Measuring climate change on Twitter using Google’s algorithm: perception and events

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -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.

#Climatechange vs. #Globalwarming: Characterizing Two Competing Climate Discourses on Twitter with Semantic Network and Temporal Analyses

“…Studies have suggested that the public shows a preference for scientific publications with general keywords compared with those with complicated scientific jargon [47], lacking a deep understanding of the complicated issue [46] and the necessity for mitigation of the climate issue [47]. These conclusions seem to suit global warming more than climate change according to the current study, which is probably because climate change receives more publicity and recognition than global warming in the scientific community.…”

“…Therefore, semantic network analysis, a methodology based on cognitive science, could help researchers infer the hidden cognitive network structure in the human mind [44] based on the analysis of texts, as the frequency, semantic distance, and co-occurrence pattern of words can accurately reflect their meaning and links [45]. Several researchers have attempted to build a semantic network of certain climate events in social media discourse and scientific publications [46][47][48]. Research about public perceptions of global climate change in Qatar confirmed that the observed semantic network of climate concepts is not random and represents as meaningful clusters [49].…”

Social network analysis of climate change discussion on Twitter during COP21

“…The hashtag "#climatechange" was selected because research has shown that awareness of climate change on Twitter is chiefly expressed using this hashtag (Hamed, et al 2015).…”

Fighting the COVID-19 Infodemic in New articles and False Publications: NeoNet, a Text-based Supervised Machine Learning Algorithm

“…Bigrams, as network construction means, are widely used in various computational problems 48 . We present an incremental network construction approach that is wellknown in the literature in prominent algorithms (e.g., Prim's algorithm 49,50 ) which starts with an empty set of nodes and incrementally adds new nodes, one node at a time.…”