Finding Goldilocks Influencers: How Follower Count Drives Social Media Engagement

Wies, Simone; Bleier, Alexander; Edeling, Alexander

doi:10.1177/00222429221125131

Cited by 108 publications

(32 citation statements)

References 67 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This information can then be utilized to identify influential consumers whose brand‐related posts are likely to be shared by other consumers (see Study 2). This approach may prove to be more cost‐efficient and effective than recruiting influencers solely based on their large number of followers (Wies et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

How construal–regulatory mode fit increases social media sharing

et al. 2023

View full text Add to dashboard Cite

How can social media managers engage consumers to share posts with others? Extending regulatory mode theory, we demonstrate that high construal levels enable the integration of regulatory mode complementarity orientations, resulting in engagement and shares. Regulatory mode complementarity refers to the combination of high assessment (i.e., the motivation to “be right” by critically evaluating options) and high locomotion (i.e., the motivation to “act” by moving toward a goal). Specifically, this research proposes that an abstract (vs. concrete) construal allows these two orientations to work together, resulting in regulatory fit. Three text analysis field studies on marketer‐ and consumer‐generated Facebook and Twitter posts show that construal–regulatory mode fit increases social media sharing. Three follow‐up studies then show generalizability, establish causality, and demonstrate the role of engagement as the underlying mechanism driving the fit effect.

show abstract

Section: Discussionmentioning

confidence: 99%

How construal–regulatory mode fit increases social media sharing

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The first of these is their high number of followers (Cha, Haddadi, Benevenuto, & Gummadi, 2010). Because public figures do not know all of the users who follow them, most of their followers are likely to be weak ties (Wies, Bleier, & Edeling, 2022). Given that weaker ties are more likely to share negative content than positive content (Fan et al, 2016;Hansen et al, 2011), negative content generated by users who have many followers, such as public figures, is more likely to be shared.…”

Section: Public Figures and Ordinary Usersmentioning

confidence: 99%

Negative Expressions are Shared more on Twitter for Public Figures than for Ordinary Users

Schöne¹,

García²,

Parkinson³

et al. 2022

Preprint

View full text Add to dashboard Cite

Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with the content of tweets. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media – public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers (and strength of ties), and proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed to mitigate overexposure to negative content.

show abstract

“…This engagement is measured through parameters like subscribers, the number of comments likes and shares, as the advertising firms critically use these parameters to optimize their selection of the right influencer (Leung et al, 2022). However, while the increasing budgets of IM have brought in millions of new influencers in the field, choosing the right set of Goldilock influencers is going to be daunting (Wies et al, 2022). The selection process requires thoroughly evaluating various factors, such as the influencer's audience demographics, content style and overall fit with the product to be promoted.…”

Section: Introductionmentioning

confidence: 99%

“…However, while the increasing budgets of IM have brought in millions of new influencers in the field, choosing the right set of Goldilock influencers is going to be daunting (Wies et al ., 2022). The selection process requires thoroughly evaluating various factors, such as the influencer's audience demographics, content style and overall fit with the product to be promoted.…”

Section: Introductionmentioning

confidence: 99%

Influencer recommendation system: choosing the right influencer using a network analysis approach

Jha,

Ray

2023

MIP

View full text Add to dashboard Cite

PurposeThe 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/approachData 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.FindingsThe 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/implicationsThe 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 implicationsManagers 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/valueThe study contributes to outreach evaluation and better estimating the impact of SMIs using a novel semantic network approach.

show abstract

Finding Goldilocks Influencers: How Follower Count Drives Social Media Engagement

Cited by 108 publications

References 67 publications

How construal–regulatory mode fit increases social media sharing

How construal–regulatory mode fit increases social media sharing

Negative Expressions are Shared more on Twitter for Public Figures than for Ordinary Users

Influencer recommendation system: choosing the right influencer using a network analysis approach

Contact Info

Product

Resources

About