Provenance-aware sentiment analysis and multi-criteria influencer selection: a decision-support framework demonstrated for Uganda Airlines

Abstract

Brands increasingly rely on creators to reach audiences, yet follower scale alone does not represent brand fit, audience relevance, engagement quality, or the emotional context of public discourse. This thesis develops and demonstrates a provenance-aware decision- support framework that combines descriptive sentiment context with evidence-aware, multi-criteria influencer ranking. Uganda Airlines provides the management scenario, while the empirical baseline is an observed legacy aviation-discourse dataset rather than a complete record of Uganda Airlines’ own social-media activity. After tweet-ID deduplication, the observed baseline contains 14,485 unique tweets collected from 17 to 24 February 2015. Of these records, 62.70% are negative, 21.19% neutral, and 16.11% positive. A separate scenario layer contains 125,200 simulated records covering 2015–2026. The resulting 139,685-record union is therefore described as simulation-augmented; its longitudinal patterns are scenario results and not indepen- dently observed trends. The influencer campaign panel contains 683 observed and 60,000 simulated rows across 95 name–platform profiles representing 37 distinct influencer names. Sixty-four profiles meet the default eligibility threshold of at least five observed posts. Influencer selection is operationalised through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The ranking criteria are value alignment, audience fit, sentiment context, engagement quality, reach, and cost e"ciency. The integrated top ten has no profiles in common with a reach-only top ten and two in common with a follower-count-only top ten, showing that the implemented ordering di!ers from scale-only selection. Criterion ablations identify engagement quality and value alignment as the most influential preferences in the current ordering. These are deterministic ranking results, not campaign-e!ect estimates. The simulated layer is reserved for stress testing rather than increasing empirical confidence. The thesis contributes (i) a reusable, values-aware framework for linking social listening to influencer selection; (ii) a transparent method for separating observed evidence from simulation-based scenario analysis; and (iii) a working dashboard that exposes provenance, evidence thresholds, criterion contributions, and ranking sensitivity. The findings support managerial exploration and monitoring, but do not establish causal campaign uplift or externally validated real-world predictive accuracy. Keywords: social listening; sentiment analysis; simulation augmentation; brand–creator alignment; influencer selection; TOPSIS; decision-support dashboard.