The variety of engaging interactions among users in social medial distinguishes it from traditional Web media.

Such a feature should be utilized while attempting to provide intelligent services to social media participants.

One of the most important observation from this work is that semantic contents of user comments can play a fairly different role in a different form of social media .

This simulates the scenario of recommending relevant news from traditional media to social media users for their further reading.

In this part, we explore the contribution of user authority and comments in social media recommender.

In a more general context, Web is one of the most important carriers for “social media” , e. g. In-

Various engaging interactions among users in social media differentiate it from traditional Web sites.

Such characteristics should be utilized in attempt to provide intelligent services to social media users.

Most recent researches on information recommendation in social media focus on the blogosphere.

different social media corpora (Section 4).

Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora.

Obviously, these natural QA pairs are usually created during people’s communication via Internet social media , among which we are interested in the community-driven

In this paper, a novel approach for modeling the semantic relevance for QA pairs in the social media sites is proposed.

As mentioned above, the user generated questions and their answers via social media are always short texts.

Our motivation of finding the homogenous question-answer corpora from different kind of social media is to guarantee the model’s performance and avoid hand-annotating work.

It indicates that the word distributions of the two corpora are quite similar, although they come from different social media sites.

The task of detecting answers in social media corpora suffers from the problem of feature sparsity seriously.

(2009) both propose the strategies to detect questions in the social media corpus, which is proved to be a nontrivial task.

Due to the feature sparsity and the low word frequency of the social media corpus, it is difficult to model the semantic relevance between questions and answers using only co-occurrence features.

In the social media corpora, the answers are always descriptive, containing one or several sentences.

The Quality assessing component itself could be built as a module that can be adjusted to the kind of Social Media in use; the creation of customized Quality feature spaces would make it possible to handle different sources of UGC (forums, collaborative authoring websites such as Wikipedia, blogs etc.).

Community Question Answering (cQA) portals are an example of Social Media where the information need of a user is expressed in the form of a question for which a best answer is picked among the ones generated by other users.

Interestingly, a great amount of information is embedded in the metadata generated as a byproduct of users’ action and interaction on Social Media .

Quality assessing of information available on Social Media had been studied before mainly as a binary classification problem with the objective of detecting low quality content.