Demystifying black-box learning models of rumor detection from social media posts

Cataloged from PDF version of thesis.

Bibliographic Details
Main Authors:	Tafannum, Faiza, Shopnil, Mir Nafis Sharear, Salsabil, Anika, Ahmed, Navid
Other Authors:	Alam, Md.Golam Rabiul
Format:	Thesis
Language:	en_US
Published:	BRAC University 2021
Subjects:	Social media Rumor Detection Black box Machine learning Deep learning Explainable LIME COVID-19 Classifier
Online Access:	http://hdl.handle.net/10361/15604

id	10361-15604
record_format	dspace
spelling	10361-156042022-01-26T10:08:22Z Demystifying black-box learning models of rumor detection from social media posts Tafannum, Faiza Shopnil, Mir Nafis Sharear Salsabil, Anika Ahmed, Navid Alam, Md.Golam Rabiul Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Social media Rumor Detection Black box Machine learning Deep learning Explainable LIME COVID-19 Classifier Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Social media and its users are vulnerable to the spread of rumors, therefore, protect ing users from these rumors spread is extremely important. This research proposes a novel approach for rumor detection in social media that consists of multiple robust models: Support Vector Machine, XGBoost Classifier, Random Forest Classifier, Extra Tree Classifier, and Decision Tree Classifier. To evaluate more, we com bine these five different machine learning models to build our own hybrid model. Then, we apply two deep learning models- Long-Short Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) and both show promising results with high accuracy. For evaluations, we are using two datasets COVID19 Fake News Dataset and Twitter15 and Twitter16- two publicly available datasets concatenated. The datasets contain posts from both Facebook and Twit ter. We extract the textual part of source posts in vector representations and fit them into the models for predicting results and we evaluate the results. These arti ficial intelligence algorithms are often referred to as “Black-box” where data goes in the box and predictions come out of the box but what is happening inside the box frequently remains cloudy. Although there have been many inspired works for fake news detection, still the number of works regarding rumor detection lags behind and the models used in the existing works do not explain their decision-making process. But with explainable AI, the opaque process happening inside the black box can be explained. We use LIME to explain our models’ predictions. We take models with higher accuracy and illustrate which feature of the data contributes the most for a post to be predicted as a rumor or a non-rumor by the models, thus, demystifying the black box learning models. Our hybrid model achieves an accuracy of 93.22% and 82.49%, while LSTM provides 99.81%, 98.41% and BERT provides 99.62%, 94.80% accuracy on the COVID-19, Twitter15 and Twitter16 datasets respectively. Faiza Tafannum Mir Nafis Sharear Shopnil Anika Salsabil Navid Ahmed B. Computer Science 2021-11-04T07:06:55Z 2021-11-04T07:06:55Z 2021 2021-09 Thesis ID: 17101063 ID: 17101423 ID: 17101498 ID: 17101373 http://hdl.handle.net/10361/15604 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 37 Pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Social media Rumor Detection Black box Machine learning Deep learning Explainable LIME COVID-19 Classifier
spellingShingle	Social media Rumor Detection Black box Machine learning Deep learning Explainable LIME COVID-19 Classifier Tafannum, Faiza Shopnil, Mir Nafis Sharear Salsabil, Anika Ahmed, Navid Demystifying black-box learning models of rumor detection from social media posts
description	Cataloged from PDF version of thesis.
author2	Alam, Md.Golam Rabiul
author_facet	Alam, Md.Golam Rabiul Tafannum, Faiza Shopnil, Mir Nafis Sharear Salsabil, Anika Ahmed, Navid
format	Thesis
author	Tafannum, Faiza Shopnil, Mir Nafis Sharear Salsabil, Anika Ahmed, Navid
author_sort	Tafannum, Faiza
title	Demystifying black-box learning models of rumor detection from social media posts
title_short	Demystifying black-box learning models of rumor detection from social media posts
title_full	Demystifying black-box learning models of rumor detection from social media posts
title_fullStr	Demystifying black-box learning models of rumor detection from social media posts
title_full_unstemmed	Demystifying black-box learning models of rumor detection from social media posts
title_sort	demystifying black-box learning models of rumor detection from social media posts
publisher	BRAC University
publishDate	2021
url	http://hdl.handle.net/10361/15604
work_keys_str_mv	AT tafannumfaiza demystifyingblackboxlearningmodelsofrumordetectionfromsocialmediaposts AT shopnilmirnafissharear demystifyingblackboxlearningmodelsofrumordetectionfromsocialmediaposts AT salsabilanika demystifyingblackboxlearningmodelsofrumordetectionfromsocialmediaposts AT ahmednavid demystifyingblackboxlearningmodelsofrumordetectionfromsocialmediaposts
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Demystifying black-box learning models of rumor detection from social media posts

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