MKTG90042 Chap.4 Influencers and Social Network Analysis
Influencers and Social Network Analysis
Influence is the capacity to affect attention, interpretation or behaviour within a social setting. Follower count is therefore only one clue. An influencer may matter because of credibility with a niche, repeated interaction, a brokerage position between communities or proximity to people who act.
The marketing decision is to match that form of influence to the campaign objective.
Social network analysis represents actors as nodes and relationships as edges. Degree centrality counts direct connections. Betweenness highlights nodes that lie on many shortest paths and may bridge groups.
Closeness concerns how readily a node can reach others, while eigenvector centrality gives more weight to connections with already influential nodes. Density describes connection within the network overall.
Choose a measure from the decision.
Degree can identify visible local connectors; betweenness can reveal brokers for cross-community transmission; closeness can support rapid network-wide contact; eigenvector centrality can locate prestige.
The metric never replaces qualitative judgement about audience fit, credibility, disclosure, brand safety and the actual content partnership.
In a seven-person network, Ari links to three neighbours inside one cluster, while Bo links only to two people but joins otherwise separate clusters. Ari has the higher degree; Bo can have higher betweenness.
A reach campaign inside the first cluster may favour Ari, whereas a message intended to cross communities may make Bo strategically valuable.
Never equate centrality with persuasion. A structurally central account may be distrusted, inactive or irrelevant to the target. Nor should a marketer select a creator solely by a reported engagement rate without checking how the figure was defined.
State the denominator, inspect audience authenticity and assess whether the partnership can produce the required experience.
A complete recommendation combines network position, audience overlap, creative role, compensation, disclosure and measurement. Explain why the selected creator can perform the needed function and what evidence would trigger a change.
This joins quantitative structure to marketing judgement instead of treating the highest score as an automatic answer.
Network boundaries are analytical choices. A centrality score changes when the observed actors, tie definition or time window changes.
State whether an edge represents following, interaction or information flow, because those networks can rank the same creator differently and support different marketing conclusions.
Influencer evaluation should distinguish structural reach from relationship quality and creative capability. A broker may carry information between communities yet lack the trust needed for endorsement.
A niche creator may reach fewer people but prompt richer questions or more qualified action. Network construction also changes the ranking: following ties, mentions, replies and co-participation represent different relationships. Choose the edge definition and observation window before calculating centrality, then explain why that network matches the marketing task. Partnership design follows after structural analysis.
Define the creator's role, content freedom, disclosure, compensation, brand-safety boundary and outcome measure. This sequence prevents a centrality score from becoming a substitute for a complete collaboration decision.
A final rehearsal for Influencers and Social Network Analysis should vary the organisation, audience and constraint while preserving the chapter logic.
Use nodes, edges and network communities to frame the first decision, test it against degree and betweenness centrality, and explain how closeness, eigenvector centrality and density changes the practical recommendation.
Transfer in Influencers and Social Network Analysis is demonstrated when the reasoning survives a new setting without relying on a memorised brand example.
Before closing the response, use influencer fit, disclosure and measurement as an audit lens.
For Influencers and Social Network Analysis, check that each factual claim comes from the case, each interpretation names a mechanism and each recommendation carries a measure or safeguard. Delete any framework that performs no analytical work in the Influencers and Social Network Analysis response.
The remaining Influencers and Social Network Analysis answer should be shorter, more specific and easier for a campaign team to review.
What this chapter covers
- 01
Nodes, edges and network communities
- 02
Degree and betweenness centrality
- 03
Closeness, eigenvector centrality and density
- 04
Influencer fit, disclosure and measurement
Worked example · free
Compare a connector and a broker
- 1Ari has the higher degree because Ari has more direct ties.
- 1Bo may have higher betweenness because Bo connects separate groups.
- 1Choose Ari for visibility within the cluster when audience fit holds.
- 1Choose Bo for cross-community flow when brokerage serves the objective.
Key terms
- Degree centrality
- The number of direct ties attached to a node.
- Betweenness centrality
- How often a node lies on shortest paths between other nodes.
Influencers and Social Network Analysis FAQ
Does the most connected account make the best influencer?
No. Degree measures direct ties, but the objective may require brokerage, network access or prestige instead. Any structural score also needs checks for audience overlap, credibility, activity and creative fit.
Exam move
Sketch small networks and compute degree by inspection, then identify bridge positions. For each result, state the campaign objective it serves and one qualitative reason the numerical ranking could mislead.
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