Document Type

Article

Publication Date

12-2015

Publication Title

Big Data and Society

Volume

2

Issue

2

Abstract

In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today’s society. Platforms like Facebook, Twitter, Google+ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: ‘‘zooming’’ in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text.

Comments

© The Author(s) 2015This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).

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