Abstract
This study aims to identify the features of Market-Generated Content (MGC) tweets that are posted with a purpose of initiating electronic word of mouth (known as eWOM). A successful tweet is a tweet that earns active participation from the customers, e.g. getting Retweeted (RT) or Favorited (FAV). The phenomenon of eWOM effectively helps to propagate marketing agendas in both short-term marketing campaigns and long-term brand awareness. In the analysis process, logistic regression and Association rule methods were applied to mine the significant features on the MGC posts which were collected from four selected banks in Thailand during a specific period of time. For results, logistic regression indicated a set of features that causes a substantial number of RT and FAV. Additionally, the Apriori algorithm of the Association rule further specified two key features for effective RT and FAV, and it also suggested how to combine other features with those two key features to enhance the gain of RT and FAV.Keywords: electronic Word of Mouth, eWOM, Marketer-Generated Content, MGC, Logistic regression, Association rule, Social media, Twitter mining.
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