Signed Link Prediction with Sparse Data: The Role of Personality Information
This work addresses signed link prediction for social network analysis, but it is incremental as it builds on existing methods by adding personality information.
The study tackled the problem of signed link prediction in social networks with sparse data, especially when negative links are scarce, by incorporating user personality information derived from social media. The proposed model demonstrated the complementary role of personality information, showing effectiveness in alleviating data sparsity on two real-world datasets.
Predicting signed links in social networks often faces the problem of signed link data sparsity, i.e., only a small percentage of signed links are given. The problem is exacerbated when the number of negative links is much smaller than that of positive links. Boosting signed link prediction necessitates additional information to compensate for data sparsity. According to psychology theories, one rich source of such information is user's personality such as optimism and pessimism that can help determine her propensity in establishing positive and negative links. In this study, we investigate how personality information can be obtained, and if personality information can help alleviate the data sparsity problem for signed link prediction. We propose a novel signed link prediction model that enables empirical exploration of user personality via social media data. We evaluate our proposed model on two datasets of real-world signed link networks. The results demonstrate the complementary role of personality information in the signed link prediction problem. Experimental results also indicate the effectiveness of different levels of personality information for signed link data sparsity problem.