Discussion among individuals about a given issue often induces polarization and bipolarization effects, i.e. individuals radicalize their initial opinion towards either the same or opposite directions. Experimental psychologists have put forward Persuasive Arguments Theory (PAT) as a clue for explaining polarization. PAT claims that adding novel and persuasive arguments pro or contra the debated issue is the major cause for polarization. Recent developments in abstract argumentation provide the tools for capturing these intuitions on a formal basis. Here Bipolar Argumentation Frameworks (BAF) are employed as a tool for encoding the information of agents in a debate relative to a given issue a. A probabilistic extension of BAF allows to encode the likelihood of the opinions pro or contra a before and after information exchange. It is shown, by a straightforward example, how these measures provide the basis to capture the intuitions of PAT.