Editor’s note: We are pleased to welcome Linchi Kwok, assistant professor of Hospitality Management in the David B. Falk College of Sport and Human Dynamics at Syracuse University, whose research interests include social media and its business implications, organizational behavior, and service operations. Dr. Kwok and Dr. Bei Yu, also of Syracuse University, published “Spreading Social Media Messages on Facebook: An Analysis of Restaurant Business-to-Consumer Communications” on September 24 in Cornell Hospitality Quarterly.
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Want to Spread Your Social Media Messages on Facebook?
This Study May Help
Being a phenomenologist and a practitioner of social media, I see Facebook as one of the most important means for B2C (business-to-consumer) 
communications. When a Facebook user likes, posts comments, or shares content with their Facebook credentials, an update will appear on this person’s wall, helping companies rapidly spread information. Thus, companies must pay close attention to Facebook users’ reactions to the messages they send on Facebook. Facebook users’ endorsement of a message can be very important in indicating the effectiveness of a company’s social media strategy.
Dr. Yu and I adopted the text mining techniques to identify the type(s) of Facebook that are endorsed (and thus propagated) by Facebook users. We analyzed 982 Facebook messages initiated by 10 restaurant chains and two independent operators, of which were among the top restaurants in terms of sales volume and number of Facebook fans. We found the following results: the “more popular” messages, which receive more “Likes” and comments, contain keywords about the restaurant (e.g., menu descriptions); the “less popular” messages seem to involve with sales and marketing. Dividing the messages into four media types (i.e., status, link, video, and photo), photo and status receive more “Likes” and comments. To dig further, we coded the messages into two message types, namely sales/marketing and conversational messages, which do not directly sell or promote the restaurants. As compared to sales and marketing messages, conversational messages receive more “Likes” and comments even though they only account for one third of the messages in this study. There is also a cross-effect of media type and message type on the number of comments a message received.
Based on the research findings, we outlined several detailed practical tactics in this paper to help companies improve their use of Facebook. Theoretically, the findings of this study provide ground work for developing a defined typology of Facebook messages and an automatic text classifier with the machine learning techniques.
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