Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2013
Abstract
With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 24th ACM Conference on Hypertext and Social Media (Hypertext 2013)
First Page
174
Last Page
178
Identifier
10.1145/2481492.2481512
Publisher
ACM Press
City or Country
Paris, France
Citation
WEI, Zhongyu; HE, Yulan; GAO, Wei; LI, Binyang; ZHOU, Lanjun; and WONG, Kam-Fai.
Main-stream media behaviour analysis on Twitter: A case study on UK general election. (2013). Proceedings of the 24th ACM Conference on Hypertext and Social Media (Hypertext 2013). 174-178.
Available at: https://ink.library.smu.edu.sg/sis_research/4586
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2481492.2481512