Measuring Brand Favorability Using Large-Scale Social Media Data

For decades, brand managers have monitored brand health with the use of consumer surveys, which have been refined to address issues related to sampling bias, response bias, leading questions, etc. However, with the advance of Web 2.0 and the internet, consumers have turned to social media to express their opinions on a variety of topics and, subsequently, have generated an extremely large amount of interaction data with brands. Analyzing these publicly available data to measure brand health has attracted great research attention. In this study, we focus on developing a method to measure brand favorability while accounting for the measure biases exhibited by social media posters. Specifically, we propose a probabilistic graphical model–based collective inference framework and implement a block-based Markov chain Monte Carlo sampling technique to obtain an adjusted brand favorability measure that is correlated with traditional survey-based measures used by brands. For analysis, we collect and examine Facebook data for more than 3,300 brands and about 205 million unique users that interact with those brands via their Facebook brand pages. Our data set is large and contains 6.68 billion likes and full text for 1.01 billion user comments, creating challenges for any modeling efforts. We evaluate the effectiveness of our model via out-of-sample prediction, external ground truth testing, and simulation. All demonstrate that our model performs very well, providing brand managers with a new method to more accurately measure consumer opinions toward the brand using social media data.