How to Use Social Media Data for Political Science Research

2020 ◽  
pp. 404-423
Author(s):  
Pablo Barberá ◽  
Zachary C. Steinert-Threlkeld
Keyword(s):  
Social Media ◽  
Political Science ◽  
Science Research ◽  
Social Media Data ◽  
Media Data

scholarly journals Study Designs for Quantitative Social Science Research Using Social Media

2020 ◽  
Author(s):  
Leticia Bode ◽  
Pamela Davis-Kean ◽  
Lisa Singh ◽  
Tanya Berger-Wolf ◽  
Ceren Budak ◽  
...  
Keyword(s):  
Social Media ◽  
Social Science ◽  
Computer Science ◽  
Quantitative Research ◽  
Social Science Research ◽  
Basic Research ◽  
Science Research ◽  
Social Media Data ◽  
Computer Scientists ◽  
Media Data

Social media provides a rich amount of data on the everyday lives, opinions, thoughts, beliefs, and behaviors of individuals and organizations in near real-time. Leveraging these data effectively and responsibly should therefore improve our ability to understand political, psychological, economic, and sociological behaviors and opinions across time. This article is the first in a series of white papers that will provide a summary of the discussions derived from meetings of social scientists and computer scientists with the goal of creating consensus for how social and computer science could converge to answer important questions about complex human behaviors and dynamics using social media data. We present three basic research designs that are commonly used in social science and are applicable to research using social media data: qualitative observation, experiments, and surveys. We also discuss a fourth design that is primarily informed by computer science, non-designed data, but that can inform social science research. After a brief discussion of the general approach of these designs and their applicability for use with social media data, we discuss the challenges associated with their use with social media data and potential solutions for “convergence” of these methods for future quantitative research in the social sciences.

scholarly journals Analysis and Visualization Considerations for Quantitative Social Science Research Using Social Media Data

2021 ◽  
Author(s):  
J. Bradford Jensen ◽  
Lisa Singh ◽  
Pamela Davis-Kean ◽  
Katharine Abraham ◽  
Paul Beatty ◽  
...  
Keyword(s):  
Social Media ◽  
Visual Analytics ◽  
Social Science Research ◽  
Science Research ◽  
Data Set ◽  
Future Directions ◽  
Social Media Data ◽  
Analysis Techniques ◽  
Media Data ◽  
Different Levels

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.

New Data Sources in Social Science Research: Things to Know Before Working With Reddit Data

2019 ◽  
pp. 089443931989330 ◽  
Author(s):  
Ashley Amaya ◽  
Ruben Bach ◽  
Florian Keusch ◽  
Frauke Kreuter
Keyword(s):  
Social Media ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Lessons Learned ◽  
Data Sources ◽  
Front Page ◽  
Social Media Data ◽  
New Research ◽  
Media Data

Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are used to working, so the assumptions often used to plan and manage a project may no longer hold. For example, social media data are so large that they may not be able to be processed on a single machine; they are in file formats with which many researchers are unfamiliar, and they require a level of data transformation and processing that has rarely been required when using more traditional data sources (e.g., survey data). Unfortunately, this type of information is often not obvious ahead of time as much of this knowledge is gained through word-of-mouth and experience. In this article, we attempt to document several challenges and opportunities encountered when working with Reddit, the self-proclaimed “front page of the Internet” and popular social media site. Specifically, we provide descriptive information about the Reddit site and its users, tips for using organic data from Reddit for social science research, some ideas for conducting a survey on Reddit, and lessons learned in merging survey responses with Reddit posts. While this article is specific to Reddit, researchers may also view it as a list of the type of information one may seek to acquire prior to conducting a project that uses any type of social media data.

Using Social Media Data to Analyse Issue Engagement During the 2017 German Federal Election

2022 ◽  
Vol 22 (1) ◽  
pp. 1-25
Author(s):  
Florian Meier ◽  
Alexander Bazo ◽  
David Elsweiler
Keyword(s):  
Social Media ◽  
Political Parties ◽  
Political Science ◽  
Computational Social Science ◽  
The Political ◽  
Political Strategy ◽  
Political Debate ◽  
Social Media Data ◽  
Political Left ◽  
Media Data

A fundamental tenet of democracy is that political parties present policy alternatives, such that the public can participate in the decision-making process. Parties, however, strategically control public discussion by emphasising topics that they believe will highlight their strengths in voters’ minds. Political strategy has been studied for decades, mostly by manually annotating and analysing party statements, press coverage, or TV ads. Here we build on recent work in the areas of computational social science and eDemocracy, which studied these concepts computationally with social media. We operationalize issue engagement and related political science theories to measure and quantify politicians’ communication behavior using more than 366k Tweets posted by over 1,000 prominent German politicians in the 2017 election year. To this end, we first identify issues in posted Tweets by utilising a hashtag-based approach well known in the literature. This method allows several prominent issues featuring in the political debate on Twitter that year to be identified. We show that different political parties engage to a larger or lesser extent with these issues. The findings reveal differing social media strategies by parties located at different sides of the political left-right scale, in terms of which issues they engage with, how confrontational they are and how their strategies evolve in the lead-up to the election. Whereas previous work has analysed the general public’s use of Twitter or politicians’ communication in terms of cross-party polarisation, this is the first study of political science theories, relating to issue engagement, using politicians’ social media data.

scholarly journals Measurement Considerations for Quantitative Social Science Research Using Social Media Data

2020 ◽  
Author(s):  
Jonathan Ladd ◽  
Rebecca Ryan ◽  
Lisa Singh ◽  
Leticia Bode ◽  
Ceren Budak ◽  
...  
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Social Scientists ◽  
Social Media Data ◽  
Measurement Issues ◽  
Computer Scientists ◽  
Media Data

Harnessing social media data for social science research entails creating measures out of the largely unstructured, noisy data that users generate on different platforms. This harnessing, particularly of data at scale, requires using methods developed in computer science. But it also typically requires integrating these methods with assessments of measurement quality along social science criteria -- reliability, validity and unbiasedness. In this paper, we outline measurement issues that arise when using social media data. We show examples of how to construct measures and discuss different measurement considerations and best practices. We conclude with a discussion of ways to accelerate research in this space, highlighting contributions that can be made by both social scientists and computer scientists.

scholarly journals Data Acquisition, Sampling, and Data Preparation Considerations for Quantitative Social Science Research Using Social Media Data

2021 ◽  
Author(s):  
Zeina Mneimneh ◽  
Josh Pasek ◽  
Lisa Singh ◽  
Rachel Best ◽  
Leticia Bode ◽  
...  
Keyword(s):  
Social Media ◽  
Data Acquisition ◽  
Social Science Research ◽  
Science Research ◽  
Necessary Condition ◽  
Data Preparation ◽  
Social Scientists ◽  
Social Media Data ◽  
Computer Scientists ◽  
Media Data

The convergence of methods and relevant theories between computer scientists and social scientists is a necessary condition for leveraging social media data to understand this increasingly important window into human societies. This paper focuses on issues of data acquisition, sampling, and data preparation. These topics incorporate data collection methods, sampling strategies, population mismatch adjustments, and other data acquisition and data preparation decisions.

scholarly journals Modeling Considerations for Quantitative Social Science Research Using Social Media Data

2021 ◽  
Author(s):  
Ceren Budak ◽  
Stuart Soroka ◽  
Lisa Singh ◽  
Michael Bailey ◽  
Leticia Bode ◽  
...  
Keyword(s):  
Social Media ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Model Fit ◽  
Model Construction ◽  
Interesting Aspect ◽  
Future Directions ◽  
Social Media Data ◽  
Media Data

In this paper, the fourth in a series of white papers, we provide a summary of the discussions and future directions that came from the topical meeting that focused on model construction with social media data. A particularly interesting aspect of this meeting was, in our view, discussion of the different disciplines’ requirements and approaches to modeling and the different considerations that are used to assess model fit.

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