Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan
In the light of growing frequency of natural disasters, social networking sites are now used for polling perception and evaluating governmental performance. The aim of this study is to examine the effects of negative and positive social media discussions to government responses with the floods disas...
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| Language: | English |
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REA Press
2025-03-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_209885_223e20ced4bf4ee9a2b9ac43eac623f8.pdf |
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| author | Fazal Tariq Muhammad Tufail Taj Rehman |
| author_facet | Fazal Tariq Muhammad Tufail Taj Rehman |
| author_sort | Fazal Tariq |
| collection | DOAJ |
| description | In the light of growing frequency of natural disasters, social networking sites are now used for polling perception and evaluating governmental performance. The aim of this study is to examine the effects of negative and positive social media discussions to government responses with the floods disaster of 2010 in Pakistan. This study, being a sentiment analysis of tweets that involves the Pakistan Flood 2010 and Disaster Relief hashtags only, classified public responses as positive, negative, or neutral. The sentiments are transformed into actionable insights using the Enhanced Hybrid Dark Social Analytical Framework (EHDSAF) technique across different areas. The study advances knowledge of how public sentiment shapes government responses by showing that negativity correlates with slower response and revised policies. The majority of the tweets analyzed were neutral (45%), followed by positive (35%), and negative (20%). Negative sentiment tends to be concentrated during the peak crisis period. Higher negative sentiment, particularly in big cities correlates with more immediate and substantial government interventions, indicated by a strong correlation of 0.65. The Pearson correlation coefficient calculated as 0.68, suggests a strong relationship between public sentiment and response. The study therefore establishes social media as an accountability forum that provides real-time feedback to government agencies in the course of calamity management. This paper highlights the effectiveness of using sentiment analysis to update the approach by which disasters are responded to, as well as improve the perception of the public towards government endeavors. |
| format | Article |
| id | doaj-art-1b33365df4d84aa5ad269010725e5f3b |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-1b33365df4d84aa5ad269010725e5f3b2025-01-30T12:23:51ZengREA PressBig Data and Computing Visions2783-49562821-014X2025-03-0151112310.22105/bdcv.2024.488065.1217209885Analyzing the impact of social media sentiments on government response during natural disasters in PakistanFazal Tariq0Muhammad Tufail1Taj Rehman2Department of Computer Science, Govt. Post Graduate College, Nowshera, Pakistan.Department of Computer Science, Govt. Post Graduate College, Nowshera, Pakistan.Qutaba University of Science and Technology, Peshawer, Pakistan.In the light of growing frequency of natural disasters, social networking sites are now used for polling perception and evaluating governmental performance. The aim of this study is to examine the effects of negative and positive social media discussions to government responses with the floods disaster of 2010 in Pakistan. This study, being a sentiment analysis of tweets that involves the Pakistan Flood 2010 and Disaster Relief hashtags only, classified public responses as positive, negative, or neutral. The sentiments are transformed into actionable insights using the Enhanced Hybrid Dark Social Analytical Framework (EHDSAF) technique across different areas. The study advances knowledge of how public sentiment shapes government responses by showing that negativity correlates with slower response and revised policies. The majority of the tweets analyzed were neutral (45%), followed by positive (35%), and negative (20%). Negative sentiment tends to be concentrated during the peak crisis period. Higher negative sentiment, particularly in big cities correlates with more immediate and substantial government interventions, indicated by a strong correlation of 0.65. The Pearson correlation coefficient calculated as 0.68, suggests a strong relationship between public sentiment and response. The study therefore establishes social media as an accountability forum that provides real-time feedback to government agencies in the course of calamity management. This paper highlights the effectiveness of using sentiment analysis to update the approach by which disasters are responded to, as well as improve the perception of the public towards government endeavors.https://www.bidacv.com/article_209885_223e20ced4bf4ee9a2b9ac43eac623f8.pdfehdsaftwitterdark social dataapisentiment analysisgopmachine learningvader |
| spellingShingle | Fazal Tariq Muhammad Tufail Taj Rehman Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan Big Data and Computing Visions ehdsaf dark social data api sentiment analysis gop machine learning vader |
| title | Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan |
| title_full | Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan |
| title_fullStr | Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan |
| title_full_unstemmed | Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan |
| title_short | Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan |
| title_sort | analyzing the impact of social media sentiments on government response during natural disasters in pakistan |
| topic | ehdsaf dark social data api sentiment analysis gop machine learning vader |
| url | https://www.bidacv.com/article_209885_223e20ced4bf4ee9a2b9ac43eac623f8.pdf |
| work_keys_str_mv | AT fazaltariq analyzingtheimpactofsocialmediasentimentsongovernmentresponseduringnaturaldisastersinpakistan AT muhammadtufail analyzingtheimpactofsocialmediasentimentsongovernmentresponseduringnaturaldisastersinpakistan AT tajrehman analyzingtheimpactofsocialmediasentimentsongovernmentresponseduringnaturaldisastersinpakistan |