  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.
