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Academic literature on the topic 'Truthseekers'

Author: Grafiati

Published: 27 July 2024

Last updated: 31 July 2025

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Journal articles on the topic "Truthseekers"

1

Maryniak, Irena. "Truthseekers, Godbuilders or culture vultures? Some supplementary remarks on religious perspectives in modern Soviet literature." Religion in Communist Lands 16, no. 3 (1988): 227–36. http://dx.doi.org/10.1080/09637498808431375.

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Al-alshaqi, Mohammed, Danda B. Rawat, and Chunmei Liu. "A BERT-Based Multimodal Framework for Enhanced Fake News Detection Using Text and Image Data Fusion." Computers 14, no. 6 (2025): 237. https://doi.org/10.3390/computers14060237.

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The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable information contained within images and texts. In response to this, we propose a multimodal fake news detection method based on BERT, with an extension to text combined with the extracted text from images through Optical Character Recognition (OCR). Here, we consider extending feature analysis with BERT_base_uncased to process inpu

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3

Al-Tarawneh, Mutaz, Ashraf Al-Khresheh, Omar Al-irr, et al. "Towards Accurate Fake News Detection: Evaluating Machine Learning Approaches and Feature Selection Strategies." European Journal of Pure and Applied Mathematics 18, no. 2 (2025): 6087. https://doi.org/10.29020/nybg.ejpam.v18i2.6087.

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The rapid spread of fake news in the digital age poses significant challenges, necessitating effective detection methods. This study presents a comprehensive evaluation of various ensemble and machine learning classifiers, combined with different feature selection techniques, to improve the accuracy and reliability of the detection of fake news. Using the TruthSeeker dataset, this research examines feature selection methods such as Recursive Feature Elimination (RFE), SelectKBest, Principal Component Analysis (PCA) and Genetic Algorithms (GA), analyzing their impact on model performance. Key m

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4

Olagunju, Afeez Ayomide, and Iyabo Olukemi Awoyelu. "Performance Evaluation of Fake News Detection Models." International Journal of Information Technology and Computer Science 16, no. 6 (2024): 89–100. https://doi.org/10.5815/ijitcs.2024.06.07.

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The rapid spread of misinformation on social media platforms, especially Twitter, presents a challenge in the digital age. Traditional fact-checking struggles with the volume and speed of misinformation, while existing detection systems often focus solely on linguistic features, ignoring factors like source credibility, user interactions, and context. Current automated systems also lack the accuracy to differentiate between genuine and fake news, resulting in high rates of false positives and negatives. This study investigates the creation of a Twitter bot for detecting fake news using deep le

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5

Lantang, Oktavian A., Raphael Edber Christopher Sendow, and Feisy Diane Kambey. "Implementation of Bidirectional Long Short-Term Memory and Convolutional Neural Network in Detecting Hoax Content on Social Media." Applied Information System and Management (AISM) 8, no. 1 (2025): 151–58. https://doi.org/10.15408/aism.v8i1.45222.

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The advancement of internet technology has facilitated the spread of information, including false information or fake news. The dissemination of hoaxes on social media, such as Twitter, can cause confusion and negatively impact society. This study aims to implement a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) for hoax detection. The dataset used consists of English tweets containing both real and fake news, collected between 2020 and 2022, as provided by the TruthSeeker dataset. The model utilizes an embedding layer with wor

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6

Gupta, Dhvani. "DETECTION OF FAKE NEWS ON TWITTER USING MACHINE LEARNING: AN XGBOOST-BASED APPROACH WITH SENTIMENT AND SOURCE CHARACTERISTIC ANALYSIS." International Journal of Advanced Research 12, no. 08 (2024): 948–64. http://dx.doi.org/10.21474/ijar01/19332.

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The spread of fake news on social media platforms is becoming an increasingly alarming problem with fake news becoming more deceptive and harder to detect. Twitter, in particular, poses a significant threat as fake news spreads faster than real news on the platform, enhancing misinformation and leading to serious consequences.This project presents a novel machine learning-based approach for detecting fake news tweets on Twitter using the TruthSeeker 2023 dataset from the University of New Brunswick. As the largest ground truth dataset for fake news detection on social media, it contains over 1

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7

Al-Tarawneh, Mutaz A. B., Omar Al-irr, Khaled S. Al-Maaitah, Hassan Kanj, and Wael Hosny Fouad Aly. "Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach." Computers 13, no. 9 (2024): 239. http://dx.doi.org/10.3390/computers13090239.

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The widespread dissemination of fake news on social media has necessitated the development of more sophisticated detection methods to maintain information integrity. This research systematically investigates the effectiveness of different word embedding techniques—TF-IDF, Word2Vec, and FastText—when applied to a variety of machine learning (ML) and deep learning (DL) models for fake news detection. Leveraging the TruthSeeker dataset, which includes a diverse set of labeled news articles and social media posts spanning over a decade, we evaluated the performance of classifiers such as Support V

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8

Dadkhah, Sajjad, Xichen Zhang, Alexander Gerald Weismann, Amir Firouzi, and Ali A. Ghorbani. "The Largest Social Media Ground-Truth Dataset for Real/Fake Content: TruthSeeker." IEEE Transactions on Computational Social Systems, 2023, 1–15. http://dx.doi.org/10.1109/tcss.2023.3322303.

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9

Khalil, Maysa, and Mohammad Azzeh. "Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)." International Journal of Speech Technology, June 14, 2024. http://dx.doi.org/10.1007/s10772-024-10106-8.

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Books on the topic "Truthseekers"

1

Murphy, C. E. Truthseeker. Ballantine Books, 2010.

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2

Bradford, Rod. The truthseeker: The biography of D.M. Bennett, the nineteenth century's most controversial publisher and free-speech martyr. Barricade Books, 2004.

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3

James, Jann. Truthseekers. PageTurner: Press & Media, 2021.

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James, Jann. The Truthseekers. PageTurner: Press & Media, 2021.

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James, Jann. The Truthseekers. PageTurner: Press & Media, 2021.

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Barlow, Russell H., and Heidi P. S. Barlow. TruthSeekers Workbook World Geography. TruthSeekers Foundation, 2021.

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7

Martin, Rob St. Truthseekers: Welcome to Blackriver. Sabledrake Enterprises, 2008.

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Sr, John L. Harris. NOW! to the TruthSeekers: A Series of Thought Provoking Fictional Essays & Short Stories. AuthorHouse, 2004.

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Heavenly Inbox. Amazon, 2023.

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10

Truthseeker. Pleasant Word-A Division of WinePress Publishing, 2003.

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Book chapters on the topic "Truthseekers"

1

Yablokov, Ilya, and Precious N. Chatterje-Doody. "The world according to the Truthseekers." In Russia Today and Conspiracy Theories. Routledge, 2021. http://dx.doi.org/10.4324/9780367224684-4.

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Guerar, Meriem, and Mauro Migliardi. "TruthSeekers Chain: Leveraging Invisible CAPPCHA, SSI and Blockchain to Combat Disinformation on Social Media." In Computational Science and Its Applications – ICCSA 2022 Workshops. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10542-5_29.

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3

Nash, David. "Short Articles from the Truthseeker." In Blasphemy in Britain and America, 1800-1930, Volume 4. Routledge, 2024. http://dx.doi.org/10.4324/9781003577201-7.

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Conference papers on the topic "Truthseekers"

1

Arslan, Recep Sinan, and Dilara Çelik. "Prediction of fake news on X: An analysis of LLMs and machine learning methods on TruthSeeker." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017046.

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Happy, Asif Iqubal, Subodh Kant Tiwari, Mithun Kumar Paswan, Sikander Azad, and Pragti. "Machine Learning Methodologies for Predicting Fake News on Social Media X: A Comparative Investigation Over TruthSeeker Dataset." In 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024. https://doi.org/10.1109/iccsc62048.2024.10830410.

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