Identifying and Classifying Influencers in Twitter only with Textual Information

Online Reputation Management systems aim at identifying and classifying Twitter influencers due to their importance for brands. Current methods mainly rely on metrics provided by Twitter such as followers, retweets, etc. In this work we follow the research initiated at RepLab 2014, but relying only on the textual content of tweets. Moreover, we have proposed a workflow to identify influencers and classify them into an interest group from a reputation point of view, besides the classification proposed at RepLab. We have evaluated two families of classifiers, which do not require feature engineering, namely: deep learning classifiers and traditional classifiers with embeddings. Additionally, we also use two baselines: a simple language model classifier and the “majority class” classifier. Experiments show that most of our methods outperform the reported results in RepLab 2014, especially the proposed Low Dimensionality Statistical Embedding.