A framework for understanding citizens' political participation in social media

被引:11
作者
Pandey, Vidushi [1 ]
Gupta, Sumeet [2 ]
Chattopadhyay, Manojit [2 ]
机构
[1] ICFAI Fdn Higher Educ, ICFAI Business Sch IBS, Hyderabad, Telangana, India
[2] Indian Inst Management Raipur, Dept Informat Technol & Syst, Raipur, Madhya Pradesh, India
关键词
Individual; Text mining; Twitter; Social media; Online political participation; Teorell et al; 's (2007) framework; Online activism; INFORMATION TECHNOLOGY; CIVIC ENGAGEMENT; AFFORDANCES; INTERNET; ONLINE; TWITTER; CLICKTIVISM; USERS;
D O I
10.1108/ITP-03-2018-0140
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Purpose The purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society. Design/methodology/approach This study is based on Teorellet al.'s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms. Findings The analysis reveals that Activism is the most prominent form of political participation undertaken by people on Twitter. Other activities that were undertaken include Formal Political participation and Consumer participation. The analysis also reveals that identity of participant does not play a classifying role as expected from the theoretical framework. It was found that the social media as a platform facilitates new forms of participation which are not feasible offline. Research limitations/implications The current work considers only the microblogging platform of Twitter as the data source. For a more comprehensive insight, analysis of other social media platforms is also required. Originality/value To the best of the authors' knowledge, this is one of the few analyses where such a large database covering multiple social media events has been created and analysed using supervised text classification algorithms. A large proportion of previous studies on social media have been based on case study and have limited analysis to only a particular event on social media. Although there exist a few works that have studied a vast and varied collection of social media data (Gaby and Caren, 2012;Shirazi, 2013;Rane and Salem, 2012), such efforts are few in number. This study aims to add to that stream of work where a wider and more generalized set of social media data is studied.
引用
收藏
页码:1053 / 1075
页数:23
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