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Detecting Shifts in Public Opinion: A Big Data Study of Global News Content

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XVII
Subtitle of host publication17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings
Publisher or commissioning bodySpringer, Cham
Number of pages11
ISBN (Electronic)9783030017682
ISBN (Print)9783030017675
DateAccepted/In press - 13 Jul 2018
DatePublished (current) - 5 Oct 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Rapid changes in public opinion have been observed in recent years about a number of issues, and some have attributed them to the emergence of a global online media sphere [1, 2]. Being able to monitor the global media sphere, for any sign of change, is an important task in politics, marketing and media analysis. Particularly interesting are sudden changes in the amount of attention and sentiment about an issue, and their temporal and geographic variations. In order to automatically monitor media content, to discover possible changes, we need to be able to access sentiment across various languages, and specifically for given entities or issues. We present a comparative study of sentiment in news content across several languages, assembling a new multilingual corpus and demonstrating that it is possible to detect variations in sentiment through machine translation. Then we apply the method on a number of real case studies, comparing changes in media coverage about Weinstein, Trump and Russia in the US, UK and some other EU countries.

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