Exploring brands that customers are likely to purchase jointly has a profound effect on marketing. This study proposes a new way to measure, or estimate the similarity between brands using social media. The proposed algorithm analyzes the daily photos and hashtags posted by each brand's followers. By clustering them and converting them into histogram-based features, we can calculate the similarity between brands. We evaluate our proposed algorithm by comparing it with the purchase logs of point/credit card companies, and answers to the questionnaires. The results show that purchase logs can predict the co-purchase behaviors in the questionnaires very well, but cannot predict customers' potential interest or willingness to buy products from new brands. On the other hand, our method can predict the users’ interest in brands with a correlation coefficient of over 0.53, which is high considering that such interest in brands is highly subjective and individual dependent.