8,629 research outputs found
Mathematical Modeling of Public Opinion using Traditional and Social Media
With the growth of the internet, data from text sources has become increasingly available to researchers in the form of online newspapers, journals, and blogs. This data presents a unique opportunity to analyze human opinions and behaviors without soliciting the public explicitly. In this research, I utilize newspaper articles and the social media service Twitter to infer self-reported public opinions and awareness of climate change. Climate change is one of the most important and heavily debated issues of our time, and analyzing large-scale text surrounding this issue reveals insights surrounding self-reported public opinion. First, I inquire about public discourse on both climate change and energy system vulnerability following two large hurricanes. I apply topic modeling techniques to a corpus of articles about each hurricane in order to determine how these topics were reported on in the post event news media. Next, I perform sentiment analysis on a large collection of data from Twitter using a previously developed tool called the hedonometer . I use this sentiment scoring technique to investigate how the Twitter community reports feeling about climate change. Finally, I generalize the sentiment analysis technique to many other topics of global importance, and compare to more traditional public opinion polling methods. I determine that since traditional public opinion polls have limited reach and high associated costs, text data from Twitter may be the future of public opinion polling
An Application of Sentiment Analysis Techniques to Determine Public Opinion in Social Media
This paper describes a prototype application that gathers textual data from the microblogging platform Twitter and carries out sentiment analysis to determine the polarity and subjectivity in relation to Brexit, the UK´ s exit from the European Union. The design, implementation and testing of the developed prototype will be discussed and an experimental evaluation of the product described. Specifically we provide insight into how events affect public opinion and how sentiment and public mood may be gathered from textual twitter data and propose this as an alternative to opinion polls. Traditional approaches to opinion polling face growing challenges in capturing the public mood. Small sample response and the time it takes to capture swings in public opinion make it difficult to provide accurate data for the political process. With over 500 million daily messages posted worldwide, the social media platform Twitter is an untapped resource of information. Users post short real time messages views and opinions on many topics, often signed with a â#hashtagâ to classify and document the subject matter in discussion. In this paper we apply automated sentiment analysis methods to tweets giving a measure of public support or hostility to a topic (âBrexitâ). The data were collected during several periods to determine changes in opinion. Using machine learning techniques we show that changes in opinion were also related to external events. Limitations of the method are that age, location and education are confounding factors where Twitter users over represent a young, urban public. However, the economic advantage of the method over real-time telephone polling are considerable
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
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
Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump
Measuring and forecasting opinion trends from real-time social media is a
long-standing goal of big-data analytics. Despite its importance, there has
been no conclusive scientific evidence so far that social media activity can
capture the opinion of the general population. Here we develop a method to
infer the opinion of Twitter users regarding the candidates of the 2016 US
Presidential Election by using a combination of statistical physics of complex
networks and machine learning based on hashtags co-occurrence to develop an
in-domain training set approaching 1 million tweets. We investigate the social
networks formed by the interactions among millions of Twitter users and infer
the support of each user to the presidential candidates. The resulting Twitter
trends follow the New York Times National Polling Average, which represents an
aggregate of hundreds of independent traditional polls, with remarkable
accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls
by 10 days, showing that Twitter can be an early signal of global opinion
trends. Our analytics unleash the power of Twitter to uncover social trends
from elections, brands to political movements, and at a fraction of the cost of
national polls
Assessing candidate preference through web browsing history
Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preferenceâincluding the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of votersâ candidate preferences in ways that are difficult to achieve using polls.Accepted manuscrip
On using Twitter to monitor political sentiment and predict election results
The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an
electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General
Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using
supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using
both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during
an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content
Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions
Opinion mining and demographic attribute inference have many applications in
social science. In this paper, we propose models to infer daily joint
probabilities of multiple latent attributes from Twitter data, such as
political sentiment and demographic attributes. Since it is costly and
time-consuming to annotate data for traditional supervised classification, we
instead propose scalable Learning from Label Proportions (LLP) models for
demographic and opinion inference using U.S. Census, national and state
political polls, and Cook partisan voting index as population level data. In
LLP classification settings, the training data is divided into a set of
unlabeled bags, where only the label distribution in of each bag is known,
removing the requirement of instance-level annotations. Our proposed LLP model,
Weighted Label Regularization (WLR), provides a scalable generalization of
prior work on label regularization to support weights for samples inside bags,
which is applicable in this setting where bags are arranged hierarchically
(e.g., county-level bags are nested inside of state-level bags). We apply our
model to Twitter data collected in the year leading up to the 2016 U.S.
presidential election, producing estimates of the relationships among political
sentiment and demographics over time and place. We find that our approach
closely tracks traditional polling data stratified by demographic category,
resulting in error reductions of 28-44% over baseline approaches. We also
provide descriptive evaluations showing how the model may be used to estimate
interactions among many variables and to identify linguistic temporal
variation, capabilities which are typically not feasible using traditional
polling methods
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