Computational Modeling of 2-sided Message’s Effects on Perceived Argument Strength

The aim of this research is studying of 2-sided message’s effects on persuasiveness of anti-drug messages by computational modeling method. It’s been done for getting more effective and more persuasive messages. Persuasiveness of messages is measured be perceived argument strength of them which is determined by audiences. In this research, according to formative researches, a method for measuring the perceived argument strength was developed. Then 3 types of messages with 3 different arguments and frames was used which 2 types of 2-sided messages (Anti/Pro and Pro/Anti) and 1 type of single-side messages was examined. The messages were displayed to the audiences through on-line questionnaires. Audiences were involved 10 persons with drug abusing experiences and 36 persons without any drug related experiences. In the questionnaires, audiences were asked about effectiveness of messages on them and their close friends. The comparison of means and standard deviations was done for 3 types of messages which ranked according their perceived argument strengths by 2 groups (with/without drug experiences) of audiences. In order to modeling the effects of factors other than (types of message and drug experience), mixed effects models were run to the data. The results showed that 2-sided messages are more persuasive than 1-sided messages and 2-sided messages with anti/pro frames are most effective messages to audiences with or without drug abusing experiences. In this type of messages, if first side of message be consistent with audience’s beliefs, then it will act like mental anchor and increases effectiveness of messages.