Using Interest Graphs to Predict Rich-Media Diffusion in Content-Based Online Social Networks

Church, E. Mitchell; Iyer, Lakshmi; Zhao, Xia

doi:10.1080/10580530.2015.1044340

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…Furthermore, the majority of the earlier studies based on consumer survey have primarily focused on the triggers that spread eWOM in SNS like Facebook (Rossmann et al , 2016) or Twitter. However, when a person shares information or welcome feedback about any product in the social media, the same may not attract his/her network friend who does not share a similar interest (Church et al , 2015). On the other hand, eWOM gets more focused attention when those are shared on the e-commerce portal from where the consumer purchases the product.…”

Section: Review Of Literaturementioning

confidence: 99%

Triggers of positive eWOM: exploration with web analytics

Amed

Mukherjee

Das

et al. 2019

MIP

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Purpose The purpose of this paper is to determine the triggers of positive electronic word of mouth (eWOM) using real-time Big Data obtained from online retail sites/dedicated review sites. Design/methodology/approach In this study, real-time Big Data has been used and analysed through support vector machine, to segregate positive and negative eWOM. Thereafter, using natural language processing algorithms, this study has classified the triggers of positive eWOM based on their relative importance across six product categories. Findings The most important triggers of positive eWOM (like product experience, product type, product characteristics) were similar across different product categories. The second-level antecedents of positive eWOM included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand. Practical implications The findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive eWOM. Consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive eWOM about the product’s ingredients/characteristics. Companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”. Originality/value This study identified the triggers of the positive eWOM using real-time Big Data extracted from online purchase platforms. This study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.

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Section: Review Of Literaturementioning

confidence: 99%

Triggers of positive eWOM: exploration with web analytics

Amed

Mukherjee

Das

et al. 2019

MIP

View full text Add to dashboard Cite

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“…One of the resources to develop this insight and understand product competition is through analyzing online customer reviews [6,8,17], especially reviews that compare the product with its competitors. When shopping online, customers often search for, evaluate, and compare products through online customer reviews.…”

Section: Introductionmentioning

confidence: 99%

Where Does My Product Stand? A Social Network Perspective on Online Product Reviews

Vemprala¹,

Xiong²,

Liu³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Customer reviews often include comparative comments on competing products. Adopting the "The Strength of Weak Ties" theory, we build a product social network around "strong tie" and "weak tie" entities. By performing text mining on comparative customer reviews collected from Amazon, we successfully identify strong and weak ties in a product network and compute the strength of these ties. Utilizing these network properties, we generate network graphs based on different product features and discover the underlying competitive relationships among them. In particular, our regression analysis shows that the strength of ties positively contributes to the review rating of a product and the strength of weak ties plays a more significant role than the strength of strong ties. These results will benefit vendors in online market to discover potential competitors, effectively tailor their marketing and product development efforts, and better position their products to increase profit and explore new market opportunities.

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“…Mozafari and Hamzeh (2015) proposed an information diffusion model to capture the effect of social behavior to diffuse information. Church et al (2015) examined the rich-media diffusion in content-based social networks.…”

Section: Introductionmentioning

confidence: 99%

Effects of source influence and peer referrals on information diffusion in Twitter

Kwon¹,

Han

Kim

2017

IMDS

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Purpose Social media have attracted attention as an information channel for content generated in heterogeneous internet services. Focusing on social media platforms, the purpose of this paper is to examine the factors behind social transmission with content crossover from other services through hypertext link (URL). The authors investigate the effects of source influence and peer referrals on diffusion outcome and address their variations in the case of content crossover. Design/methodology/approach The authors use a Poisson regression model due to the discrete nature of the dependent variable. The authors conduct an empirical study using 233 million real transaction data generated by 1,203,196 Korean users of Twitter. Findings Source influence and peer referral have a positive impact on cascade size in the content dissemination process. In the case of content crossover, the impact of source influence decreases. However, the impact of peer referrals increases in the process of external content dissemination. Research limitations/implications The authors demonstrate source and peer effects on content diffusion and that these effects vary when shared content is linked from an external service by a URL. Practical implications The findings indicate that firms that wish to diffuse information through social media or enter the social media with new services to provide new ways of creating and sharing content should understand the nature of the social transmission process. Originality/value Given the growing popularity of social media, particularly SNSs with online social networks as information channels, the authors first consider online social transmission as a user-driven diffusion process. Based on social factors in the diffusion process, the authors derive source and peer effects on the social transmission process.

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Using Interest Graphs to Predict Rich-Media Diffusion in Content-Based Online Social Networks

Cited by 13 publications

References 31 publications

Triggers of positive eWOM: exploration with web analytics

Triggers of positive eWOM: exploration with web analytics

Where Does My Product Stand? A Social Network Perspective on Online Product Reviews

Effects of source influence and peer referrals on information diffusion in Twitter

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