Browsing by Author "Hosen, MD Alamin"

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A Proposed Bi-LSTM Method to Fake News Detection
(Daffodil International University, 2022-04-22) Islam, Taminul; Hosen, MD Alamin; Mony, Akhi; Hasan, MD Touhid; Jahan, Israt; Kundu, Arindom
Recent years have seen an explosion in social media usage, allowing people to connect with others. Since the appearance of platforms such as Facebook and Twitter, such platforms influence how we speak, think, and behave. This problem negatively undermines confidence in content because of the existence of fake news. For instance, false news was a determining factor in influencing the outcome of the U.S. presidential election and other sites. Because this information is so harmful, it is essential to make sure we have the necessary tools to detect and resist it. We applied Bidirectional Long Short-Term Memory (Bi-LSTM) to determine if the news is false or real in order to showcase this study. A number of foreign websites and newspapers were used for data collection. After creating & running the model, the work achieved 84% model accuracy and 62.0 F1-macro scores with training data.
Fake News Detection Using Machine Learning Algorithm
(Daffodil International University, 2022-01-04) Hosen, MD Alamin; Mony, Akhi; Hasan, MD Touhid
Recent years have seen an explosion in social media usage, allowing people to connect with others. Since the appearance of platforms such as Facebook and Twitter, such platforms influence how we speak, think, and behave. This problem negatively undermines confidence in content because of the existence of fake news. For instance, false news was a determining factor in influencing the outcome of the 2016 presidential election. Because this information is so harmful, it is essential to make sure we have the necessary tools to detect and resist it. It's difficult to determine what news is false and what is true. We've hardly put in any effort for such a high-quality outcome. This work is for analyzing & delectating the fake news from a fresh collected dataset. We applied Bidirectional Long Short-Term Memory (BiLSTM) to determine if the news is false or real in order to showcase this study. This machine learning technique and approach are being used since there is a lot of study into how people can improve the efficiency and accuracy of their work. A number of foreign websites and newspapers were used for data collection. After creates & running the model, the work achieved 84% model accuracy and 62.0 F1-macro score with training data.