Social media data

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.

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 OSoMe: The IUNI observatory on social media

2016 ◽  
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 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.

scholarly journals OSoMe: The IUNI observatory on social media

2016 ◽  
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 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.

Location Integration and Data Markets

2019 ◽  
pp. 66-88
Author(s):  
Rowan Wilken
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Case Studies ◽  
Data Capture ◽  
Social Networking Service ◽  
Social Media Data ◽  
Locative Media ◽  
Mobile Social Networking ◽  
Revenue Models ◽  
Media Data

This chapter explores the still-evolving business and revenue models and geolocation data capture efforts of two commercial businesses now central to the contemporary settlement of locative media: Foursquare and Facebook. In Foursquare’s case, it underwent a quite dramatic series of transformations, evolving from a check-in based mobile social networking service, to a search and recommendation service, and now also serving as a firm offering location intelligence related enterprise services. In Facebook’s case, it set about further strengthening its grip on social media data markets by adding geolocation functionalities and geodata capture capabilities to its social networking operations. These two case studies provide a rich composite picture of the business ecologies of locational information. The aim in selecting these cases is to develop a clearer understanding of how both firms accrue location data and how they extract location value—that is, how this information is shared, harvested, valued, reused, and commodified.

scholarly journals OSoMe: the IUNI observatory on social media

2016 ◽  
Vol 2 ◽  
pp. e87 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document