Exploring Influencer Dynamics and Information Flow in a Local Restaurant Social Network

Food influencers are popular within the digital community associated with the food industry. Given that they can easily reach their target audiences by spreading messages on social media, businesses prefer to collaborate with influencers to achieve business-oriented goals. This practice is known as influencer marketing. The critical factor for successful influencer marketing efforts lies in identifying the right influencers. The aim of this study is to identify potential influencers in a social network, measure the impact on their followers’ restaurant preferences, and analyze the flow of information within their ego network for promoting food industry. Using scraped data related to users who write comments on restaurants, as well as their followers, followings, and restaurants located in the Alsancak district of İzmir province from Zomato, we generated a directed single large graph database. Utilizing social network analysis (SNA) centrality measurements on this graph database, we identified potential influencers. After identifying potential influencers, we applied ego network analysis to measure the impact of these influencers on their followers’ restaurant preferences and to analyze the flow of information within their ego network. Our findings point out the importance of the following factors: the number of photographs uploaded, the number of comments, and the number of followers to be considered when identifying potential influencers in a social network. By understanding these determining factors, marketers and businesses can strategically collaborate with influential users to enhance their brand presence and effectively target their desired audience.