Determining Influential Users in Internet Social Networks

By Michael Trusov, Anand V. Bodapati, Randolph E. Bucklin. 2010

752 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1c4311c11f4d1cc735227f879101172f6a7adcbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Marketing

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

The role of digital influencers in brand recommendation: Examining their impact on engagement, expected value and purchase intention (2019, neutral): “…actual influence of influencers taking into account evidence questioning whether the number of followers and other direct indicators of popularity (e.g., profile views) do in fact automatically imply high influence (De Veirman et al ., 2017; Romero et al ., 2011; Trusov, Bodapati, & Bucklin, 2010).”
A Thematic Exploration of Digital, Social Media, and Mobile Marketing: Research Evolution from 2000 to 2015 and an Agenda for Future Inquiry (2016, methodology): “Trusov, Bodapati, and Bucklin (2010) also made an important contribution to this literature by developing a method for identifying in ﬂ uential users in online social networks, where in ﬂ uence is de ﬁ ned by having signi ﬁ cant effects on the online activities of others.”
Integrating Marketing Communications: New Findings, New Lessons, and New Ideas (2016, background): “Integrated marketing communication plans also need to be in place to solicit credible and positive reviews and ratings, endorsements, and testimonials in the brand’s owned media as well as earned and shared media (Chevalier and Mayzlin 2006; Trusov, Bodapati, and Bucklin 2010).”
Measuring social media influencer index- insights from facebook, Twitter and Instagram (2019, background): “The exponential increase in the amount of content generated through social media forces the network participants to strive for greater attention and subsequent influence on the information takers (Trusov, Bodapati, & Bucklin, 2010).”
Introduction to the Special Issue - Social Media and Business Transformation: A Framework for Research (2013, background): “…social media have been integral to recent advances in relational inference about consumer preferences (Hill et al. 2006, Aral et al. 2009, Trusov et al. 2010), novel peer-to-peer and targeted marketing techniques (Aral and Walker 2011, 2012), and demand prediction (Asur and Huberman…”
Digital marketing: A framework, review and research agenda (2017, neutral): “Trusov, Bodapati, and Bucklin (2010) focused on identifying the influential members in a social network – the influencers – who could then be the target for a firm hoping to propagate their message.”
How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis (2018, background): “[56] show, community members differ in the frequency, volume, type, and quality of digital content they generate and consume.”