The relationship between misinformation and social noise and their impact on the information ecosystem

Alsaid, Manar

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https://hdl.handle.net/2142/130873 Copy

Description

Title

The relationship between misinformation and social noise and their impact on the information ecosystem

Author(s)

Alsaid, Manar

Issue Date

2024-10-16

Keyword(s)

• Data curation
• Information management
• Information privacy
• Intellectual property

Abstract

Social noise and social entropy are new concepts modeled after Shannon’s information and communication theory, in which the interference of noise between the sender and receiver is measured using entropy. Social noise in the context of social media plays a vital role in magnifying and spreading misinformation, which in turn impacts the overall information ecosystem. Ecosystems are made of interconnected and integrated parts that rely on one another to maintain balance and survive. Studies related to social noise and misinformation have shown that social noise can contribute significantly to spreading misinformation and potentially alter the original intended message (Alsaid & Pampapura, 2022); Alsaid et al. (2024). paper investigates methods of quantifying social noise using entropy to minimize the spread of misinformation on social media, particularly X. Using a combination of Uncertainty Reduction Theory (URT) and Social Entropy, data analysis was performed using one million tweets harvested from #Ukraine. Data analysis involved several methods: sentiment analyses, term association, network maps, and entropy computation. Results have shown a direct relationship between social noise and social entropy as a measure of uncertainty. Also, social noise and uncertainty decrease with the use of URLs and rich content. It is evident from the results that the entropy value is influenced by the accuracy of keywords identified using topic modeling as descriptive of social noise constructs. Semantic analysis of tweets can help improve the definition of social noise constructs, leading to enhanced and more accurate entropy calculation. Future studies may consider advanced machine learning and AI

Publisher

Association for Library and Information Science Education

Series/Report Name or Number

Proceedings of the ALISE Annual Conference, 2024

Type of Resource

text

Genre of Resource

• Conference Paper
• Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130873

DOI

https://doi.org/10.21900/j.alise.2024.1774

Copyright and License Information

• Copyright 2024 Manar Alsaid
• This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/).

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