网红推荐系统:使用网络分析方法选择合适的网红

IF 3.6 3区 管理学 Q2 BUSINESS
Abhishek Kumar Jha, Sanjog Ray
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引用次数: 0

摘要

社交媒体的兴起导致了影响者和影响者营销(IM)领域的出现,这已经成为学术研究的重要领域。然而,尽管它作为一个重要的研究领域,有几个重大的挑战必须解决。一个重大挑战涉及识别、评估和推荐社交媒体影响者(SMIs)。为了解决这个问题,本研究提出了一个语义网络模型,能够测量网红在特定主题或主题上的表现。这项研究可以帮助管理者根据他们的估计范围确定合适的SMIs。设计/方法/方法从YouTube上受欢迎的影响者和公开可用的性能指标(观看和喜欢)中提取数据。其次,利用网红过去制作的视频的标题来开发一个基于相似度度量的语义网络,将所有视频与其他视频连接起来。第三,最近邻法提取目标标题视频的邻居。最后,基于邻居集,对目标视频与影响者的观看和喜欢进行范围预测。结果表明,该模型可以根据推荐的视频标题和内容创建者准确预测观看和点赞的范围,在YouTube上不同的影响者中准确率为69-78%。目前的研究引入了一种新颖和创新的方法,利用SMI先前内容之间的文本关联来预测其未来内容的结果。尽管研究结果令人鼓舞,但这项研究也认识到未来研究人员可能面临的各种限制。预测新主题帖子的浏览量和基于年龄精确调整视频浏览量是本研究的两个主要局限性。有兴趣雇用网红的经理可以采用建议的方法来评估网红在特定主题上的潜在表现。这项研究有助于管理者利用基于数据的指标,对影响者的选择做出明智的决定,这些指标易于理解和解释。原创性/价值本研究使用一种新颖的语义网络方法,有助于外展评估和更好地估计smi的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influencer recommendation system: choosing the right influencer using a network analysis approach
Purpose The rise of social media has led to the emergence of influencers and influencer marketing (IM) domains, which have become important areas of academic inquiry. However, despite its prominence as an area for study, several significant challenges must be addressed. One significant challenge involves identifying, assessing and recommending social media influencers (SMIs). This study proposes a semantic network model capable of measuring an influencer's performance on specific topics or subjects to address this issue. This study can assist managers in identifying suitable SMIs based on their estimated reach. Design/methodology/approach Data from popular YouTube influencers and publicly available performance measures (views and likes) are extracted. Second, the titles of the past videos made by the influencer are used to develop a semantic network connecting all the videos to other videos based on similarity measures. Third, the nearest neighbor approach extracts the neighbors of the target title video. Finally, based on the set of neighbors, a range prediction is made for the views and likes of the target video with the influencer. Findings The results show that the model can predict an accurate range of views and likes based on the suggested video titles and the content creator, with 69–78% accuracy across different influencers on YouTube. Research limitations/implications The current study introduces a novel and innovative approach that exploits the textual association between a SMI's previous content to forecast the outcome of their future content. Although the findings are encouraging, this research recognizes various constraints that upcoming researchers may tackle. Forecasting views of posts concerning novel subjects and precisely adjusting video view counts based on their age constitute two primary limitations of this study. Practical implications Managers interested in hiring influencers can employ the suggested approach to evaluate an influencer's potential performance on a specific topic. This research aids managers in making informed decisions regarding influencer selection, utilizing data-based metrics that are simple to comprehend and explain. Originality/value The study contributes to outreach evaluation and better estimating the impact of SMIs using a novel semantic network approach.
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来源期刊
CiteScore
8.00
自引率
9.10%
发文量
64
期刊介绍: Marketing Intelligence & Planning (MIP) facilitates communication between researchers and practitioners, providing the users of research with a wealth of robust and relevant information. At a time when some journals are losing their relevance to industry and practical requirements, MIP successfully offers a bridge between academic and practitioner thinking, while retaining a high level of scientific rigour.
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