Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling
Table 3: Topics by authors
['Recommended', '1st', '2nd', '3rd']
In addition to inference on the topic distribution of each document, the TN topic model allows us to analyze the topic distribution of each author. We illustrate the use of the TN topic model for author recommendation. On a new test dataset with 90451 tweets and 625 new authors, we predict the most similar and dissimilar authors for the new authors, based on the training model of 60370 tweets. We quantify the recommendation quality with the cosine similarities of the authors’ topic distributions for the recommended author pairs. This suggests that our kernel function is more appropriate. Additionally, we manually checked the recommended authors and we found that they usually belong to the same community, i.e., having tweets with similar topics.