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

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.

2021 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson

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.


2021 ◽  
Author(s):  
Kenneth Joseph ◽  
Sarah Shugars ◽  
Ryan Gallagher ◽  
Jon Green ◽  
Alexi Quintana Mathé ◽  
...  

2021 ◽  
Author(s):  
Muhammad Luqman Jamil ◽  
Sebastião Pais ◽  
João Cordeiro ◽  
Gaël Dias

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.


2021 ◽  
Author(s):  
J. Bradford Jensen ◽  
Lisa Singh ◽  
Pamela Davis-Kean ◽  
Katharine Abraham ◽  
Paul Beatty ◽  
...  

This is the fifth in a series of white papers providing a summary of the discussions and future directions that are derived from these topical meetings. This paper focuses on issues related to analysis and visual analytics. While these two topics are distinct, there are clear overlaps between the two. It is common to use different visualizations during analysis and given the sheer volume of social media data, visual analytic tools can be important during analysis, as well as during other parts of the research lifecycle. Choices about analysis may be informed by visualization plans and vice versa - both are key in communicating about a data set and what it means. We also recognized that each field of research has different analysis techniques and different levels of familiarity with visual analytics. Putting these two topics into the same meeting provided us with the opportunity to think about analysis and visual analytics/visualization in new, synergistic ways.


Author(s):  
V. Subramaniyaswamy ◽  
R. Logesh ◽  
M. Abejith ◽  
Sunil Umasankar ◽  
A. Umamakeswari

Social Media has become one of the major industries in the world. It has been noted that almost three fourth of the world's population use social media. This has instigated many researches towards social media. One such useful application is the sentimental analysis of real time social media data for security purposes. The insights that are generated can be used by law enforcement agencies and for intelligence purposes. There are many types of analyses that have been done for security purposes. Here, the authors propose a comprehensive software application which will meticulously scrape data from Twitter and analyse them using the lexicon based analysis to look for possible threats. They propose a methodology to obtain a quantitative result called criticality to assess the level of threat for a public event. The results can be used to understand people's opinions and comments with regard to specific events. The proposed system combines this lexicon based sentimental analysis along with deep data collection and segregates the emotions into different levels to analyse the threat for an event.


AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 93-98
Author(s):  
Jisun An ◽  
Giovanni Luca Ciampaglia ◽  
Nir Grinberg ◽  
Kenneth Joseph ◽  
Alexios Mantzarlis ◽  
...  

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.


Author(s):  
Marko Klašnja ◽  
Pablo Barberá ◽  
Nick Beauchamp ◽  
Jonathan Nagler ◽  
Joshua A. Tucker

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.


2018 ◽  
Vol 38 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson

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.


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