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Analyzing and Visualizing Government-Citizen Interactions on Twitter to Support Public Policy-making

Published: 09 April 2020 Publication History

Abstract

Twitter is widely adopted by governments to communicate with citizens. It has become a major source of data for analyzing how governments communicate with citizens and how citizens respond to such communication, uncovering important insights about government-citizen interactions that could be used to support public policy-making. This article presents research that aims at developing a software tool called Twitter Analytics for Government Intelligence and Public Participation (TA4GIP) that applies sentiment analysis and visualization techniques to information collected from Twitter and presents the findings to policy-makers and other non-technical users to facilitate understanding and interpretation. The use of the tool is illustrated through the case study of Twitter communication carried by five government secretaries responsible for health, education, social development, labor, and environment sectors in Mexico, and corresponding citizen responses over a nine-month period. The case study demonstrates that TA4GIP helps identify and analyze relevant aspects of government presence and citizen participation on social media, such as abnormal activity, salient topics being discussed, citizen views about enacted public policies, correlations between types of emotions in responses to particular government announcements, topics that generate polarized reactions from citizens, and many others.

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Information

Published In

cover image Digital Government: Research and Practice
Digital Government: Research and Practice  Volume 1, Issue 2
Special Issue on Government and Social Media and Regular Papers
April 2020
120 pages
EISSN:2639-0175
DOI:10.1145/3394083
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2020
Accepted: 01 August 2019
Revised: 01 June 2019
Received: 01 February 2019
Published in DGOV Volume 1, Issue 2

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Author Tags

  1. Digital government
  2. government 2.0
  3. sentiment analysis
  4. social media
  5. visual analysis

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Cited By

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  • (2024)Publics’ Motivations for Sharing Public Policy Promotions on Social Media and Their Impacts on Publics’ Sharing BehaviorAsian Communication Research10.20879/acr.2024.21.01121:1(143-163)Online publication date: 30-Apr-2024
  • (2023)The Impact of China’s Biopolitical Approach to COVID-19 on PetsAsian Studies10.4312/as.2023.11.3.93-12711:3(93-127)Online publication date: 7-Sep-2023
  • (2023)Sentiment Analysis of National Eligibility-Cum Entrance Test on Twitter Data Using Machine Learning TechniquesIoT, Cloud and Data Science10.4028/p-8fy5ca(344-354)Online publication date: 27-Feb-2023
  • (2023)Interactive Governmental Communication Promoting Participatory Citizen Engagement in Health Crisis Events - Evidence from IndiaPublic Administration Quarterly10.37808/paq.47.1.347:1(51-86)Online publication date: 1-Mar-2023
  • (2023)E-Government as a Key to the Economic Prosperity and Sustainable Development in the Post-COVID EraEconomies10.3390/economies1104011211:4(112)Online publication date: 6-Apr-2023
  • (2023)Heterogeneous Diffusion of Government Microblogs and Public Agenda Networks during Public Policy Communication in ChinaEntropy10.3390/e2504064025:4(640)Online publication date: 11-Apr-2023
  • (2023)Assessing causality among topics and sentiments: The case of the G20 discussion on TwitterJournal of Information Science10.1177/01655515231160034Online publication date: 30-Mar-2023
  • (2022)Estimating the Best Time to View Cherry Blossoms Using Time-Series Forecasting MethodMachine Learning and Knowledge Extraction10.3390/make40200184:2(418-431)Online publication date: 30-Apr-2022
  • (2022)Network Media Public Opinion and Social Governance Supported by the Internet-of-Things Big DataSecurity and Communication Networks10.1155/2022/24598152022Online publication date: 1-Jan-2022
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