  Judgment aggregation is a framework to aggregate individual opinions on
multiple, logically connected issues into a collective outcome. These opinions
are cast by judges, which can be for example referees, experts, advisors or
jurors, depending on the application and context. It is open to manipulative
attacks such as \textsc{Manipulation} where judges cast their judgments
strategically. Previous works have shown that most computational problems
corresponding to these manipulative attacks are \NP-hard. This desired
computational barrier, however, often relies on formulas that are either of
unbounded size or of complex structure.
  We revisit the computational complexity for various \textsc{Manipulation} and
\textsc{Bribery} problems in premise-based judgment aggregation, now focusing
on simple and realistic formulas. We restrict all formulas to be clauses that
are (positive) monotone, Horn-clauses, or have bounded length. For basic
variants of \textsc{Manipulation}, we show that these restrictions make several
variants, which were in general known to be \NP-hard, polynomial-time solvable.
Moreover, we provide a P vs.\ NP dichotomy for a large class of clause
restrictions (generalizing monotone and Horn clauses) by showing a close
relationship between variants of \textsc{Manipulation} and variants of
\textsc{Satisfiability}. For Hamming distance based \textsc{Manipulation}, we
show that \NP-hardness even holds for positive monotone clauses of length
three, but the problem becomes polynomial-time solvable for positive monotone
clauses of length two. For \textsc{Bribery}, we show that \NP-hardness even
holds for positive monotone clauses of length two, but it becomes
polynomial-time solvable for the same clause set if there is a constant budget.
