IParasitology: Mining the Internet to Test Parasitological Hypotheses. Issue 4 (April 2021)

• Record Type: Journal Article
• Authors: Poulin, Robert; Bennett, Jerusha; Filion, Antoine; Bhattarai, Upendra Raj; Chai, Xuhong; de Angeli Dutra, Daniela; Donlon, Erica; Doherty, Jean-François; Jorge, Fátima; Milotic, Marin; Park, Eunji; Sabadel, Amandine; Thomas, Leighton J.
• Is Part Of: Trends in parasitology. Volume 37:Issue 4(2021)
• Page Start: 267
• Page End: 272
• Publication Date: 2021-04
• Subjects: citizen science -- crowdsourcing -- digital data -- Google searches -- machine learning -- social media; Parasitology -- Periodicals; Parasitology -- Periodicals; Biology -- Periodicals; Parasitology; Biology; Parasitologie -- Périodiques; Online resources; 571.999
• DOI: 10.1016/j.pt.2021.01.003
• Languages: English
• ISSNs: 1471-4922

Abstract

Abstract : Digital data (internet queries, page views, social media posts, images) are accumulating online at increasing rates. Tools for compiling these data and extracting their metadata are now readily available. We highlight the possibilities and limitations of internet data to reveal patterns in host–parasite interactions and encourage parasitologists to embrace iParasitology.