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Measuring relative public opinion on location-based social media: the 2016 presidential election

Authors: Jean-Claude Thill*, University of North Carolina at Charlotte, Zhaoya Gong, University of Birmingham, Tengteng Cai, University of North Carolina at Charlotte, Scott Hale, University of Oxford, Mark Graham, University of Oxford
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems, Political Geography
Keywords: location-based social media, polling, opinion, word embedding, presidential elections
Session Type: Paper
Day: 4/8/2020
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual Track 9
Presentation File: No File Uploaded

Social media has become an emerging alternative to opinion polls for public opinion
collection, while it is still posing many challenges as a passive data source, such as
structurelessness, quantifiability, and representativeness. Social media data with
geotags provide new opportunities to unveil the geographic locations of users
expressing their opinions. This paper aims to answer two questions: 1) whether
quantifiable measurement of public opinion can be obtained from social media and 2)
whether it can produce better or complementary measures compared to opinion polls.
This research proposes a novel approach to measure the relative opinion of Twitter
users towards public issues in order to accommodate more complex opinion structures
and take advantage of the geography pertaining to the public issues. To ensure that
this new measure is technically feasible, a modeling framework is developed including
building a training dataset by adopting a state-of-the-art approach and devising a new
deep learning method called Opinion-Oriented Word Embedding. With a case study of
the tweets selected for the 2016 U.S. presidential election, we demonstrate the
predictive superiority of our relative opinion approach and we show how it can aid
visual analytics and support opinion predictions. Although the relative opinion measure
is proved to be more robust compared to polling, our study also suggests that the
former can advantageously complement the later in opinion prediction.

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