Home // INTELLI 2023, The Twelfth International Conference on Intelligent Systems and Applications // View article

Using Historical Social Media Retrieved Trust Attributes to Help Distinguishing Trustworthy Users

Authors:
Teng-Chieh Huang
K. Suzanne Barber

Keywords: social media, trust, trust attributes, time series forecast, random forests classification.

Abstract:
With the penetration of social media across the world, the information generated by the users has increased exponentially. The wisdom of crowds can now be easily accessed from the Internet. The problem is, how to correctly interpret the true public opinion without distorting it? Considering the spam or malicious users hidden in the social media spreading misinformation and disinformation, the solution might not be trivial. Some previous work uses quantity accumulation trying to mitigate the influence of bad users. More recent works add machine learning techniques to help with the correct judgment. However, the importance of the individual user - the actual person who is behind the screen, does not attract the attention it deserves. In this work, focusing on the history of user behavior, we provide a different angle to understand the connection between the credibility of social media users and the trustworthiness of their virtual representatives. By analyzing Twitter data from November 2017 to November 2021 which contains three types of users (typical, topic-related, and expert) on two target domains (politics and finance), we can gain deeper insights on the social media users and their trustworthiness.

Pages: 13 to 18

Copyright: Copyright (c) IARIA, 2023

Publication date: March 13, 2023

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-68558-064-3

Location: Barcelona, Spain

Dates: from March 13, 2023 to March 17, 2023