] . Sharing news without checking the quality of content is also making news quality questionable and news losing its ground universally. Spammers use social media to share their clickbait and influence traffic to fake news [2] . Social media platforms are very powerful in creating spreading satire or absurdity, biased opinions and manipulating mindsets [3] . Some traditional computational techniques also can be found and used to target specific text as hoax on basis of the textual content. However, the majority of these just check these websites are “PolitiFact” and “Snopes”. Humans maintain these website repositories and these consist of a list of websites classified as ambiguous and fake [4] .  \nNevertheless, this list does not classify all the categories of news; it is specifically for political news. Humans’ability to detect fake news content is 54%[5]. Every day the number of news content all over the world is increasing rapidly, and it’s becoming impossible for humans to classify all the news that get published. On the other hand, the process is also very time consuming.  \nTo detect fake news from all categories NLP can play a major role. So, in this research we used NLP with feature classification to detect fake news. ML and DL algorithms both are tested by us. In the later section, we presented related work and figuring out the previous works’ gap, and we have presented our own approaches to increase outcome of fake news detection.  \nLITERATURE REVIEW  \nFake news detection is getting more and more attention nowadays by scholars from different backgrounds. To solve this problem authors used Single Modal and Multi-modal detection approaches.  \nIn fake news detection tasks, the main challenge for researchers is how to distinguish news according to features, how data should be classified, and which classification will give better output. A work based on A linguistic model to search language driven features for classification done before. For a news article, this model can find grammatical, syntax-based features, word d","cbCaiunWq9QQMeYx","https://ap.wps.com/l/cbCaiunWq9QQMeYx","pdf",456419,1,9,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Why is fake news detection important in this paper?\",\"answer\":\"Fake news can strongly influence public perception and help groups manipulate outcomes of events like elections. The paper also notes the difficulty for humans to classify all news content quickly and accurately.\"},{\"question\":\"What dataset and preprocessing steps are used for classification?\",\"answer\":\"The study trains on 1876 news items from a collected dataset. Texts are preprocessed using Natural Language Processing approaches to clean and filter the content before training.\"},{\"question\":\"Which algorithms does the paper evaluate and which performs best?\",\"answer\":\"The research evaluates three Machine Learning algorithms (Stochastic gradient descent, Naïve Bayes, Logistic Regression) and two Deep Learning models (LSTM variants such as AWD-LSTM/ASGD Weight-Dropped LSTM). The best result comes from the Naive Bayes classifier with 56% accuracy and an F1-macro around 32%.\"}]","Machine Learning Technique Based Fake News Detection | PDF",1785806550,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-technique-based-fake-news-detection","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-technique-based-fake-news-detection/121734/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is fake news detection important in this paper?","Question",{"text":76,"@type":77},"Fake news can strongly influence public perception and help groups manipulate outcomes of events like elections. The paper also notes the difficulty for humans to classify all news content quickly and accurately.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and preprocessing steps are used for classification?",{"text":81,"@type":77},"The study trains on 1876 news items from a collected dataset. Texts are preprocessed using Natural Language Processing approaches to clean and filter the content before training.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithms does the paper evaluate and which performs best?",{"text":85,"@type":77},"The research evaluates three Machine Learning algorithms (Stochastic gradient descent, Naïve Bayes, Logistic Regression) and two Deep Learning models (LSTM variants such as AWD-LSTM/ASGD Weight-Dropped LSTM). 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