Detecting Real-World Influence Through Twitter
This work addresses the challenge of accurately identifying influential individuals on social media for applications in marketing and social analysis, though it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of detecting real-world influence from Twitter accounts by showing that common metrics like retweets and followers are insufficient, and proposed machine learning approaches using NLP and social network analysis to classify and rank influencers, achieving state-of-the-art results on the CLEF RepLab 2014 dataset.
In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.