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Co-occurrence networks of Twitter content after manual or automatic processing. A case- study on “gluten-free”
Food Quality and Preference ( IF 5.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.foodqual.2020.103993
Patricia Puerta , Laura Laguna , Leticia Vidal , Gastón Ares , Susana Fiszman , Amparo Tárrega

Abstract 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 3000 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 on gluten-free, showing users share information about products, occasions, social situations, and places but also 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.

中文翻译:

手动或自动处理后 Twitter 内容的共现网络。“无麸质”案例研究

摘要 从社交网络(如 Twitter)收集信息已经出现,以获取用户对某个主题的自发和直接意见。本研究侧重于使用共现网络来分析 Twitter 信息。目标是研究文本预处理(基于定性分析或仅预清理的编码)的影响,并应用共现网络来分析 Twitter 上关于“无麸质”等特定主题的言论。因此,收集了 16,386 条包含“sin-gluten”和“gluten-free”术语的西班牙语推文。使用 3000 条推文的子集以两种方式构建共现网络:i) 来自手动编码的文本和 ii) 来自预清理的文本。结果表明,来自预清理文本的共现网络提供了有意义的信息,显示了诸如来自编码文本的网络之类的术语的结构和相关性。整套推文用于探索有关无麸质的 Twitter 信息,显示用户分享有关产品、场合、社交场合和地点的信息,以及与产品相关的产品特性、感觉以及饮食或健康问题。五个对缺乏麸质至关重要的产品类别(面包、蛋糕、饼干、啤酒和比萨饼)占据了大多数推文,根据相关术语,旨在推荐如何获得(购买或烹饪)这些无麸质食品并展示他们准备和吃什么(如何、何时、何地)。这些方面因产品而异,
更新日期:2020-12-01
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