Do Anti-Immigrant Laws Shape Public Sentiment? A Study of Arizona’s SB 1070 Using Twitter Data

2017 ◽  
Vol 123 (2) ◽  
pp. 333-384 ◽  
Author(s):  
René D. Flores
Keyword(s):  
Sb 1070 ◽  
Public Sentiment ◽  
Twitter Data

scholarly journals Saving Lives and Changing Minds with Twitter in Disasters and Pandemics: A Literature Review

2020 ◽  
Vol 1 (1) ◽  
pp. 59-77
Author(s):  
Hamed Seddighi ◽  
Ibrahim Salmani ◽  
Saeideh Seddighi
Keyword(s):  
Natural Hazards ◽  
Selection Criteria ◽  
Literature Search ◽  
Aid Workers ◽  
Inclusion Criteria ◽  
Building Trust ◽  
Public Sentiment ◽  
Twitter Data ◽  
Comprehensive Literature Search

Twitter is a major tool for communication during emergencies and disasters. This study aimed to investigate Twitter use during natural hazards and pandemics. The included studies reported the role of Twitter in disasters triggered by natural hazards. Electronic databases were used for a comprehensive literature search to identify the records that match the mentioned inclusion criteria published through May 2020. Forty-five articles met the selection criteria and were included in the review. These indicated ten functions of Twitter in disasters, including early warning, dissemination of information, advocacy, assessment, risk communication, public sentiment, geographical analysis, charity, collaboration with influencers and building trust. Preventing the spread of misinformation is one of the most important issues in times of disaster, especially pandemics. Sharing accurate, transparent and prompt information from emergency organizations and governments can help. Moreover, analyzing Twitter data can be a good way to understand the mental state of the community, estimate the number of injured people, estimate the points affected by disasters and model the prevalence of epidemics. Therefore, various groups such as politicians, government, nongovernmental organizations, aid workers and the health system can use this information to plan and implement interventions.

scholarly journals Causal Modeling of Twitter Activity during COVID-19

Computation ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 85 ◽  
Author(s):  
Oguzhan Gencoglu ◽  
Mathias Gruber
Keyword(s):  
Domain Knowledge ◽  
Causal Modeling ◽  
Public Attention ◽  
Health Events ◽  
Public Sentiment ◽  
Twitter Data ◽  
Primary Responsibility ◽  
Twitter Activity ◽  
Adverse Health Events ◽  
Essential Prerequisite

Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.

scholarly journals Chennai Floods 2021: Sentiment Analysis of Twitter Data using Tweepy and TextBlob

2021 ◽  
Vol 9 (12) ◽  
pp. 785-789
Author(s):  
Raghav Tinnalur Swaminathan
Keyword(s):  
Sentiment Analysis ◽  
Data Extraction ◽  
Review System ◽  
The Public ◽  
Public Sentiment ◽  
Twitter Data

Abstract: The rise in the usage of Twitter for the exclamation of the problems worldwide and also as a ‘review system,’ where the customers can directly hold an entity responsible in front of the public by tweeting and tagging them, gives them immense power and counts towards being an advantage for researchers to analyze such data that can be scraped and used through APIs for a variety of purposes. Through this research, our motive is to analyze the 2021 Chennai floods with data sourced from twitter to understand the public sentiment during the 14-day span. The same is achieved with the help of Tweepy to authenticate data extraction from Twitter and TextBlob, for the classification of sentiment tags - positive, negative, and neutral. The result of this study focuses on the visualization of our findings, with various charts and metrics indicating the sentiment of the tweets we have scraped and analyzed. Keywords: Sentiment Analysis, WordCloud, Subjectivity, Polarity, Chennai Floods

scholarly journals Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Abu Kausar ◽  
Arockiasamy Soosaimanickam ◽  
Mohammad Nasar
Keyword(s):  
Sentiment Analysis ◽  
Public Sentiment ◽  
Twitter Data
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