Connecting the dots to infer followers' topical interest on Twitter

Nigam, Aastha; Aguinaga, Salvador; Chawla, Nitesh V.

doi:10.1109/besc.2016.7804498

Cited by 4 publications

(7 citation statements)

References 10 publications

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“…To address the limitation of previous research, we conducted a new qualitative study. Table (1) outlines the data collection methods used in this study. The first phase consisted of an interview with sixteen participants, aged between 18 and 30 years.…”

Section: Methodsmentioning

confidence: 99%

“…Social media enables people to write about their daily life, express opinions, share information and connect with other people for personal and business requirements. Also, social media facilitate a great opportunity for different business to advert their brands and reach out to customers and create opportunities and communities [1]. However, this wealth of connectivity may drive people to relate to social media in a problematic style and experience fear of missing out (FoMO).…”

Section: Introductionmentioning

confidence: 99%

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Fear of Missing Out (FoMO) as Really Lived: Five Classifications and one Ecology

Alutaybi

McAlaney

Arden‐Close

et al. 2019

2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC)

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Social media provides a platform for information sharing and dissemination and has speedily become a popular method for individuals to relate to others regardless of the time and geographical distance. However, this wealth of connectivity and availability of information may lead to the experience of the Fear of Missing Out (FoMO) that typically refers to a preoccupation of the users of social media about lost opportunities when they are offline or unable to connect and communicate as wished. Despite the recognition of the concept, studies around FoMO have used offline data collection techniques such as interviews, focus groups and surveys. This has led to a limited understanding of the lived FoMO experience and a rather simplified and coarse-grained view of the concept. In this paper, we delve into the specifics and nuances of FoMO through multi-stage qualitative research, including interviews, diary study and three focus group sessions and elaborate upon the concept and determine its various manifestations and classification. The lived experience is mainly gathered through a diary study. We present five main classifications characterising FoMO and develop an ecology for it.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fear of Missing Out (FoMO) as Really Lived: Five Classifications and one Ecology

Alutaybi

McAlaney

Arden‐Close

et al. 2019

2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC)

View full text Add to dashboard Cite

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“…The LDA topic modeling approach is also applied in [67], where a user centric topic discovery framework is proposed. The users' tweets are analyzed for identifying their interests and for creating personalized topic profiles.…”

Section: User-oriented Matchingmentioning

confidence: 99%

“…The work in [67] was described in Section 4.2.1 and presents an LDA [59] topic profile modeling approach for the discovery of users' interests. Therefore, a POS tagger extracts the nouns from their tweets, which are then provided to a search engine to retrieve the top relating web pages.…”

Section: Topic and Event-oriented Matchingmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content. In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

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“…Many studies have been conducted to explore aspects of customer services experiences in various business domains, such as travel and telecoms [14,16,23,26,28]. News agencies are not far from social media analysis, using it to uncover users' interests so they can provide more focused contents [18]. While various domains have long applied network analysis techniquessuch as for crime detection and prevention [19,20] -only recently has work has been conducted to see how users relate to brands via network structures [8], how information shared by companies disseminate and their types [22], and what type of engagements from companies was found to be of effect on customers perception of the brand [13].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Performance and Competition in Customer Services on Twitter

Albishry

Crick

Tryfonas

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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This is the final published version of the article (version of record). It first appeared online via ACM at https://dl.acm.org/citation.cfm?id=3191631 . Please refer to any applicable terms of use of the publisher. University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. ABSTRACTWith an increasing number of consumers using social media platforms to share both their satisfaction and displeasure about the products and services they use every day, organisations with a customer service focus are recognising the importance of rapidand genuine -online engagement with their customers. In turn, consumers increasingly judge organisations on the quality of customer service and degree of responsiveness to online queries. This paper presents an extensible framework for evaluating direct engagements of customer service teams with customers on Twitter. Furthermore, this framework provides the capability to measure and analyse indirect engagement with industry sector rivals, especially their patterns, frequency and intensity. By applying graph analysis to these Twitter interactions, our framework generates various analytical measures and visual representations, exemplified through a case study based on seven major UK telecoms companies. With a dataset consisting of 15,000 tweets and 3,500 user profiles, the results provide sustained evidence for indirect engagements between business rivals, with customer queries acting as a trigger for intense competition between companies based in the same industry sub-domain.

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Connecting the dots to infer followers' topical interest on Twitter

Cited by 4 publications

References 10 publications

Fear of Missing Out (FoMO) as Really Lived: Five Classifications and one Ecology

Fear of Missing Out (FoMO) as Really Lived: Five Classifications and one Ecology

Modeling Influence with Semantics in Social Networks

An Evaluation of Performance and Competition in Customer Services on Twitter

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