15. A method implemented on a computing device for mining data from discussion threads having a plurality of authors of individual posts in a set of posts within the discussion threads, comprising: using the computing device to perform the following: computing a first semantic term that mathematically represents a post in the discussion thread as a combination of several topics, where each topic contains several words, the first semantic term given by an equation: 
 ∥ D−XΘ∥ F 2 , where F is a number of topics, and where, 
 D={{right arrow over (d)} (1) , . . . , {right arrow over (d)} (L) }, d is a post and L is a number of posts, Θ is a coefficient matrix given by, 
 Θ={{right arrow over (θ)} (1) . . . {right arrow over (θ)} (L) }, where θ is a topic coefficient, and X is a topic space given by, 
 X={{right arrow over (x)} (1) , . . . ,{right arrow over (x)} (T) }, for a topic x and a given topic number T; computing a second semantic term that use a semantic sparse constraint to mathematically represent that a post may be only related to a limited topic and the coefficients of other topics are zero, the second semantic term given by an equation: 
 ∥{right arrow over (θ)} (i) ∥1, for the i th post; computing a first structure term mathematically representing that if a post replies to some previous post of the discussion thread then the topic of a current post can be similar to a combination of topics of previous posts, the first structure term given by an equation:  θ → ( i ) - ∑ k = 1 i - 1 ⁢ b → k ( i ) · θ → ( k )  F 2 , where {right arrow over (b)} k (i) is the structural coefficient between the i th post and the k th post; computing a second structure term that uses a structure sparse constraint to mathematically represent that a post may be only related to a limited previous post and the coefficients of other posts are zero, the second structure term given by an equation: 
 ∥{right arrow over (b)} (i) ∥1, where b is a structural coefficient for an i th post; linearly combining the first semantic term, the second semantic term, the first structure term, and the second structure term to generate a simultaneous semantic and structure model that simultaneously models semantic and structure of the discussion threads, the simultaneous semantic and structure model given by an equation: f = ⁢  D - X ⁢ ⁢ Θ  F 2 + λ 1 ⁢ ∑ i = 1 L ⁢  θ → ( i )  1 + λ 2 ⁢ ∑ i = 1 L ⁢  θ → ( i ) - ∑ k = 1 i - 1 ⁢ b k ( i ) · θ → ( k )  F 2 + λ 3 ⁢ ∑ i = 1 L ⁢  b → ( i )  1 , where λ 1 , λ 2 , and λ 3 , are optimization parameters; and using the simultaneous semantic and structure model in at least one application to mine the discussion threads for data.
15. A method implemented on a computing device for mining data from discussion threads having a plurality of authors of individual posts in a set of posts within the discussion threads, comprising: using the computing device to perform the following: computing a first semantic term that mathematically represents a post in the discussion thread as a combination of several topics, where each topic contains several words, the first semantic term given by an equation: 
 ∥ D−XΘ∥ F 2 , where F is a number of topics, and where, 
 D={{right arrow over (d)} (1) , . . . , {right arrow over (d)} (L) }, d is a post and L is a number of posts, Θ is a coefficient matrix given by, 
 Θ={{right arrow over (θ)} (1) . . . {right arrow over (θ)} (L) }, where θ is a topic coefficient, and X is a topic space given by, 
 X={{right arrow over (x)} (1) , . . . ,{right arrow over (x)} (T) }, for a topic x and a given topic number T; computing a second semantic term that use a semantic sparse constraint to mathematically represent that a post may be only related to a limited topic and the coefficients of other topics are zero, the second semantic term given by an equation: 
 ∥{right arrow over (θ)} (i) ∥1, for the i th post; computing a first structure term mathematically representing that if a post replies to some previous post of the discussion thread then the topic of a current post can be similar to a combination of topics of previous posts, the first structure term given by an equation:  θ → ( i ) - ∑ k = 1 i - 1 ⁢ b → k ( i ) · θ → ( k )  F 2 , where {right arrow over (b)} k (i) is the structural coefficient between the i th post and the k th post; computing a second structure term that uses a structure sparse constraint to mathematically represent that a post may be only related to a limited previous post and the coefficients of other posts are zero, the second structure term given by an equation: 
 ∥{right arrow over (b)} (i) ∥1, where b is a structural coefficient for an i th post; linearly combining the first semantic term, the second semantic term, the first structure term, and the second structure term to generate a simultaneous semantic and structure model that simultaneously models semantic and structure of the discussion threads, the simultaneous semantic and structure model given by an equation: f = ⁢  D - X ⁢ ⁢ Θ  F 2 + λ 1 ⁢ ∑ i = 1 L ⁢  θ → ( i )  1 + λ 2 ⁢ ∑ i = 1 L ⁢  θ → ( i ) - ∑ k = 1 i - 1 ⁢ b k ( i ) · θ → ( k )  F 2 + λ 3 ⁢ ∑ i = 1 L ⁢  b → ( i )  1 , where λ 1 , λ 2 , and λ 3 , are optimization parameters; and using the simultaneous semantic and structure model in at least one application to mine the discussion threads for data. 20. The method of claim 15 , further comprising: recovering an implicit reply structure from the discussion threads using the simultaneous semantic and structure model; constructing a reply network using the implicit reply structure, where the reply network contains a plurality of authors; ranking the plurality of authors using a ranking technique based on a ranking value computed by the ranking technique; determining whether the ranking value is greater than or equal to an expert threshold value; if not, then determining that an author is not an expert; and if so, then designating the author an expert.