Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free”
Gathering information from social networks such as Twitter has emerged to obtain spontaneous and direct opinions of users about a topic. This study focuses on using co-occurrence networks to analyse Twitter information. The objectives were to study the impact of text pre-treatment (codification based in qualitative analysis or just pre-cleaning) and to apply co-occurrence networks for analysing what is said on Twitter about specific topics like “gluten-free”. As such, 16,386 tweets in Spanish containing terms “sin-gluten” and “gluten-free” were collected. A subset of 3,000 tweets was used to make co-occurrence networks two ways: i) from the manually coded text and ii) from pre-cleaned text. Results indicate that the co-occurrence network from pre-cleaned text provides meaningful information showing structure and relevance for terms like the network from coded text. The whole set of tweets was used to explore Twitter information about gluten-free, showing users share information about products, occasions, social situations, and places but also to product characteristics, sensations, and diet or health issues related to the products. Five product categories, critical for the lack of gluten (bread, cake, cookie, beer, and pizza) occupied most tweets, and according to the related terms were intended to recommend how to get (buying or cooking) these gluten-free products and to exhibit what (how, when, and where) they prepare and eat. These aspects were different among products and separated co-occurrence networks allowed better identification.
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Format: | artículo biblioteca |
Language: | English |
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Elsevier
2020-06-11
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Subjects: | Gluten-free, Co-occurrence networks, Social media, Consumers, Twitter, |
Online Access: | http://hdl.handle.net/10261/214443 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100003359 |
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dig-iata-es-10261-2144432021-06-11T04:30:41Z Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” Puerta, Patricia Laguna, Laura Vidal, Leticia Ares, Gastón Fiszman, Susana Tárrega, Amparo Ministerio de Economía y Competitividad (España) Generalitat Valenciana Gluten-free Co-occurrence networks Social media Consumers Twitter Gathering information from social networks such as Twitter has emerged to obtain spontaneous and direct opinions of users about a topic. This study focuses on using co-occurrence networks to analyse Twitter information. The objectives were to study the impact of text pre-treatment (codification based in qualitative analysis or just pre-cleaning) and to apply co-occurrence networks for analysing what is said on Twitter about specific topics like “gluten-free”. As such, 16,386 tweets in Spanish containing terms “sin-gluten” and “gluten-free” were collected. A subset of 3,000 tweets was used to make co-occurrence networks two ways: i) from the manually coded text and ii) from pre-cleaned text. Results indicate that the co-occurrence network from pre-cleaned text provides meaningful information showing structure and relevance for terms like the network from coded text. The whole set of tweets was used to explore Twitter information about gluten-free, showing users share information about products, occasions, social situations, and places but also to product characteristics, sensations, and diet or health issues related to the products. Five product categories, critical for the lack of gluten (bread, cake, cookie, beer, and pizza) occupied most tweets, and according to the related terms were intended to recommend how to get (buying or cooking) these gluten-free products and to exhibit what (how, when, and where) they prepare and eat. These aspects were different among products and separated co-occurrence networks allowed better identification. Authors are grateful to the Spanish Ministry of the Economy and Competitiveness for financial support (project AGL-2016-75403-R) and for the Juan de la Cierva contract for author Laura Laguna (IJCI-2016-27427). Furthermore, to Generalitat Valenciana (Project Prometeo 2017/189). Peer reviewed 2020-06-15T18:08:00Z 2020-06-15T18:08:00Z 2020-06-11 artículo http://purl.org/coar/resource_type/c_6501 Food Quality and Preference 86: 103993 (2020) 0950-3293 http://hdl.handle.net/10261/214443 10.1016/j.foodqual.2020.103993 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100003359 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2016-75403-R Postprint https://doi.org/10.1016/j.foodqual.2020.103993 Sí open Elsevier |
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Gluten-free Co-occurrence networks Social media Consumers Gluten-free Co-occurrence networks Social media Consumers |
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Gluten-free Co-occurrence networks Social media Consumers Gluten-free Co-occurrence networks Social media Consumers Puerta, Patricia Laguna, Laura Vidal, Leticia Ares, Gastón Fiszman, Susana Tárrega, Amparo Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” |
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Gathering information from social networks such as Twitter has emerged to obtain spontaneous and direct opinions of users about a topic. This study focuses on using co-occurrence networks to analyse Twitter information. The objectives were to study the impact of text pre-treatment (codification based in qualitative analysis or just pre-cleaning) and to apply co-occurrence networks for analysing what is said on Twitter about specific topics like “gluten-free”. As such, 16,386 tweets in Spanish containing terms “sin-gluten” and “gluten-free” were collected. A subset of 3,000 tweets was used to make co-occurrence networks two ways: i) from the manually coded text and ii) from pre-cleaned text. Results indicate that the co-occurrence network from pre-cleaned text provides meaningful information showing structure and relevance for terms like the network from coded text. The whole set of tweets was used to explore Twitter information about gluten-free, showing users share information about products, occasions, social situations, and places but also to product characteristics, sensations, and diet or health issues related to the products. Five product categories, critical for the lack of gluten (bread, cake, cookie, beer, and pizza) occupied most tweets, and according to the related terms were intended to recommend how to get (buying or cooking) these gluten-free products and to exhibit what (how, when, and where) they prepare and eat. These aspects were different among products and separated co-occurrence networks allowed better identification. |
author2 |
Ministerio de Economía y Competitividad (España) |
author_facet |
Ministerio de Economía y Competitividad (España) Puerta, Patricia Laguna, Laura Vidal, Leticia Ares, Gastón Fiszman, Susana Tárrega, Amparo |
format |
artículo |
topic_facet |
Gluten-free Co-occurrence networks Social media Consumers |
author |
Puerta, Patricia Laguna, Laura Vidal, Leticia Ares, Gastón Fiszman, Susana Tárrega, Amparo |
author_sort |
Puerta, Patricia |
title |
Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” |
title_short |
Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” |
title_full |
Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” |
title_fullStr |
Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” |
title_full_unstemmed |
Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free” |
title_sort |
co-occurrence networks of twitter content after manual or automatic processing. a case- study on “gluten-free” |
publisher |
Elsevier |
publishDate |
2020-06-11 |
url |
http://hdl.handle.net/10261/214443 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100003359 |
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