  Given a social network, where each user is associated with a selection cost,
the problem of \textsc{Budgeted Influence Maximization} (\emph{BIM Problem} in
short) asks to choose a subset of them (known as seed users) within an
allocated budget whose initial activation leads to the maximum number of
influenced nodes. Existing Studies on this problem do not consider the
tag-specific influence probability. However, in reality, influence probability
between two users always depends upon the context (e.g., sports, politics,
etc.). To address this issue, in this paper we introduce the
\textsc{Tag\mbox{-}Based Budgeted Influence Maximization problem} (\emph{TBIM
Problem} in short), where along with the other inputs, a tag set (each of them
is also associated with a selection cost) is given, each edge of the network
has the tag specific influence probability, and here the goal is to select
influential users as well as influential tags within the allocated budget to
maximize the influence. Considering the fact that real-world campaigns targeted
in nature, we also study the \textsc{Earned Benefit Maximization} Problem in
tag specific influence probability setting, which formally we call the
\textsc{Tag\mbox{-}Based Earned Benefit Maximization problem} (\emph{TEBM
Problem} in short). For this problem along with the inputs of the TBIM Problem,
we are given a subset of the nodes as target users, and each one of them is
associated with a benefit value that can be earned by influencing them.
Considering the fact that different tag has different popularity across the
communities of the same network, we propose three methodologies that work based
on \emph{effective marginal influence gain computation}. The proposed
methodologies have been analyzed for their time and space requirements.
