The Emotions that Drive Viral Video

Nelson-Field, Karen; Riebe, Erica; Newstead, Kellie

doi:10.1016/j.ausmj.2013.07.003

Cited by 130 publications

(95 citation statements)

References 28 publications

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“…This includes psychological metrics (e.g., arousal and valence measured via self-assessment, e.g. [40]), physiological measures (e.g., valence measured via EEG, [30]; see also [39]) or behavioral measures (e.g., facial expression via facial recognition, [33]). It is important to note here, that this excludes analyses of affect, which do not involve individual reactions, such as sentiment analyses of social media posts (e.g., [50]).…”

Section: Methodology Of the Literature Reviewmentioning

confidence: 99%

Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda

Schreiner

Fischer

Riedl

2019

Electron Commer Res

135

114

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We present a review of N = 45 studies, which deals with the effect of characteristics of social media content (e.g., topic or length) on behavioral engagement. In addition, we reviewed the possibility of a mediating effect of emotional responses in this context (e.g., arousing content has been shown to increase engagement behavior). We find a diverse body of research, particularly for the varying content characteristics that affect engagement, yet without any conclusive results. We therefore also highlight potential confounding effects causing such diverging results for the relationship between content characteristics and content engagement. We find no study that evaluates the mediating effect of emotional responses in the content-engagement relationship and therefore call for further investigations. In addition, future research should apply an extended communication model adapted for the social media context to guarantee rigorous research.

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Section: Methodology Of the Literature Reviewmentioning

confidence: 99%

Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda

Schreiner

Fischer

Riedl

2019

Electron Commer Res

135

114

View full text Add to dashboard Cite

show abstract

“…Information spreading processes are a basement of viral marketing [1], rumours [2], social and political changes [3], innovation adoption [4] and other initiatives with emotional appeal [5]. Possible types of contents propagated within electronic systems include texts [6], concepts [7], video material [8], images [9] and marketing messages [10]. Research in this area is, among others, related to information flow within the networks [11], factors affecting performance of marketing campaigns [12] and the selection of highly influential nodes [13].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Rankings for Seed Selection in Complex Networks: Balancing Costs and Coverage

Jankowski

2017

Entropy

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Information spreading processes within the complex networks are usually initiated by a selection of highly influential nodes in accordance with the used seeding strategy. The majority of earlier studies assumed the usage of selected seeds at the beginning of the process. Our previous research revealed the advantage of using a sequence of seeds instead of a single stage approach. The current study extends sequential seeding and further improves results with the use of dynamic rankings, which are created by recalculation of network measures used for additional seed selection during the process instead of static ranking computed only once at the beginning. For calculation of network centrality measures such as degree, only non-infected nodes are taken into account. Results showed increased coverage represented by a percentage of activated nodes dependent on intervals between recalculations as well as the trade-off between outcome and computational costs. For over 90% of simulation cases, dynamic rankings with a high frequency of recalculations delivered better coverage than approaches based on static rankings.

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“…Previous research on virality has primarily been drawn from five different fields: psychology (e.g., [9]; [11]; [12]; [13]; [4]), computer science (e.g., [14]; [15]; [16]; [1]; [10]; [17]; [18]; [19]; [20]; [21]; [22]), political science (e.g., [2]; [23]; [24]), marketing (e.g., [25]; [26]; [27]; [28]; [29]; [30]; [31]; [7]; [32]; [33]), and health (e.g., [34]). These studies have been mainly conducted in Western countries, such as the United States, Canada, Germany, Italy, and Australia.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Intelligent Parallel Mixed Method Approach for Characterising Viral YouTube Videos in Saudi Arabia

Alshanqiti¹,

Bajnaid²,

Rehman³

et al. 2020

IJACSA

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In social networking platforms, comprehending virality, exemplified by YouTube, is of great importance, which helps in understanding what characteristics utilised to create content along with what dynamics involved in contributing to YouTube's strength as a platform for sharing content. The current literature surrounding virality problem appears sparse concerning development theories, investigations regarding empirical facts, and an understanding of what makes videos go viral. The overarching objective is to understand deeply the phenomena of viral YouTube videos in Saudi Arabia, hence we propose an intelligent convergent parallel mixed-methods approach that begins, as an internal step, by a qualitative thematic analyses method and an NLP-based quantitative method independently, followed by training an unsupervised clustering model for integrating the internal analysis outputs for deeper insights. We have empirically analysed some trended YouTube videos along with their contents, for studying such phenomena. One of our main findings revealed that boosting entertainments, traditions, politics, and/or religion issues when making a video, that is associated in somehow with sarcastic or rude remarks, is likely the preeminent impulse for letting a regular video go viral.

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The Emotions that Drive Viral Video

Cited by 130 publications

References 28 publications

Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda

Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda

Dynamic Rankings for Seed Selection in Complex Networks: Balancing Costs and Coverage

Intelligent Parallel Mixed Method Approach for Characterising Viral YouTube Videos in Saudi Arabia

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