Reactions to science communication: discovering social network topics using word embeddings and semantic knowledge

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dc.creatorBernardo Cerqueirade Lima
dc.creatorRenata Maria Abrantes Baracho Porto
dc.creatorThomas Mandl
dc.creatorPatricia Baracho Porto
dc.date.accessioned2025-02-12T18:45:19Z
dc.date.accessioned2025-09-09T00:15:53Z
dc.date.available2025-02-12T18:45:19Z
dc.date.issued2023-09-22
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.description.sponsorshipOutra Agência
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1007/s13278-023-01125-5
dc.identifier.issn1869-5469
dc.identifier.urihttps://hdl.handle.net/1843/79993
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofSocial Network Analysis and Mining
dc.rightsAcesso Aberto
dc.subjectPandemia
dc.subjectAprendizado de Máquina
dc.subjectComunicação
dc.subjectComunicação na ciência
dc.subjectRedes sociais
dc.subject.otherTopic modeling
dc.subject.otherMachine-Learning
dc.subject.otherCommunication
dc.subject.otherCOVID 19
dc.subject.otherModelagem de Informação
dc.subject.otherInteligência Artificial
dc.titleReactions to science communication: discovering social network topics using word embeddings and semantic knowledge
dc.typeArtigo de periódico
local.citation.epage11
local.citation.spage1
local.citation.volume13
local.description.resumoSocial media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources. This study aims to devise a framework that can sift through large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information, and how their behavior toward science communication (e.g., through videos or texts) is related to their information-seeking behavior. To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators, or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data.
local.publisher.countryBrasil
local.publisher.departmentARQ - DEPARTAMENTO DE TEC ARQUITETURA E URBANISMO
local.publisher.initialsUFMG
local.url.externahttps://link.springer.com/content/pdf/10.1007/s13278-023-01125-5.pdf

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