In recent years, online social networks are among the most popu-lar websites with high PV (Page View) all over the world, as they have renewed the way for information discovery and distribution. Millions of users have registered on these websites and hence gen-erate formidable amount of user-generated contents every day. The social networks become "giants", likely eligible to carry on any research tasks. However, we have pointed out that these giants still suffer from their "Achilles Heel", i.e., extreme sparsity [34, 32]. Compared with the extremely large data over the whole col-lection, individual posting documents such as microblogs seem to be too sparse to make a difference under various research scenar-ios, while actually these postings are different. In this paper we propose to tackle the Achilles Heel of social networks by smooth-ing the language model via influence propagation. To further our previously proposed work to tackle the sparsity issue, we extend the socialized language model smoothing with bi-directional influ-ence learned from propagation. Intuitively, it is insufficient not to distinguish the influence propagated between information source and target without directions. Hence, we formulate a bi-directional socialized factor graph model, which utilizes both the textual cor-relations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and so-cial interactions. These factors are modeled as attributes and de-pendencies among documents and their corresponding users, and then are distinguished on the direction level. We propose an effec-tive learning algorithm to learn the proposed factor graph model with directions. Finally we propagate term counts to smooth doc-uments based on the estimated influence. We run experiments on two instinctive datasets of Twitter and Weibo. The results validate the effectiveness of the proposed model. By incorporating direction information into the socialized language model smoothing, our ap-proach obtains improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of per-plexity, nDCG and MAP measurements.