Identifying Topic-Specific Opinion Leaders Through Graph Embedding
Summary
In social networks, some users have an exceptional ability to affect the opinions
and behaviours of others. Such people are known as opinion leaders. Accurately identifying opinion leaders can assist greatly in the study of information
flow throughout social networks, in addition to providing valuable insights for
marketing purposes. Furthermore, contemporary graph embedding tools can
significantly improve the process of identifying opinion leaders. Despite this,
little research has focused on combining graph embedding and opinion leader
detection. Consequently, this thesis focuses on research that integrates these
two areas together. I do this by first creating graph embeddings that capture
the information contained in the data. Then, I feed these embeddings to an
opinion leader detection algorithm that is designed to use the information captured by the embeddings to create a ranking of users, with the highest-ranking
users being the designated opinion leaders. I compare these results with several
benchmarks of opinion leader detection methods that do not use graph embeddings. The quality of opinion leaders is similar for each method, though not all models designate the same users as opinion leaders. To conclude, I discuss
this research in the broader context of the field of graph embedding and opinion
leader detection, and provide suggestions for further research.