Volltext-Downloads (blau) und Frontdoor-Views (grau)

Identifying Key Opinion Leaders in Social Networks - An Approach to use Instagram Data to Rate and Identify Key Opinion Leader for a Specific Business Field

  • This thesis focuses on the identification of influential users, also known as key opinion leaders, within the social network Instagram. Instagram is a very popular platform to share images with the option to categorise the images by certain tags. It is possible to collect public data from Instagram via the open API of the platform. This thesis presents a concept to create an automated crawler for this API and col- lect data into a database in order to apply algorithms from graph theory to identify opinion leaders afterwards. The sample topic for this thesis has been veganfood and all associated posts from Instagram have been crawled. After the user data has been crawled a graph has been created to do further research with common social network analysis tools. The graph contained a total set of more than 26,000 nodes. To identify opinion leaders from this graph, five di↵erent metrics have been applied, in particular PageRank, Betweenness centrality, Closeness Centrality, Degree and Eigen- vector centrality. After applying the di↵erent algorithms the results have been eval- uated and additionally an marketing expert with focus on social media analysed the results. This project was able to figured out that it is possible to find opinion leaders by using the PageRank algorithm and that those opinion leaders have a very good value of en- gagement. This indicates that they show a high interaction with other users on their posts. In conclusion the additional research options are discussed to provide a future outlook.

Download full text files

  • Identifying Key Opinion Leaders in Social Networks - An Approach to use Instagram Data to Rate and Identify Key Opinion Leader for a Specific Business Field

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Christopher Egger
URN:urn:nbn:de:hbz:832-epub4-8450
Referee:Kristian Fischer
Document Type:Master's Thesis
Language:English
Publishing Institution:Hochschulbibliothek der Technischen Hochschule Köln
Granting Institution:Technische Hochschule Köln
Date of Publication (online):2016/05/04
Tag:Graph Theory; Social Network Analysis
Institutes:Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Advanced Media Institute
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft
Open Access:Open Access
Licence (German):License LogoEs gilt das UrhG