scholarly journals Measuring political legitimacy with Twitter: Insights from India’s Aadhaar program

2021 ◽  
pp. 146144482110334
Author(s):  
Laura C Mahrenbach ◽  
Jürgen Pfeffer

As emerging powers forge ahead with big data initiatives, questions arise regarding the implications of these programs for governance in the Global South more broadly. One understudied aspect deals with how actors attribute legitimacy to governments’ big data activities. We explore actors’ agency in one crucial case: the world’s largest demographic and biometric data program, India’s Aadhaar. Analyzing roughly 250,000 tweets collected in the first 10 years of Aadhaar’s operation, we find that both normative acceptance and cost–benefit calculations are crucial for legitimacy attribution. This finding challenges mainstream theoretical approaches, which prioritize normative factors and often fail to examine how normative and material factors interact during legitimacy attribution. In addition, our study demonstrates a new, mixed-methods approach to measuring legitimacy attribution using Twitter data, which overcomes traditional challenges. As such, we underline the viability of Twitter data as a tool for social measurement.

2019 ◽  
Vol 11 (1) ◽  
pp. 11-14
Author(s):  
Xiaorui Shao ◽  
◽  
Chang-So Kim ◽  
Kwak Dong Ryul

2018 ◽  
Vol 17 (02) ◽  
pp. 1850013 ◽  
Author(s):  
Avinash Samuel ◽  
Dilip Kumar Sharma

Social Networks have become an important part of people’s life as they share their day-to-day happenings, portray their opinions on various topics or find out information related to their queries. Due to the overwhelming volume of tweets generated on a daily basis, it is not possible to read all the tweets and differentiate the tweets based on the views or the attitude they portray only. The primary objective of sentiment analysis is to find out the attitude/emotion/opinion/sentiment that is present in the material provided. Commonly, the tweets can be clustered on the basis of them being positive or negative i.e. being in favour of the topic or being against the topic. The clustering and indexing of the tweets help in the organisation, searching, and summarisation of task. Twitter data are considered as Big Data and the information contained within the tweets is unstructured and if utilised properly can be very useful for educational and governance purposes. In this paper, a method is presented which clusters and then indexes the tweets on the basis of the sentiments and emoticons that are present in the tweet.


Sign in / Sign up

Export Citation Format

Share Document