Defining News Authenticity on Social Media Using Machine Learning Approach

Hlaing, May Me Me; Kham, Nang Saing Moon

doi:10.1109/icca49400.2020.9022837

Cited by 17 publications

(6 citation statements)

References 7 publications

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“…In comparison, our model's accuracy using SVM is 89.8%, and NB classifiers archives 87.5% accuracy. • Some of the studies focus on News content and user feature, for example, [5,7,16,27] for misinformation detection.…”

Section: Resultsmentioning

confidence: 99%

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Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

Asfand-e-yar¹,

Hashir²,

Tanvir³

et al. 2023

Computers, Materials &Amp; Continua

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The growth of the internet and technology has had a significant effect on social interactions. False information has become an important research topic due to the massive amount of misinformed content on social networks. It is very easy for any user to spread misinformation through the media. Therefore, misinformation is a problem for professionals, organizers, and societies. Hence, it is essential to observe the credibility and validity of the News articles being shared on social media. The core challenge is to distinguish the difference between accurate and false information. Recent studies focus on News article content, such as News titles and descriptions, which has limited their achievements. However, there are two ordinarily agreed-upon features of misinformation: first, the title and text of an article, and second, the user engagement. In the case of the News context, we extracted different user engagements with articles, for example, tweets, i.e., read-only, user retweets, likes, and shares. We calculate user credibility and combine it with article content with the user's context. After combining both features, we used three Natural language processing (NLP) feature extraction techniques, i.e., Term Frequency-Inverse Document Frequency (TF-IDF), Count-Vectorizer (CV), and Hashing-Vectorizer (HV). Then, we applied different machine learning classifiers to classify misinformation as real or fake. Therefore, we used a Support Vector Machine (SVM), Naive Byes (NB), Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and K-Nearest Neighbors (KNN). The proposed method has been tested on a real-world dataset, i.e., "fakenewsnet". We refine the fakenewsnet dataset repository according to our required features. The dataset contains 23000+ articles with millions of user engagements. The highest accuracy score is 93.4%. The proposed model achieves its highest accuracy using count vector features and a random forest classifier. Our discoveries confirmed that the proposed classifier would effectively classify misinformation in social networks.

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Section: Resultsmentioning

confidence: 99%

“…Dementieva et al [6] follow different hypotheses and target five languages, i.e., English, Spanish, French, German, and Russian. In [7], authors used an RF, DT, and Adaboost classifier to detect fake News from social media. Random forest gives better results, and all their results accuracy is under 80%.…”

Section: Machine Learning Classifiersmentioning

confidence: 99%

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

Asfand-e-yar¹,

Hashir²,

Tanvir³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…They utilize the synonymous extraction technique and three classifiers based on a multidimensional dataset. Testing findings demonstrate the efficiency in defining news authenticity in online news media [13].…”

Section: B Boenninghoff Et Al (2019)mentioning

confidence: 86%

A New Technology for Verifying the Identity of the Online Social Network User Account Author

2022

JISSR

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The Web has a huge amount of data accessible for internet users, and a large amount of data is also produced, thanks to the development and expansion of web technology. The Internet has become an online learning platform to exchange ideas and share views. Social networking services like Twitter, Facebook, and Google+ quickly acquire popularity since they enable users to exchange opinions on issues, talk with other groups or post messages worldwide. The expanded usage of Online Social Network (OSN) has become necessary to appear to grow Authorship Verification (AV), OSN is the environment in which users can connect with other users to discuss ideas of any topics then expand data and information. AV is considered as a resource of researches and information in different ways, as is the case Sentiment Analysis (SA). In this paper, the proposed technique is compared with the previous feature extraction technique which was inefficient in providing better results comprised of the Tweets API dataset. Twitter is a popular website for social networking users posting and interacting with "tweets". The new model is henceforth capable to provide better accuracy.

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“…Recently, most of the bot detection and fact checking platforms are employing artificial intelligence (AI) to cross check the originality of the shared information and both machine learning (ML) [16] and deep learning (DL) [17] models are gaining huge significance among the researchers because of their superior learning and classification abilities. ML and DL based text classification can help in distinguishing social bots from normal users and fact checking the shared information.…”

Section: Introductionmentioning

confidence: 99%

An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach

Sethurajan,

K．

2023

MethodsX

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Defining News Authenticity on Social Media Using Machine Learning Approach

Cited by 17 publications

References 7 publications

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

A New Technology for Verifying the Identity of the Online Social Network User Account Author

An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach

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