國立虎尾科技大學 |

AI Approaches to Understand Human Deceptions, Perceptions, and Perspectives in Social Media.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	AI Approaches to Understand Human Deceptions, Perceptions, and Perspectives in Social Media./
Author:	Li, Chih-Yuan.
Description:	1 online resource (204 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
Contained By:	Dissertations Abstracts International85-04A.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380604918

AI Approaches to Understand Human Deceptions, Perceptions, and Perspectives in Social Media.
Li, Chih-Yuan.

AI Approaches to Understand Human Deceptions, Perceptions, and Perspectives in Social Media. - 1 online resource (204 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: A.

Thesis (Ph.D.)--New Jersey Institute of Technology, 2023.

Includes bibliographical references

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens' perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups. This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine Learning approaches for fake news detection models, and a hybrid method for topic identification, whether they are fake or real. To understand the user's perceptions or attitude toward some topics, this study analyzes the sentiments expressed in social media text. The sentiment analysis of posts can be used as an indicator to measure how topics are perceived by the users and how their perceptions as a whole can affect decision makers in government and industry, especially during the COVID-19 pandemic. It is difficult to measure the public perception of government policies issued during the pandemic. The citizen responses to the government policies are diverse, ranging from security or goodwill to confusion, fear, or anger. This dissertation provides a near real-time approach to track and monitor public reactions toward government policies by continuously collecting and analyzing Twitter posts about the COVID-19 pandemic.To address the social media's overwhelming number of posts, content echo-chamber, and information isolation issue, this dissertation provides a multiple view-based summarization framework where the same contents can be summarized according to different perspectives. This framework includes components of choosing the perspectives, and advanced text summarization approaches. The proposed approaches in this dissertation are demonstrated with a prototype system to continuously collect Twitter data about COVID-19 government health policies and provide analysis of citizen concerns toward the policies, and the data is analyzed for fake news detection and for generating multiple-view summaries.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380604918Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Fake news detectionIndex Terms--Genre/Form:

554714
Electronic books.

AI Approaches to Understand Human Deceptions, Perceptions, and Perspectives in Social Media.
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