Fake News Detection using Machine Learning

Kulkarni, Prasad; Karwande, Suyash; Keskar, Rhucha; Kale, Prashant; Iyer, Sumitra

doi:10.1051/itmconf/20214003003

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…To contextualize the results obtained, [5] reported an of 85%, precision of 84%, recall of 87%, and F1 score of 85% for their logistic regression classi er. Also, [12] reported an accuracy of 90.32% with SDG and 88.47% and 86.90% with SVM and KNN respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Vikram Tembhurne & Almin [14] Kulkarni et al, [5] addressed the concern of fake news in online resources by proposing a machine learning-based detection model. Their study employed classi ers like Random Forest, Logistic Regression, Decision Tree, KNN, and Gradient Booster, the results showed that Logistic Regression achieved the highest accuracy of 85.04%, followed by Random Forest with 84.50% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Addressing the challenge of fake news detection has become paramount in curbing the proliferation of false information across various mediums, including social media and messaging platforms [5]. Factchecking has been a widely used method, involving the manual veri cation of news articles against credible sources [6].…”

Section: Introductionmentioning

confidence: 99%

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Fake News Detection Using a Logistic Regression Model and Natural Language Processing Techniques

Adeyiga,

Toriola,

Abioye(Ogunbiyi)

et al. 2023

Preprint

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The proliferation of fake news has become a significant challenge in recent years, impacting democracy, the journalism industry, and people's daily lives. The spread of intentionally misleading or fabricated information has led to a decline in confidence in government institutions and has profound implications for people's daily lives. This study aims to detect false information and real news using logistic regression algorithms and natural language processing techniques, implement the model using Python, and develop a website for news classification. The "Fake News Detection" dataset from Kaggle, consisting of approximately 20,000 news articles labelled as real or fake, was used. Data cleaning was done and feature extraction techniques, including Term Frequency – Inverse Document Frequency (TF-IDF) vectorization, was applied to extract features from the data. The logistic regression model with K-Nearest Neighbour (KNN), Passive Aggressive classifier and Naïve Bayes model were trained on the extracted features and evaluated using various metrics. The system was implemented using Python and Google Collaboratory, with the front-end of the website developed using Hyper Text Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. The architecture of the system involves training the model and deploying it using Flask, a lightweight web framework. Evaluation of the classifiers show the following results: Accuracy - 97.90%, 82.92%, 91.32%, 89.71%; Precision - 96.59%, 80.25%, 94.13%, 93.88%; Recall - 99.32%, 94.08%, 90.88%, 89.14%; and F1 score - 97.94%, 86.62, 92.48, 91.45% for logistic regression, KNN, Passive Aggressive and Naïve Bayes classifier respectively. Based on these results, logistic regression outperformed the other three classifiers. This shows that logistic regression model is more effective in fake news detection. The developed system provides a valuable tool in combating fake news and contributes to the on-going research in automatic fake news detection using machine learning.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fake News Detection Using a Logistic Regression Model and Natural Language Processing Techniques

Adeyiga,

Toriola,

Abioye(Ogunbiyi)

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The mathematical formula y = mx + c is used in linear regression analysis to determine the line of greatest fitting for the correlation between the dependent variable (y) and the independent variable (x) [38]. Gradient Boosting algorithms can optimize different functions, which makes them flexible, and they often provide predictive accuracy [39]. After training and testing the data with the Gradient Boosting model using many cost functions, as shown in Table 7, we got MSE 0.7686.…”

Section: Regression Using Many Functions Of Evaluation Matrixmentioning

confidence: 99%

Predicting baccalaureate student result to prevent failure: a hybrid model approach

Essayad,

Moulay Abdella

2024

IJ-AI

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<div class="OutlineElement Ltr SCXW634036 BCX2" style="direction: ltr;"><p>The Moroccan Ministry of National Education has seen substantial modifications over the previous ten years, which have contributed to improving the quality of education. However, there is a discrepancy in the percentage of academic achievement between the regional directorates and educational institutions. Machine learning techniques have become a powerful tool for proactively predicting student admission. The goal of our paper is to build machine learning models using various algorithms to predict the final baccalaureate school year outcomes. We compare regression and classification to find the reasons behind students' failure and to choose an appropriate model for predicting the results. This helps decision-makers make appropriate interventions.</p></div>

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“…The dynamic system's accuracy is 93%, and it gets better with each repetition. Prasad, Suyash, Rhucha, Prashant and Sumitra in [10] paper, offer a solution by outlining a machine learning-based fake news detection technique. Prerequisite information acquired from different news websites is needed for this model.…”

Section: IImentioning

confidence: 99%

SenseWorth – A Tweets Classifier

Agarkar¹,

Chopdekar²,

Gujar³

et al. 2022

IJRASET

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A lot of people use tweeter to provide their opinions on various topics including sports, politics, finance, etc. Now the information that generally is present on Twitter does not have any means to check whether the data being twitter is correct or not. So in order to check the authenticity of the data,the data must be classified into true and false. Firstly, the dataset would be created using python by devising the code for the same. The code thus designed would be able to extract the tweets with the amount specified by the user. Hence the CSV would be created. After the creation of the CSV, data pre-processing would be applied to the data such that all the unnecessary data such as emojis, words, and information would be removed automatically, and thus we would receive the tweet without any kind of stop-words. This cleaned data would further be tested and trained with the help of machine learning algorithms. These Machine learning algorithms would generally be used to classify the data according to the domain and provide the user with an authenticated answer whether the tweet is true or not along with its accuracy. This helpsthe userto identify the tweet and thus provide an authenticated answer on which informationto believe and to which we should not

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Fake News Detection using Machine Learning

Cited by 6 publications

References 7 publications

Fake News Detection Using a Logistic Regression Model and Natural Language Processing Techniques

Fake News Detection Using a Logistic Regression Model and Natural Language Processing Techniques

Predicting baccalaureate student result to prevent failure: a hybrid model approach

SenseWorth – A Tweets Classifier

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