Automated feature extraction from social media for systematic lead user identification

Pajo, Sanjin; Vandevenne, Dennis; Duflou, Joost R.

doi:10.1080/09537325.2016.1220517

Cited by 17 publications

(29 citation statements)

References 42 publications

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“…Like P6, the researcher conducting the netnographic study of “MM” does not have a team of trained experts—in fact, the research is conducted only by one person, who is familiar with netnographic procedures through scholarly studies. The analysis shows that both have succeeded in conducting such a method, therefore, it is believed that Pajo et al’s (2017) suggestion of a trained team of experts, is not crucial to the success of the research. Having experience with netnographic research over time can only enhance a fashion designer’s product offering, branding, and overall marketing strategy.…”

Section: Data Analysis and Findingsmentioning

confidence: 98%

“…The P6 team also conducts a few other research methods; thus, it is not solely focused on netnography and does not have trained experts in the method. This fashion designer regards the team as sufficient for the process, although Pajo, Vandevenne, and Duflou (2017) noted that a team would require trained experts to perform a successful analysis. Like P6, the researcher conducting the netnographic study of “MM” does not have a team of trained experts—in fact, the research is conducted only by one person, who is familiar with netnographic procedures through scholarly studies.…”

Section: Data Analysis and Findingsmentioning

confidence: 99%

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Netnography as a marketing research tool in the fashion industry in Southeast Europe

Xharavina

Kapoulas²,

Miaoulis³

2019

International Journal of Market Research

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In the past decades, marketing has been revolutionized by digital sources, which provide marketers with rich information on potential consumers. Consequently, this article explores the evolving opportunities that online communities present to marketers in collecting consumer insights. It advances Southeast Europe’s marketing researchers’ understanding of netnography by introducing them to its concept, procedures, and implications. This study triangulates the data through utilizing seven one-to-one in-depth interviews with fashion designers, employing two focus groups with fashion consumers who actively congregate in online communities, and through conducting netnography on a fashion-related online community. This article demonstrates netnography practices, and experiences with the goal of having fashion designers and marketers understand its potential as an efficient method for providing effective qualitative market intelligence. It shows that netnography is a relatively easy, cost-effective and time-efficient approach, and it supports brand development through achieving a better understanding of consumer perceptions. Overall, netnography has great potential as a marketing research tool. Online fashion community members’ views support it as most of them prefer to participate in netnographic research. Nevertheless, the majority of fashion designers in Southeast Europe are not fully aware of the method and its exact procedures and, hence, avoid using it.

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Section: Data Analysis and Findingsmentioning

confidence: 98%

Section: Data Analysis and Findingsmentioning

confidence: 99%

Netnography as a marketing research tool in the fashion industry in Southeast Europe

Xharavina

Kapoulas²,

Miaoulis³

2019

International Journal of Market Research

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“…For instance, Pajo etal. developed a Fast Lead User Identification (FLUID) approach with features extracted from activity measures, centrality measures and sentiment [ 27 ]. Fan et al.…”

Section: Related Workmentioning

confidence: 99%

Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration

Zhuang

2021

Heliyon

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Online Social networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify influencers is thus very significant, allowing us to control the outbreak of public negative opinion, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The identification of influencers attracts increasing attentions from both computer science and communication science, with multiple dimensional metrics ranging from structure-based to information-based and action-based. However, most work simply rely on one dimensional metrics. Therefore, in this paper, we analyze three dimensional characteristics (structure-based, information-based, and action-based factors) to develop the multidimensional social influence (MSI) measurement approach. With topic distillation and conditional expectation, the MSI approach can not only measure users topic-level influence, but also measure users global-level influence. Based on data collected from SinaWeibo.com, the experimental results show that the proposed framework outperforms two traditional methods (LeaderRank and FBI) both on the topic-level and the global-level. The proposed framework can be effectively applied to promote word-of-mouth marketing, and to steer public opinion in certain directions, even to support decisions during a negotiation process.

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“…For this cause, n-gram models did not have a major effect on social media linguistic theory, where part of the explicit objective is to model these dependencies. (3) The main advantage of PMI method is that by using this method one can estimate whether the two items in the social media is having a genuine association or not. (4) LSA is easy to understand, implement and use.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The whole pattern mining process can be considered as three main serial of data pre-processing, feature extraction and classification. 3 The data pre-processing aids decrease of noise, filtering, data standardization, etc. The feature extraction and selection stage characterize the data, resulting in a feature vector utilized to predict or detect data by clustering or classification.…”

Section: Introductionmentioning

confidence: 99%

Pattern Mining Approaches Used in Social Media Data

Chaki

Dey²,

Panigrahi

et al. 2020

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

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Automated feature extraction from social media for systematic lead user identification

Cited by 17 publications

References 42 publications

Netnography as a marketing research tool in the fashion industry in Southeast Europe

Netnography as a marketing research tool in the fashion industry in Southeast Europe

Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration

Pattern Mining Approaches Used in Social Media Data

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