Fatma Dogan

ORCID Identifier(s)

0000-0003-0489-9300

Graduation Semester and Year

2015

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Chengkai Li

Second Advisor

Bahram Khalili

Fourth Advisor

Dimitrios Zikos

Abstract

In increasing democracy and improving political discourse, political fact-checking has come to be a necessity. While politicians make claims about facts all the time, journalists and fact-checkers oftentimes reveal them as false, exaggerated, or misleading. Use of technology and social media tools such as Facebook and Twitter has rapidly increased the spread of misinformation. Thus, human fact-checkers face difficulty in keeping up with a massive amount of claims, and falsehoods frequently outpace truths. All U.S. politicians have successively adopted Twitter, and they make use of Twitter for a wide variety of purposes, a great example being making claims to enhance their popularity. Toward the aim of helping journalists and fact-checkers, we developed a system that automatically detects check-worthy factual claims in tweets related to U.S. politics and posts them on a publicly visible Twitter account. The research consists of two processes: collecting and processing political tweets. The process for detecting check-worthy factual claims involves preprocessing collected tweets, finding the check-worthiness score of each tweet, and applying several filters to eliminate redundant and irrelevant tweets. Finally, a political classification model distinguishes tweets related to U.S. politics from other tweets and reposts them on a created Twitter account.

Keywords

Factual claim, Twitter

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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