Advanced visualization of Twitter data for its analysis as a communication channel in traditional companies

Zarco, Carmen; Santos, Elena; Cordón, Óscar

doi:10.1007/s13748-019-00181-3

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…areas with positive attitude) to launch their new products based on social media data. Furthermore, this spatial analysis of online comments can provide valuable information to design tailored marketing strategies for their products, taking into account not only the perceptions of its target communities but also their distribution and spatial heterogeneity, revealed here to be an important factor to consider (Zarco et al, 2019). Data from microblogging platforms are easily accessible and represent consumers' perceptions regarding specific products or brands in real time, thus offering a good source of information to benchmark against firms' offers, prices, profits, sales and contextual information (Mostafa, 2018).…”

Section: Discussionmentioning

confidence: 99%

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Pindado¹,

Barrena

2020

BFJ

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PurposeThis paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.Design/methodology/approachA density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.FindingsTwitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.Research limitations/implicationsThe social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.Practical implicationsThe approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.Originality/valueThis study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

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Section: Discussionmentioning

confidence: 99%

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Pindado¹,

Barrena

2020

BFJ

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“…For example, (Jun & Park, 2017) considered them to analyze brand positioning by establishing relationships among brands as well as among brand and product attributes based on the structure of online web searchers developed by the users. Besides, (Zarco, et al, 2019) proposes using these kinds of network-based visual representations to identify the communication model followed by Spanish wineries in Twitter. In our case, the analysis of the bipartite social networks as well as their projections on the brands networks, on the one hand, and the hashtag networks, on the other hand, allows us to identify the communication models, uncover common and different patterns, and determine the most and less active actors in the complex systems.…”

Section: Methodsmentioning

confidence: 99%

“…This element is the hashtag, based on describing contents, pictures and videos using a short text preceded by the # symbol (Hu, et al, 2014). Twitter is recognized for being a long-known platform characterized by its immediacy (Zarco, et al, 2019). Meanwhile, Instagram is a relatively young social network allowing to share content through photos and retouching them with filters.…”

Section: Brands and Social Mediamentioning

confidence: 99%

Analyzing the communication in social media of the main sustainable brands during COVID-19 crisis: the Spanish vs. Italian cases

Zarco

Cordón

2020

Preprint

Self Cite

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Communication is now a valuable element to demonstrate the degree of commitment and transparency that organizations have towards Sustainability. In a critical moment such that experienced with the COVID-19 crisis, companies recognized facing the challenge of continuous and committed communication on social networks as a sustainable activity. All the latter raises the question whether the brands recognized as the most sustainable have actually succeed on being aware of communicating at the most critical moments of the COVID-19 crisis. In this context, an analysis is made of the activity maintained by the main sustainable companies, both Spanish and Italian, within two of the most popular social networks, Twitter and Instagram, at the very early stages of the crisis in both countries. Our results show that most companies have managed to rise to the occasion and show their commitment to the population through social networks.

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“…On the other hand, Bliss (Bliss, 2014) uses a statistical approach for link prediction by periodically sampling the datasets and forming variance matrix and proximity matrix after extracting topological characteristics of nodes. Likewise, Zarco et al (Zarco, Santos, & Cordón, 2019) apply visualization methods based on social network analysis techniques to get visual representations of the similarity relations of different companies on Twitter. It is worth mentioning that most of the existing similarity based works lose rich information available for link prediction by just considering the connections between the target nodes and their common neighbors.…”

Section: Introductionmentioning

confidence: 99%

Improving link prediction in social networks using local and global features: a clustering-based approach

Ghasemi

Zarei

2021

Prog Artif Intell

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Link prediction problem has increasingly become prominent in many domains such as social network analyses, bioinformatics experiments, transportation networks, criminal investigations and so forth. A variety of techniques has been developed for link prediction problem, categorized into 1) similarity based approaches which study a set of features to extract similar nodes; 2) learning based approaches which extract patterns from the input data; 3) probabilistic statistical approaches which optimize a set of parameters to establish a model which can best compute formation probability. However, existing literatures lack approaches which utilize strength of each approach by integrating them to achieve a much more productive one. To tackle the link prediction problem, we propose an approach based on the combination of first and second group methods; the existing studied works use just one of these categories. Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes, which enforce the approach more efficiency compared to approaches using mere measures. Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures which differentiate the strength of clusters; basically, the usage of local and global indices and the clustering information plays an imperative role in our link prediction process. Some extensive experiments held on real datasets including Facebook, Brightkite and HepTh indicate good performances of our proposal method. Besides, we have experimentally verified our approach with some previous techniques in the area to prove the supremacy of ours.

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Advanced visualization of Twitter data for its analysis as a communication channel in traditional companies

Cited by 5 publications

References 36 publications

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Analyzing the communication in social media of the main sustainable brands during COVID-19 crisis: the Spanish vs. Italian cases

Improving link prediction in social networks using local and global features: a clustering-based approach

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