OSoMe: The IUNI observatory on social media

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available at osome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.

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OSoMe: the IUNI observatory on social media

PeerJ Computer Science ◽

10.7717/peerj-cs.87 ◽

2016 ◽

Vol 2 ◽

pp. e87 ◽

Cited By ~ 16

Author(s):

Clayton A. Davis ◽

Giovanni Luca Ciampaglia ◽

Luca Maria Aiello ◽

Keychul Chung ◽

Michael D. Conover ◽

...

Keyword(s):

Social Media ◽

Online Social Networks ◽

Network Science ◽

Computational Social Science ◽

Collaborative Effort ◽

Indiana University ◽

Social Phenomena ◽

Social Media Data ◽

Derived Data ◽

Media Data

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present theIUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available atosome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.

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Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-030320-040844 ◽

2020 ◽

Vol 3 (1) ◽

pp. 433-458 ◽

Cited By ~ 1

Author(s):

Rion Brattig Correia ◽

Ian B. Wood ◽

Johan Bollen ◽

Luis M. Rocha

Keyword(s):

Mental Health ◽

Social Media ◽

Population Level ◽

Data Access ◽

Health Conditions ◽

Social Phenomena ◽

Medical Treatments ◽

Social Media Data ◽

Health Related ◽

Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

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Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

Social Science Computer Review ◽

10.1177/0894439318822007 ◽

2019 ◽

Vol 38 (5) ◽

pp. 633-650 ◽

Cited By ~ 2

Author(s):

Josh Pasek ◽

Colleen A. McClain ◽

Frank Newport ◽

Stephanie Marken

Keyword(s):

Social Media ◽

Survey Data ◽

Large Scale ◽

Presidential Approval ◽

Social Phenomena ◽

Social Media Data ◽

Complex Picture ◽

Large Scale Survey ◽

Media Data

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

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Exploring Public Perceptions of Native-Born American Emigration Abroad and Renunciation of American Citizenship through Social Media

Immigration and the Current Social, Political, and Economic Climate ◽

10.4018/978-1-5225-6918-3.ch004 ◽

2019 ◽

pp. 60-101

Author(s):

Shalin Hai-Jew

Keyword(s):

Social Media ◽

Research Literature ◽

Social Phenomena ◽

Social Media Data ◽

The Public ◽

The Social ◽

American Citizenship ◽

Social Media Platforms ◽

Work Done ◽

Media Data

There has been little work done on American emigration abroad and even less done on the formal renunciation of American citizenship. This chapter provides an overview of both phenomena in the research literature and then provides some methods for using the extraction of social media data and their visualization as a way of tapping into the public mindsets about these social phenomena. The software tools used include the following: Network Overview, Discovery and Exploration for Excel (NodeXL Basic), NVivo, and Maltego Carbon; the social media platforms used include the following: Wikipedia, YouTube, Twitter, and Flickr.

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

ACM Transactions on Internet Technology ◽

10.1145/3467020 ◽

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.

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Social media data

Data in Society ◽

10.1332/policypress/9781447348214.003.0005 ◽

2019 ◽

pp. 47-60

Author(s):

Adrian Tear ◽

Humphrey Southall

Keyword(s):

Social Media ◽

Population Based ◽

Computational Social Science ◽

Data Capture ◽

Personal Privacy ◽

User Privacy ◽

Social Media Data ◽

Data Validity ◽

Media Data ◽

By Products

The increasing availability of huge volumes of social media ‘Big Data’ from Facebook, Flickr, Instagram, Twitter and other social network platforms, combined with the development of software designed to operate at web scale, has fuelled the growth of computational social science. Often analysed by ‘data scientists’, social media data differ substantially from the datasets officially disseminated as by-products of government-sponsored activity, such as population censuses or administrative data, which have long been analysed by professional statisticians. This chapter outlines the characteristics of social media data and identifies key data sources and methods of data capture, introducing several of the technologies used to acquire, store, query, visualise and augment social media data. Unrepresentativeness of, and lack of (geo)demographic control in, social media data are problematic for population-based research. These limitations, alongside wider epistemological and ethical concerns surrounding data validity, inadvertent co-option into research and protection of user privacy, suggest that caution should be exercised when analysing social media datasets. While care must be taken to respect personal privacy and sample assiduously, this chapter concludes that statisticians, who may be unfamiliar with some of the programmatic steps involved in accessing social media data, must play a pivotal role in analysing it.

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Exploring Public Perceptions of Native-Born American Emigration Abroad and Renunciation of American Citizenship through Social Media

Social Media Listening and Monitoring for Business Applications - Advances in E-Business Research ◽

10.4018/978-1-5225-0846-5.ch013 ◽

2017 ◽

pp. 338-382 ◽

Cited By ~ 1

Author(s):

Shalin Hai-Jew

Keyword(s):

Social Media ◽

Research Literature ◽

Social Phenomena ◽

Social Media Data ◽

The Public ◽

The Social ◽

American Citizenship ◽

Social Media Platforms ◽

Work Done ◽

Media Data

There has been little work done on American emigration abroad and even less done on the formal renunciation of American citizenship. This chapter provides an overview of both phenomena in the research literature and then provides some methods for using the extraction of social media data and their visualization as a way of tapping into the public mindsets about these social phenomena. The software tools used include the following: Network Overview, Discovery and Exploration for Excel (NodeXL Basic), NVivo, and Maltego Carbon; the social media platforms used include the following: Wikipedia, YouTube, Twitter, and Flickr.

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Invisible Data: A Framework for Understanding Visibility Processes in Social Media Data

Social Media + Society ◽

10.1177/2056305120984472 ◽

2021 ◽

Vol 7 (1) ◽

pp. 205630512098447

Author(s):

Christina Neumayer ◽

Luca Rossi ◽

David M. Struthers

Keyword(s):

Social Media ◽

Conceptual Framework ◽

Human Interaction ◽

Digital Data ◽

Social Phenomena ◽

Social Media Data ◽

Four Dimensions ◽

Social Visibility ◽

Point Of Departure ◽

Media Data

Social media data are increasingly used to study a variety of social phenomena. This development is based on the assumption that digital traces left on social media can provide insights into the nature of human interaction. In this research, we turn our attention to what remains invisible in research based on social media data. Using Andrea Brighenti’s work on “social visibility” as a point of departure, we unpack data invisibilities, as they are created within four dimensions: people and intentionality, technologies and tools, accessibility and form, and meaning and imaginaries. We introduce the notion of quasi-visible data as an intermediary between visible and invisible data highlighting the processual character of data invisibilities. With this conceptual framework, we contribute to developing a more reflective and ethical field of research into the study of social phenomena based on social media data. We conclude by arguing that distancing ourselves from the assumption that all social media data are visible and focusing on the invisible will enhance our understanding of digital data.

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