  In this paper, we propose a generalized opinion dynamics model (GODM), which
can dynamically compute each person's expressed opinion, to solve the internal
opinion maximization problem for social trust networks. In the model, we
propose a new, reasonable and interpretable confidence index, which is
determined by both person's social status and the evaluation around him. By
using the theory of diagonally dominant, we obtain the optimal analytic
solution of the Nash equilibrium with maximum overall opinion. We design a
novel algorithm to maximize the overall with given budget by modifying the
internal opinions of people in the social trust network, and prove its
optimality both from the algorithm itself and the traditional optimization
algorithm-ADMM algorithms with $l_1$-regulations. A series of experiments are
conducted, and the experimental results show that our method is superior to the
state-of-the-art in four datasets. The average benefit has promoted $67.5\%$,
$83.2\%$, $31.5\%$, and $33.7\%$ on four datasets, respectively.
