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<p>In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.</p>
</abstract>

In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.

Construction and validation of a new media marketing influence assessment model

Chengdu College of University of Electronic Science and Technology of China

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Construction and validation of a new media marketing influence assessment model"}

In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy....

Construction and validation of a new media marketing influence assessment model

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 23 mar 2024

Accettato: 07 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1703

Parole chiave
New media marketing, PageRank, UIEM-CMR influence assessment model, Influence assessment

© 2024 Xia Hu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction and validation of a new media marketing influence assessment model

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 23 mar 2024

Accettato: 07 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1703

Parole chiaveNew media marketing, PageRank, UIEM-CMR influence assessment model, Influence assessment

© 2024 Xia Hu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
New media marketing, PageRank, UIEM-CMR influence assessment model, Influence assessment