Twitter Presence of Jet Airways-Deriving Customer Insights Using Netnography and Wordclouds

Ahuja, Vandana; Shakeel, Moonis

doi:10.1016/j.procs.2017.11.336

Cited by 29 publications

(29 citation statements)

References 11 publications

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“…After all the tweets were downloaded, some tweets were deleted due to repeated content, news, or retweets [15]. The images and multimedia files that were published along with the text of the tweet were excluded from the analysis [31]. Following Saura and Bennett [28], the sample of tweets was considered valid according to the following criteria: (i) active Twitter profiles, i.e., with activity during the three months after the use of the indicated hashtags; (ii) Twitter user profile with a profile photo and a cover photo; (iii) retweets from the same tweet using the indicated hashtags were removed, as such tweets were considered to be duplicate content; (iv) only public profiles and tweets in English using #Millennials, #Millennial and #MillennialGeneration were included and (v) tweets should have been at least 80 characters long with spaces and had to have the hashtag as indicated.…”

Section: Methodsmentioning

confidence: 99%

“…Of note, the analysis of comments made by users about other companies or users on the same SNS makes it possible to measure the total number of engagements. Nowadays, researchers are able to analyze users' motivations to produce UGC [30,31]. For instance, Ordun and Akun [11] and Rodden and Hritz [22] have focused their research in UGC created by millennials to deepen the knowledge of psychological traits, attitudes and motivations [32].…”

Section: Ugc Analysismentioning

confidence: 99%

See 1 more Smart Citation

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

Saura

Debasa

Reyes-Menéndez

2019

Journal of Open Innovation: Technology, Market, and Complexity

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The millennial generation plays a leading role in today’s connected world in which exists a confluence of numerous technologies and the internet in science, economy and innovation. This study aimed to identify the key factors that characterize the millennial generation within the online chatter on Twitter using an innovative approach. To this end, we analyzed the user generated content (UGC) in the social network (SNS) Twitter using a three-steps knowledge-based method for information management. In order to develop this method, we first used latent Dirichlet allocation (LDA), a state-of-the-art thematic modeling tool that works with Python, to analyze topics in our database. The data were collected by extracting tweets with the hashtag #Millennial, #Millennials and #MillennialGeneration on Twitter (n = 35,401 tweets). Secondly, sentiment analysis with a support vector machine (SVM) algorithm was also developed using machine-learning. Applying this method to the LDA results resulted in the categorization of the topics into those that contained negative, positive and neutral sentiments. Thirdly, in order to gather the final results, data text mining techniques were used. The negative factors that characterize the behavior of this generation are depression, loneliness and real-world relationship. The positive factors are body image, self-expression, travelers and digital life and the neutral factors are self-identity and anxiety. Practical implications can be used by public actors, companies or policy makers that are focused on the millennial generation as a target. The study has important theoretical applications as the topics discovered can be used to test quantitative models based on the findings and insights extracted from the UGC sample.

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Section: Methodsmentioning

confidence: 99%

Section: Ugc Analysismentioning

confidence: 99%

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

Saura

Debasa

Reyes-Menéndez

2019

Journal of Open Innovation: Technology, Market, and Complexity

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“…The sentiment analysis stage was performed using the lexicon-based sentiment analysis and classification, one of the most popular methods for measuring the polarity of sentiment of a collection of documents (Ahuja and Shakeel, 2017). In this particular case, we applied the Valence Aware Dictionary for Sentiment Reasoning (VADER), which is a rule-based model that is able to manage a wide variety of content generated in social media and compute its sentiment polarity.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…A word cloud is a visual representation of text that is developed based on the frequency of the usage of each word in the data collection. Word clouds are an increasingly popular method as they are very efficient in summarizing large amounts of data, in this case tweets from Twitter, and depict the ideologies behind a textual discourse [34,35]. In this research, the aim of the word cloud analysis was to understand the most relevant topics and concepts linked to the two keywords-"sustainable" and "sustainability"-in the conversations that take place on social media.…”

Section: Word Cloud Analysis and Segmentationmentioning

confidence: 99%

The Concept of Sustainability on Social Media: A Social Listening Approach

2020

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The concept of sustainability has gone far beyond the issues of the sustainable management of natural and environmental resources. Nowadays, sustainability is part of the social sciences in a different way. The aim of this research was dual. Firstly, we analyzed the different contexts and areas of knowledge where this concept is used in society by using social listening on Twitter, one of the most popular social networks today. The sentiments of these conversations were rated to assess whether the feelings and perceptions of these conversations on the social network were positive or negative regarding the use of the concept. Also, we tested if these perceptions about the topic were attuned to other more formal fields, such as scientific research, or strategies followed nationally or internationally by agencies and organizations related to sustainability. The method used on this first part of the research consisted of an analysis of 15,000 tweets collected from Twitter using natural language processing (NLP) for clustering the main areas of knowledge of topics where the concept of sustainability was used, and the sentiment of these conversations on the social network. Secondly, we mapped the social network of users who generated or spread content regarding sustainability on Twitter within the period of observation. Social network analysis (SNA) focuses on the taxonomy of the network and its dynamics and identifies the most relevant players in terms of generation of conversation and also their referrers who spread their messages worldwide. For this purpose, we used Gephi, an open source software used for network analysis and visualization, that allows for the exploration and visualization of large networks of any kind, in depth. The findings of this research are new, not only because of the mix of technology and methods used for extracting data from Twitter and analyzing them from different perspectives, but also because they show that social listening is a powerful method for analyzing relevant social phenomena. Listening on social networks can be used more effectively than other more traditional processes to gather data that are more costly and time consuming and lack the momentum and spontaneity of digital conversations.

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“…Building on the observation that 'the digital has become part of the material, sensory and social worlds we inhabit ' (Pink et al, 2016, p. 6), several social research methods are in fact transferable to digital contexts (Russo, 2019). Accordingly, the expanding field of online ethnography now includes research on written texts like tweets, webpages and Facebook posts (Ahuja & Shakeel, 2017;Dong, 2017;Naess, 2017), as well as visual representations like images, videos and emojis (Arya et al, 2018;García-Rapp, 2018;Kudaibergenova, 2019;Schuman et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Podcast ethnography

Lundström

2020

International Journal of Social Research Methodology

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This article introduces the method of podcast ethnography. The method encompasses three general stages: to explore a podcast from a particular social field, to engage with it through careful, ethnographic reflexivity and to examine the podcast by developing typologies and themes expedient for analysis. Podcast ethnography is beneficial due to its spatial and temporal flexibility; observing a podcast universe can be performed on the move and in parallel with other tasks. This advantage enables a muchneeded breathing space for researchers inquiring vehement milieus, such as white radical nationalism. The article uses an example from this precise milieu in Sweden-the podcast Motgift [Antidote]-to illustrate and flesh out the potentials and challenges of applying the method's three stages. In so doing, the article argues for inclusion of podcast ethnography into the extended family of ethnographic methods.

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Twitter Presence of Jet Airways-Deriving Customer Insights Using Netnography and Wordclouds

Cited by 29 publications

References 11 publications

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

The Concept of Sustainability on Social Media: A Social Listening Approach

Podcast ethnography

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