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.

中文翻译：

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影响者推荐系统：使用网络分析方法选择合适的影响者

目的

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，这些领域已成为学术研究的重要领域。然而，尽管它作为一个研究领域很突出，但仍必须解决一些重大挑战。一项重大挑战涉及识别、评估和推荐社交媒体影响者 (SMI)。本研究提出了一种语义网络模型，能够衡量影响者在特定主题或主题上的表现，以解决这个问题。这项研究可以帮助管理者根据估计的覆盖范围确定合适的 SMI。

设计/方法论/途径

提取来自流行 YouTube 影响者的数据和公开的绩效衡量标准（观看次数和点赞数）。其次，影响者过去制作的视频的标题用于开发一个基于相似性度量将所有视频与其他视频连接起来的语义网络。第三，最近邻方法提取目标标题视频的邻居。最后，基于邻居集合，对目标视频与影响者的观看和喜欢进行范围预测。

发现

结果表明，该模型可以根据建议的视频标题和内容创建者预测准确的观看次数和点赞范围，对于 YouTube 上的不同影响者，准确率达到 69-78%。

研究局限性/影响

当前的研究引入了一种新颖且创新的方法，该方法利用 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，但这项研究认识到即将到来的研究人员可能会解决的各种限制。预测有关新颖主题的帖子的观看次数以及根据年龄精确调整视频观看次数构成了本研究的两个主要局限性。

实际影响

有兴趣雇用影响者的管理者可以采用建议的方法来评估影响者在特定主题上的潜在表现。这项研究利用易于理解和解释的基于数据的指标，帮助管理者就影响者选择做出明智的决策。

原创性/价值

该研究有助于使用新颖的语义网络方法进行外展评估并更好地估计 SMI 的影响。