Information extraction using a mixed method analysis of social media data

A case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India

Bibliographic Data

ID21501030
AuthorsDhivya Karmegam (0000-0003-3307-8704, SRM Institute of Science and Technology, corresponding author), Bagavandas Mappillairaju (0000-0003-4794-6250, SRM Institute of Science and Technology)
Year2023
Volume39
Issue4
Pages739-749
Publication date2023-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInformation Development (JOURNAL)
Journal identifiersISSN: 0266-6669 • E-ISSN: 1741-6469
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/02666669211049153
OpenAlexW3209622674
LanguageEN
References cited46

During unexpected social events, information extracted from social media content posted by the people could play a crucial role in understanding the public opinion about the event. In this study, a mixed method procedure, which combines automated and human-based methods, is proposed to mine information from tweets to understand people's thoughts toward an unexpected turn of events. The proposed framework was applied on tweets posted regarding the police shooting to disperse protesters during the anti-Sterlite protests on May 22, 2018, at Thoothukudi in the Indian state of Tamil Nadu. The tweets were analyzed in two ways: (i) sentiment classification with automated computational methods and (ii) qualitatively examining the context of the expressed sentiments. In the case of anti-Sterlite protests, people expressed mixed emotions toward the protests for the closure of the Sterlite plant. A large negative sentiment toward the police shooting could be gleaned from the tweets. Analyzing tweets by the proposed method provides clear insights regarding the incident, which in turn will aid in planning an emergency response

Algorithm · Political science · Politics · Public opinion · Public relations · Sentiment analysis · Social media · Sociology · Tamil · World Wide Web · Advanced Text Analysis Techniques · Computer Science · History · Law · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Artificial Intelligence

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