Measuring climate change on Twitter using Google's algorithm: perception and events. Issue 4 (16th November 2015)

• Record Type: Journal Article
• Authors: Hamed, Ahmed Abdeen; Ayer, Alexa A.; Clark, Eric M.; Irons, Erin A.; Taylor, Grant T.; Zia, Asim
• Is Part Of: International journal of web information systems. Volume 11:Issue 4(2015)
• Page Start: 527
• Page End: 544
• Publication Date: 2015-11-16
• Subjects: Web mining -- Applications of Web mining and searching -- Web structure/linkage mining -- Web design metrics -- Web media; World Wide Web -- Periodicals; Internet -- Periodicals; Information storage and retrieval systems -- Periodicals; 004.678
• DOI: 10.1108/IJWIS-08-2015-0025
• Languages: English
• ISSNs: 1744-0084

Abstract

Abstract : Purpose: – The purpose of this paper is to test the hypothesis of whether more complex and emergent hashtags can be sufficient pointers to climate change events. Human-induced climate change is one of this century's greatest unbalancing forces to have affected our planet. Capturing the public awareness of climate change on Twitter has proven to be significant. In a previous research, it was demonstrated by the authors that public awareness is prominently expressed in the form of hashtags that uses more than one bigram (i.e. a climate change term). The research finding showed that this awareness is expressed by more complex terms (e.g. "climate change"). It was learned that the awareness was dominantly expressed using the hashtag: #ClimateChange. Design/methodology/approach: – The methods demonstrated here use objective computational approaches [i.e. Google's ranking algorithm and Information Retrieval measures (e.g. TFIDF)] to detect and rank the emerging events. Findings: – The results shows a clear significant evidence for the events signaled using emergent hashtags and how globally influential they are. The research detected the Earth Day, 2015, which was signaled using the hashtag #EarthDay. Clearly, this is a day that is globally observed by the worldwide population. Originality/value: – It was proven that these computational methods eliminate the subjectivity errors associated with humans and provide inexpensive solution for event detection on Twitter. Indeed, the approach used here can also be applicable to other types of event detections, beyond climate change, and surely applicable to other social media platforms that support the use of hashtags (e.g. Facebook). The paper explains, in great detail, the methods and all the numerous events detected.