Surveying public opinion using label prediction on social media data

2019 ◽  
Author(s):  
Marija Stanojevic ◽  
Jumanah Alshehri ◽  
Zoran Obradovic
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Social Media Data ◽  
Label Prediction ◽  
Media Data

scholarly journals Journalists’ Use of Social Media to Infer Public Opinion: The citizens’ perspective

2021 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Online Survey ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Traditional Approaches ◽  
Journalistic Practice ◽  
Media Data

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data is being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N=1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that: (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data; (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion; and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

scholarly journals (Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys

2021 ◽  
Author(s):  
Kenneth Joseph ◽  
Sarah Shugars ◽  
Ryan Gallagher ◽  
Jon Green ◽  
Alexi Quintana Mathé ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Social Media Data ◽  
Public Opinion Surveys ◽  
Media Data

scholarly journals Detect Extreme Sentiments on Social Networks using BERT

2021 ◽  
Author(s):  
Muhammad Luqman Jamil ◽  
Sebastião Pais ◽  
João Cordeiro ◽  
Gaël Dias
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Public Opinion ◽  
Social Network ◽  
Social Networking ◽  
Online Social Networking ◽  
Social Media Data ◽  
Preceding Work ◽  
Unsupervised Approach ◽  
Media Data

Abstract Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined user’s sentiments on these platforms to study their behaviour in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work [1], by proposing an unsupervised approach to automatically detect extreme opinions/posts in social networks. We have evaluated our performance on five different social network and media datasets. In this work, we use the semi-supervised approach BERT to check the accuracy of our classified dataset. The latter task shows that, in these datasets, posts that were previously classified as negative or positive are, in fact, extremely negative or positive in many cases.

scholarly journals Reports of the Workshops Held at the 2017 International AAAI Conference on Web and Social Media

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 93-98
Author(s):  
Jisun An ◽  
Giovanni Luca Ciampaglia ◽  
Nir Grinberg ◽  
Kenneth Joseph ◽  
Alexios Mantzarlis ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Observational Studies ◽  
The Other ◽  
Current Interest ◽  
Social Media Data ◽  
Demographic Research ◽  
User Perceptions ◽  
Informal Setting ◽  
Media Data

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s International Conference on Web and Social Media (AAAI-17) was held in Montréal, Québec, Canada on Tuesday, May 15, 2017. There were eight workshops in the program: Digital Misinformation, Events Analytics Using Social Media Data, News and Public Opinion, Observational Studies through Social Media, Perceptual Biases and Social Media, Social Media and Demographic Research, Studying User Perceptions and Experiences with Algorithms, The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from two of the workshop chose to include papers in the AAAI Technical Reports series (Observational Studies through Social Media and News and Public Opinion). Their papers were included as a nonarchival part of the ICWSM proceedings. Organizers from four of the workshops (Digital Misinformation, News and Public Opinion, Perceptual Biases and Social Media, and Studying User Perceptions and Experiences with Algorithms) submitted reports, which are reproduced in this report. Brief summaries of the other four workshops have been reproduced from their website descriptions.

Measuring Public Opinion with Social Media Data

2017 ◽  
Author(s):  
Marko Klašnja ◽  
Pablo Barberá ◽  
Nick Beauchamp ◽  
Jonathan Nagler ◽  
Joshua A. Tucker
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Social Networking ◽  
Social Networking Sites ◽  
Research Agenda ◽  
The Novel ◽  
Social Media Data ◽  
Public Opinion Surveys ◽  
New Directions ◽  
Media Data

This chapter examines the use of social networking sites such as Twitter in measuring public opinion. It first considers the opportunities and challenges that are involved in conducting public opinion surveys using social media data. Three challenges are discussed: identifying political opinion, representativeness of social media users, and aggregating from individual responses to public opinion. The chapter outlines some of the strategies for overcoming these challenges and proceeds by highlighting some of the novel uses for social media that have fewer direct analogs in traditional survey work. Finally, it suggests new directions for a research agenda in using social media for public opinion work.

scholarly journals Journalists’ Use of Social Media to Infer Public Opinion: The Citizens’ Perspective

2018 ◽  
Vol 38 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Online Survey ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Traditional Approaches ◽  
Journalistic Practice ◽  
Media Data

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data are being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults ( N = 1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data, (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion, and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

Journalists’ Use of Social Media to Infer Public Opinion: The citizens’ perspective

2021 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Online Survey ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Traditional Approaches ◽  
Journalistic Practice ◽  
Media Data

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data is being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N=1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that: (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data; (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion; and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

scholarly journals Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 187 ◽  
Author(s):  
Marko M. Skoric ◽  
Jing Liu ◽  
Kokil Jaidka
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Predictive Power ◽  
Meta Analysis ◽  
Structural Features ◽  
Social Media Data ◽  
Statistical Estimates ◽  
Electoral Outcomes ◽  
Media Data ◽  
Different Levels

In recent years, many studies have used social media data to make estimates of electoral outcomes and public opinion. This paper reports the findings from a meta-analysis examining the predictive power of social media data by focusing on various sources of data and different methods of prediction; i.e., (1) sentiment analysis, and (2) analysis of structural features. Our results, based on the data from 74 published studies, show significant variance in the accuracy of predictions, which were on average behind the established benchmarks in traditional survey research. In terms of the approaches used, the study shows that machine learning-based estimates are generally superior to those derived from pre-existing lexica, and that a combination of structural features and sentiment analyses provides the most accurate predictions. Furthermore, our study shows some differences in the predictive power of social media data across different levels of political democracy and different electoral systems. We also note that since the accuracy of election and public opinion forecasts varies depending on which statistical estimates are used, the scientific community should aim to adopt a more standardized approach to analyzing and reporting social media data-derived predictions in the future.

Policy Change and Public Opinion: Measuring Shifting Political Sentiment With Social Media Data

2020 ◽  
Vol 48 (5) ◽  
pp. 612-621
Author(s):  
Nicholas Joseph Adams-Cohen
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Policy Change ◽  
Gay Rights ◽  
The United States ◽  
Same Sex ◽  
Data Set ◽  
Social Media Data ◽  
Same Sex Marriage ◽  
Media Data

This article uses Twitter data and machine-learning methods to analyze the causal impact of the Supreme Court’s legalization of same-sex marriage at the federal level in the United States on political sentiment and discourse toward gay rights. In relying on social media text data, this project constructs a large data set of expressed political opinions in the short time frame before and after the Obergefell v. Hodges decision. Due to the variation in state laws regarding the legality of same-sex marriage prior to the Supreme Court’s decision, I use a difference-in-difference estimator to show that, in those states where the Court’s ruling produced a policy change, there was relatively more negative movement in public opinion toward same-sex marriage and gay rights issues as compared with other states. This confirms previous studies that show Supreme Court decisions polarize public opinion in the short term, extends previous results by demonstrating opinion becomes relatively more negative in states where policy is overturned, and demonstrates how to use social media data to engage in causal analyses.

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