Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm
Hyunseo Lee,
Ian Paik Choe,
Jioh In,
Han Sol Kim
Issue:
Volume 5, Issue 2, December 2020
Pages:
20-26
Received:
6 July 2020
Accepted:
21 July 2020
Published:
19 September 2020
Abstract: With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main source of news being social media articles. Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements). Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed. To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article. To establish an accurate system, words used in examples of fake news and real news were collected using Python. Verifying fake and real news is an important process that all news should go through as it can result in immense consequences. Data on real news and fake news were collected from Kaggle. We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.
Abstract: With recent advancements in social media and technology as a whole, online news sources have increased. Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms. In the current status quo, many people feel comfortable with their main s...
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A Model for Prediction of Kidney Cancer Using Data Analytics Technique
Issue:
Volume 5, Issue 2, December 2020
Pages:
27-36
Received:
31 August 2020
Accepted:
6 October 2020
Published:
17 October 2020
Abstract: Across the globe, kidney cancer and other cancerous diseases has been a threat to human lives. The incidence and mortality rate represent a significant and growing threat to both developed and developing countries especially in Africa, where most cancers are diagnosed at an advanced stage. This typically contributes to its complications and high rate of mortality, and has been attributed to limited awareness of early signs and symptoms of the disease, lack of detective mechanism and inaccessible cancer care in our health care centres. To preclude the harm and mortality caused by the disease, an intelligent mechanism for early prediction and prognosis of the syndrome is vital. However, early detection and prognosis requires an accurate information and analytic procedure that will assist and equip the health-care providers/public with the skills to identify early the indicators of the disease. Efforts in this work, produced a model for early prediction of kidney cancer using data analytic approach. Dataset and reports pertaining to the disease were acquired from selected private and public hospitals in fifty-two (52) selected LGA in Nigeria. A two-layered classifier system consisting of Artificial Neural Networks (ANN) and Decision Tree (DT) designed for the work was successfully employed in the model building. Waikato Environment for Knowledge Analysis (WEKA) platform was used for the experiment. The performance of the classifiers considered was compared using standard metrics of accuracy and time taken as benchmark. Experimental results show that the J48 decision tree algorithm outperform all other algorithms in the classifier family with correctly classified instances of 74.7%, F-Measure of 0.614, TP rate of 0.747, FP rate of 0.135, precision and recall of 0.687 and 0.714 respectively. It took the algorithm, 0.03 seconds to build the model. The performance of this algorithm proved its suitability as a valuable tool for the research purpose. The model will in no small measure support the efforts of the national health scheme in preventing the disease mortality rate.
Abstract: Across the globe, kidney cancer and other cancerous diseases has been a threat to human lives. The incidence and mortality rate represent a significant and growing threat to both developed and developing countries especially in Africa, where most cancers are diagnosed at an advanced stage. This typically contributes to its complications and high ra...
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