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Article
Publication date: 14 November 2016

Rong Wang, Wenlin Liu and Shuyang Gao

The purpose of this paper is to conceptualize the use of Twitter hashtag as a strategy to enhance the visibility and symbolic power of social movement-related information…

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4381

Abstract

Purpose

The purpose of this paper is to conceptualize the use of Twitter hashtag as a strategy to enhance the visibility and symbolic power of social movement-related information. It examined how characteristics of hashtag drove information virality during a networked social movement.

Design/methodology/approach

Twitter data from two days during the Occupy Wall Street Movement in 2011 were collected. With network analysis, the authors identified popular hashtag types and examined hashtag co-occurrence patterns during the two contrasting movement days. It also provides a comparative analysis of how major types of viral hashtag may play different roles depending on different movement cycles.

Findings

The authors found that the role of hashtag influencing information virality may vary based on the context of the tweets. For example, movement participants applied more strategic hashtag combinations during the unexpected event day to reach different social circles. Consistent patterns were identified in mobilizing influential actors such as public figures. Different use patterns of media outlet hashtag were found across the two days.

Originality/value

Implications on how hashtag type and event dynamics may shape hashtag co-occurrence patterns were discussed.

Details

Online Information Review, vol. 40 no. 7
Type: Research Article
ISSN: 1468-4527

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Article
Publication date: 22 March 2021

Nimish Joseph, Arpan Kumar Kar and P. Vigneswara Ilavarasan

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close…

Abstract

Purpose

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.

Design/methodology/approach

This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.

Findings

Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.

Research limitations/implications

This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.

Originality/value

There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.

Details

Information Discovery and Delivery, vol. 49 no. 2
Type: Research Article
ISSN: 2398-6247

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Article
Publication date: 30 September 2014

Gohar Feroz Khan and Sokha Vong

– The purpose of this paper is to seek reasons for some videos going viral over YouTube (a type of social media platform).

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6913

Abstract

Purpose

The purpose of this paper is to seek reasons for some videos going viral over YouTube (a type of social media platform).

Design/methodology/approach

Using YouTube APIs (Application Programming Interface) and Webometrics analyst tool, the authors collected data on about 100 all-time-most-viewed YouTube videos and information about the users associated with the videos. The authors constructed and tested an empirical model to understand the relationship among users’ social and non-social capital (e.g. User Age, Gender, View Count, Subscriber, Join Date, Total Videos Posted), video characteristics (Post Date, Duration, and Video Category), external network capital (in-links and hit counts), and Virality (Likes, Dislikes, Favorite Count, View Count, and Comment Count). Partial least square and Webometric analysis was used to explore the association among the constructs.

Findings

Among other findings, the results showed that popularity of the videos was not only the function of YouTube system per se, but that network dynamics (e.g. in-links and hits counts) and offline social capital (e.g. fan base and fame) play crucial roles in the viral phenomenon, particularly view count.

Originality/value

The authors for the first time constructed and tested an empirical model to find out the determinants of viral phenomenon over YouTube.

Details

Internet Research, vol. 24 no. 5
Type: Research Article
ISSN: 1066-2243

Keywords

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Article
Publication date: 30 June 2020

Chang Heon Lee and Heng Yu

Social media have increasingly gained credibility as information sources in emergencies. Retweeting or resharing nature has made Twitter a popular medium of information…

Abstract

Purpose

Social media have increasingly gained credibility as information sources in emergencies. Retweeting or resharing nature has made Twitter a popular medium of information dissemination. The purpose of this article is to enhance our understanding of both linguistic style and content properties (i.e. both affective and informational contents) that drives resharing behavior or virality of disaster messages on Twitter. We investigate this issue in the context of natural disaster crisis.

Design/methodology/approach

In this study, the authors develop, drawing upon language expectancy and uncertainty reduction theories as an enabling framework, hypotheses about how the language (i.e. style and content) influence resharing behavior. They employ a natural language processing of disaster tweets to examine how the language – linguistic style (concrete and interactive language) and linguistic content (information- and affect-focused language) – affects resharing behavior on Twitter during natural disasters. To examine the effects of both linguistic style and content factors on virality, a series of negative binomial regressions were conducted, particularly owing to the highly skewed count data.

Findings

Our analysis of tweets from the 2013 Colorado floods shows that resharing disasters tweets increases with the use of concrete language style during acute emergencies. Interactive language is also positively associated with retweet frequency. In addition, neither positive nor negative emotional tweets drive down resharing during acute crises, while information-focused language content has a significantly positive effect on virality.

Practical implications

Agencies for public safety and disaster management or volunteer organizations involved in disseminating crisis and risk information to the public may leverage the impacts of the linguistic style and language content through the lens of our research model. The findings encourage practitioners to focus on the role of linguistic style cues during acute disasters. Specifically, from the uncertainty reduction perspective, using concrete language in the disaster tweets is the expected norm, leading to a higher likelihood of virality. Also, interactively frame disaster tweets are more likely to be diffused to a larger number of people on Twitter.

Originality/value

The language that people use offer important psychological cue to their intentions and motivations. However, the role of language on Twitter has largely been ignored in this crisis communication and few prior studies have examined the relationship between language and virality during acute emergencies. This article explains the complex and multifaceted nature of information resharing behavior using a multi-theoretical approach – including uncertainty reduction and language expectancy theory – to understand effects of language style and content cues on resharing behavior in the context of natural crisis events.

Details

Industrial Management & Data Systems, vol. 120 no. 8
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 14 January 2022

Krishnadas Nanath, Supriya Kaitheri, Sonia Malik and Shahid Mustafa

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of…

Abstract

Purpose

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

Design/methodology/approach

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

Findings

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Practical implications

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

Originality/value

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

Details

Journal of Systems and Information Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1328-7265

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Article
Publication date: 23 July 2021

Adarsh Anand, Mohammed Shahid Irshad and Yogesh K. Dwivedi

YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making…

Abstract

Purpose

YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making the channel subscribers the potential viewers of the video. And thus, they are the first to come to know about any new offering. But later on, the view count also increases due to virality, that is, mass sharing of the content by the users on different social media platforms similar to word-of-mouth in the field of marketing. Therefore, the purpose of this paper is to examine different diffusion patterns as they can help to inflate traffic and generate revenue.

Design/methodology/approach

YouTube's view count grows majorly through virality. The pattern of view count growth has generally been considered unimodal in most of the available research in the field of YouTube. In the present work, the growth process due to views through the subscribers and views due to word-of-mouth (virality) is presented. Considering that the impact of virality in view count growth comes later in the video life cycle; the viewing patterns of both the segments have been mathematically modeled; independently.

Findings

Different models have been proposed to capture the view count growth pattern and how the impact of virality changes the view count growth curve and thereby results in a multimodal curve structure. The proposed models have been verified on various view count data sets of YouTube videos using SPSS (Statistical Package for the Social Sciences), and their ranks have been determined using a weighted criteria–based approach. The results obtained clearly depict the presence of many modes in the life cycle of view counts.

Originality/value

Till now, the literature is evident of the video life cycle following a bell shape curve. This study claims that the initial thrust is by subscribers and then the contribution in the view count by people watching via word-of-mouth comes into picture and brings in another hump in the growth curve.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 3 August 2020

Niyati Aggrawal, Anuja Arora, Adarsh Anand and Yogesh Dwivedi

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video…

Abstract

Purpose

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

Design/methodology/approach

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

Findings

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Originality/value

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

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Abstract

Details

Internet Celebrity: Understanding Fame Online
Type: Book
ISBN: 978-1-78756-079-6

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Article
Publication date: 23 September 2013

Enrique Bonsón and Melinda Ratkai

This study aims to propose a set of metrics in order to assess reactivity, dialogic communication and stakeholder engagement (popularity, commitment and virality)…

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5536

Abstract

Purpose

This study aims to propose a set of metrics in order to assess reactivity, dialogic communication and stakeholder engagement (popularity, commitment and virality): stakeholders' mood and social legitimacy on corporate Facebook pages. These metrics can offer a better understanding and measurability of this social media/social network/online communication management tool.

Design/methodology/approach

Three theories (dialogic, stakeholders and legitimacy) were considered in the development of these metrics. Empirical evidence was collected from a sample of 314 European companies. Then ten active companies were used to validate the proposed metrics on Facebook.

Findings

The constructed set of metrics was found to be valid and efficiently usable according to the principles of the applied theories. Moreover all the proposed metrics could be adapted for such sites as Google+.

Research limitations/implications

Limitations can only be identified within the validation process as the metrics were only applied to ten representative companies from the Eurozone.

Practical implications

The proposed metrics will help users, marketing/PR/communication professionals and company managers to measure their and their competitors' popularity, commitment, virality (metrics which reflect stakeholder engagement), and the mood of stakeholders, and use content analysis in order to measure social legitimacy via CSR information disclosure on Facebook. Thus the online reputation of a company can be practically measured.

Originality/value

This paper is the first proposing metrics to assess stakeholder engagement and social legitimacy on a corporate Facebook page that can be used in both academic and professional circles to a gain a better understanding of corporate online communication via Facebook.

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Article
Publication date: 10 April 2019

Xia Liu

Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big…

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1243

Abstract

Purpose

Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended.

Design/methodology/approach

A Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data.

Findings

The findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots.

Research limitations/implications

As brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots.

Originality/value

This is the first big data examination of social bots in the context of brand-related user-generated content.

Details

Journal of Services Marketing, vol. 33 no. 4
Type: Research Article
ISSN: 0887-6045

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