Functional analysis of the 2020 U.S. elections on Twitter and Facebook using machine learning

Social Networking Sites (SNS), such as Facebook and Twitter, are important tools for political campaigns. A line of related work analyzed political campaigns online. The initial efforts in analyzing campaign discourse functions relied on human analysis, which is time consuming and does not scale wel...

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Bibliographic Details
Main Authors: Alashri, Saud, Alalola, Turki
Format: Conference Proceeding
Language:English
Subjects:
Applied computing
Applied computing > Law, social and behavioral sciences
Big Data
Blogs
Computing methodologies
Computing methodologies > Artificial intelligence
Computing methodologies > Artificial intelligence > Natural language processing
Computing methodologies > Artificial intelligence > Natural language processing > Discourse, dialogue and pragmatics
Discourse Analysis
Functional Theory
Information systems
Information systems > Information systems applications
Linguistics
Machine learning
Political Discourse
Psychology
Social networking (online)
Social Networking Sites
Text Classification
US Elections
Voting
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Summary:Social Networking Sites (SNS), such as Facebook and Twitter, are important tools for political campaigns. A line of related work analyzed political campaigns online. The initial efforts in analyzing campaign discourse functions relied on human analysis, which is time consuming and does not scale well with big data. To address these gaps, we propose a model to detect the type of campaign topics: Policy vs. Character, and how the public (commentators) responded to these messages. The proposed model yielded an accuracy of 78% (F-measure) in detecting post type. Moreover, experimental results show the analysis of commentators linguistic and psychological characteristics.
ISSN:2473-991X
DOI:10.1109/ASONAM49781.2020.9381302