Utilizing Facebook pages of the political parties to automatically predict the political orientation of Facebook users. Issue 5 (12th September 2016)

• Record Type: Journal Article
• Authors: David, Esther; Zhitomirsky-Geffet, Maayan; Koppel, Moshe; Uzan, Hodaya
• Is Part Of: Online information review. Volume 40:Issue 5(2016)
• Page Start: 610
• Page End: 623
• Publication Date: 2016-09-12
• Subjects: Social networks -- Machine learning -- Automatic political profiling -- Cross-corpora classification -- Text categorization; 025.04
• DOI: 10.1108/OIR-09-2015-0308
• Languages: English
• ISSNs: 1468-4527

Abstract

Abstract : Purpose: Social network sites have been widely adopted by politicians in the last election campaigns. To increase the effectiveness of these campaigns the potential electorate is to be identified, as targeted ads are much more effective than non-targeted ads. Therefore, the purpose of this paper is to propose and implement a new methodology for automatic prediction of political orientation of users on social network sites by comparison to texts from the overtly political parties' pages. Design/methodology/approach: To this end, textual information on personal users' pages is used as a source of statistical features. The authors apply automatic text categorization algorithms to distinguish between texts of users from different political wings. However, these algorithms require a set of manually labeled texts for training, which is typically unavailable in real life situations. To overcome this limitation the authors propose to use texts available on various political parties' pages on a social network site to train the classifier. The political leaning of these texts is determined by the political affiliation of the corresponding parties. The classifier learned on such overtly political texts is then applied on the personal user pages to predict their political orientation. To assess the validity and effectiveness of the proposed methodology two corpora were constructed: personal Facebook pages of 450 Israeli citizens, and political parties Facebook pages of the nine prominent Israeli parties. Findings: The authors found that when a political tendency classifier is trained and tested on data in the same corpus, accuracy is very high. More significantly, training on manifestly political texts (political party Facebook pages) yields classifiers which can be used to classify non-political personal Facebook pages with fair accuracy. Social implications: Previous studies have shown that targeted ads are more effective than non-targeted ads leading to substantial saving in the advertising budget. Therefore, the approach for automatic determining the political orientation of users on social network sites might be adopted for targeting political messages, especially during election campaigns. Originality/value: This paper proposes and implements a new approach for automatic cross-corpora identification of political bias of user profiles on social network. This suggests that individuals' political tendencies can be identified without recourse to any tagged personal data. In addition, the authors use learned classifiers to determine which self-identified centrists lean left or right and which voters are likely to switch allegiance in subsequent elections.