A Novel Evolving Sentimental Bag-of-Words Approach for Feature Extraction to Detect Misinformation

Barve, Yashoda; Saini, Jatinderkumar R.; Pal, Kaushika; Kotecha, Ketan

doi:10.14569/ijacsa.2022.0130431

Cited by 15 publications

(2 citation statements)

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“…According to the literature from among basic features, sentimental features play a crucial role in detecting misinformation. According to the literature, true information has more positive words, and misinformation consists of more negative words [ 2 , 3 , 42 ]. Therefore, the authors manually developed a sentimental bag of words with 793 words.…”

Section: Proposed Methodologymentioning

confidence: 99%

Detecting and classifying online health misinformation with ‘Content Similarity Measure (CSM)’ algorithm: an automated fact-checking-based approach

Barve¹,

Saini

2023

J Supercomput

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Information dissemination occurs through the 'word of media' in the digital world. Fraudulent and deceitful content, such as misinformation, has detrimental effects on people. An implicit fact-based automated fact-checking technique comprising information retrieval, natural language processing, and machine learning techniques assist in assessing the credibility of content and detecting misinformation. Previous studies focused on linguistic and textual features and similarity measures-based approaches. However, these studies need to gain knowledge of facts, and similarity measures are less accurate when dealing with sparse or zero data. To fill these gaps, we propose a 'Content Similarity Measure (CSM)' algorithm that can perform automated fact-checking of URLs in the healthcare domain. Authors have introduced a novel set of content similarity, domain-specific, and sentiment polarity score features to achieve journalistic fact-checking. An extensive analysis of the proposed algorithm compared with standard similarity measures and machine learning classifiers showed that the ‘content similarity score’ feature outperformed other features with an accuracy of 88.26%. In the algorithmic approach, CSM showed improved accuracy of 91.06% compared to the Jaccard similarity measure with 74.26% accuracy. Another observation is that the algorithmic approach outperformed the feature-based method. To check the robustness of the algorithms, authors have tested the model on three state-of-the-art datasets, viz. CoAID, FakeHealth, and ReCOVery. With the algorithmic approach, CSM showed the highest accuracy of 87.30%, 89.30%, 85.26%, and 88.83% on CoAID, ReCOVery, FakeHealth (Story), and FakeHealth (Release) datasets, respectively. With a feature-based approach, the proposed CSM showed the highest accuracy of 85.93%, 87.97%, 83.92%, and 86.80%, respectively.

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Section: Proposed Methodologymentioning

confidence: 99%

Detecting and classifying online health misinformation with ‘Content Similarity Measure (CSM)’ algorithm: an automated fact-checking-based approach

Barve¹,

Saini

2023

J Supercomput

Self Cite

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“…Furthermore, GPT provides a large selection of word representations (Jadhav & Shukla, 2024). In order to identify false information from web URLs, Barve et al (2022) presented a novel sentiment-based incremental machine-learning approach. Zhou and Zafarani (2020) found that sentiment was one of the latent textual features in machine-learning models for the detection of fake news.…”

Section: Previous Work On Automatic Fake News Detectionmentioning

confidence: 99%

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

Atchariyachanvanich,

Saengkhunthod,

Kerdnoonwong

et al. 2024

Journal of Cases on Information Technology

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These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

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Evaluating Machine Learning Algorithms for New Indian Parliament Building Sentiment Analysis

Saini,

Vaidya,

Kasande

2024

Proceedings of Third International Conference on Computing and Communication Networks

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A Novel Evolving Sentimental Bag-of-Words Approach for Feature Extraction to Detect Misinformation

Cited by 15 publications

References 0 publications

Detecting and classifying online health misinformation with ‘Content Similarity Measure (CSM)’ algorithm: an automated fact-checking-based approach

Detecting and classifying online health misinformation with ‘Content Similarity Measure (CSM)’ algorithm: an automated fact-checking-based approach

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

Evaluating Machine Learning Algorithms for New Indian Parliament Building Sentiment Analysis

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