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Identifying similar opinions in news comments using a community detection algorithm

Identifying similar opinions in news comments using a community detection algorithm
Identifying similar opinions in news comments using a community detection algorithm
Despite playing many important roles in society, the news media have been frequently criticised for failing to represent a wide range of viewpoints. Online news systems have the potential to allow readers to add additional information and perspectives. However, due to the simplicity of the filtering mechanisms typically employed, these systems can themselves be prone to over-promoting popular viewpoints at the expense of others. Previous research has attempted to diversify news comments through the use of content similarity, sentiment analysis, named entity recognition, and other factors. In this paper we propose the use of a commonly used community detection algorithm on a network of voting data to identify sentiment groups in news discussion threads, with the eventual goal that these groups may be used to present diverse content. In a controlled experiment with 154 participants, we verify that the Louvain Community Detection algorithm is able to group users with accuracy comparable to an average human. This produces groups containing users who share similar sentiment on a given topic. This is an important step towards ensuring that each group is represented, as by using this method future news systems can ensure that more diverse views are represented in open comment threads.
community detection, news, comments, discussion, sentiment, viewpoint
978-3-319-27432-4
Scott, Jonathan
d1d8c6ed-90cc-4d26-a053-aece826dc716
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Leonard, Pauline
a2839090-eccc-4d84-ab63-c6a484c6d7c1
Scott, Jonathan
d1d8c6ed-90cc-4d26-a053-aece826dc716
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Leonard, Pauline
a2839090-eccc-4d84-ab63-c6a484c6d7c1

Scott, Jonathan, Millard, David and Leonard, Pauline (2015) Identifying similar opinions in news comments using a community detection algorithm. 7th International Conference on Social Informatics, China. (doi:10.1007/978-3-319-27433-1_7).

Record type: Conference or Workshop Item (Paper)

Abstract

Despite playing many important roles in society, the news media have been frequently criticised for failing to represent a wide range of viewpoints. Online news systems have the potential to allow readers to add additional information and perspectives. However, due to the simplicity of the filtering mechanisms typically employed, these systems can themselves be prone to over-promoting popular viewpoints at the expense of others. Previous research has attempted to diversify news comments through the use of content similarity, sentiment analysis, named entity recognition, and other factors. In this paper we propose the use of a commonly used community detection algorithm on a network of voting data to identify sentiment groups in news discussion threads, with the eventual goal that these groups may be used to present diverse content. In a controlled experiment with 154 participants, we verify that the Louvain Community Detection algorithm is able to group users with accuracy comparable to an average human. This produces groups containing users who share similar sentiment on a given topic. This is an important step towards ensuring that each group is represented, as by using this method future news systems can ensure that more diverse views are represented in open comment threads.

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Published date: 10 December 2015
Venue - Dates: 7th International Conference on Social Informatics, China, 2015-12-10
Keywords: community detection, news, comments, discussion, sentiment, viewpoint
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 385300
URI: https://eprints.soton.ac.uk/id/eprint/385300
ISBN: 978-3-319-27432-4
PURE UUID: de2be76a-303a-4d22-9b8a-5fd7b11ec6ce
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710
ORCID for Pauline Leonard: ORCID iD orcid.org/0000-0002-8112-0631

Catalogue record

Date deposited: 13 Jan 2016 14:52
Last modified: 29 Aug 2019 00:52

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Contributors

Author: Jonathan Scott
Author: David Millard ORCID iD
Author: Pauline Leonard ORCID iD

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