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http://dspace.dtu.ac.in:8080/jspui/handle/repository/18217Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | KUSHWAHA, PRIYANKA | - |
| dc.date.accessioned | 2021-03-02T05:41:32Z | - |
| dc.date.available | 2021-03-02T05:41:32Z | - |
| dc.date.issued | 2020-07 | - |
| dc.identifier.uri | http://dspace.dtu.ac.in:8080/jspui/handle/repository/18217 | - |
| dc.description.sponsorship | Twitter is being used as the mass medium by many people to express opinions, post clarifications, share information, advertise, complain, provide feedback, and reporting. Social media have witnessed an explosive growth of malicious and deceptive information. Studies have confirmed that misleading information diffuses significantly farther, faster, deeper, and more broadly than factual information in all categories of information. Thus, it has become important to detect misinformation at hteir early stages before it spreads online, thus avoiding risk, damage, errors, hoaxes, and other falsehoods. Twitter can also be used to aid in managing the communication during any operational aspects of large events of national or international importance such as assembly and general elections, Kumbh Mela, Common Wealth Games and epidemic situations like COVID-19. Using the case of malfunctioning of electronic voting machines (EVMs) in the general elections of India 2019, we propose a data science framework that can be used to identify genuine and non-genuine tweet. This study also helps organizations/management to prioritize only genuine tweets during an operational crisis occurring in large events and address the no-genuine tweets to avoid any fake information propagation. To develop this approach, we integrated results and insights from feature engineering, text mining, sentiment analysis, and data mining to detect genuine and non-genuine information using Twitter data. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TD-5118; | - |
| dc.subject | SOCIAL MEDIA | en_US |
| dc.subject | EVM GLITCHES | en_US |
| dc.subject | INFIRMATION | en_US |
| dc.subject | en_US | |
| dc.title | IDENTIFYING (MIS) INFORMATION FROM SOCIAL MEDIA IN OPERATIONAL CRISIS SITUATIONS : A CASE OF EVM GLITCHES IN INDIA | en_US |
| dc.type | Thesis | en_US |
| Appears in Collections: | MBA | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| MBA Priyanka Kushwaha pdf.pdf | 1.04 MB | Adobe PDF | View/Open |
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