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dc.contributor.author
Gallo, Fabio Rafael  
dc.contributor.author
Simari, Gerardo  
dc.contributor.author
Martinez, Maria Vanina  
dc.contributor.author
Falappa, Marcelo Alejandro  
dc.date.available
2020-11-12T14:52:06Z  
dc.date.issued
2020-09-01  
dc.identifier.citation
Gallo, Fabio Rafael; Simari, Gerardo; Martinez, Maria Vanina; Falappa, Marcelo Alejandro; Predicting user reactions to Twitter feed content based on personality type and social cues; Elsevier Science; Future Generation Computer Systems; 110; 1-9-2020; 918-930  
dc.identifier.issn
0167-739X  
dc.identifier.uri
http://hdl.handle.net/11336/118257  
dc.description.abstract
The events in the past few years clearly indicate that the modern social, political and economical landscapes are heavily influenced by how information flows through social networks. For instance, the recent outcomes of the US presidential elections and the Brexit vote show that misinformation and otherwise influencing content can affect events of great importance. In this paper, we adopt a simplified version of the recently proposed Network Knowledge Base (NKB) model to tackle the problem of predicting basic actions that a user can take given the content of their social media feeds: either take action (by reusing content seen in their feeds or creating new one), or otherwise take no action. We propose processing raw data obtained from social media based on the framework defined by the NKB model, and then formulate an action/no action prediction task that takes as input five features (including the user's personality type and other social cues), and then go on to show—via an extensive empirical evaluation with real-world Twitter data—that machine learning classification algorithms can be successfully applied in this setting to make predictions about user reactions. The main result obtained is that, out of the features considered, personality type based on the Big-5 (also known as OCEAN) model is the most impactful; furthermore, though the rest of the features taken individually do not have a significant impact, the best results are obtained when they are all taken together. This is a first step in applying the NKB model towards understanding the effect of pathogenic social media phenomena such as fake news, how they spread via cascades, and how to counteract their ill effects.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
HYBRID ARTIFICIAL INTELLIGENCE MODELS  
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SOCIAL KNOWLEDGE BASES  
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SOCIAL NETWORKS  
dc.subject.classification
Ciencias de la Computación  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Predicting user reactions to Twitter feed content based on personality type and social cues  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-09-02T19:05:53Z  
dc.journal.volume
110  
dc.journal.pagination
918-930  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Gallo, Fabio Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina  
dc.description.fil
Fil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Arizona State University; Estados Unidos  
dc.description.fil
Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina  
dc.description.fil
Fil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina  
dc.journal.title
Future Generation Computer Systems  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0167739X19304091  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.future.2019.10.044