Anandhavelu Natarajan

Will your facebook post be engaging

Social media has become the ideal platform for promotional activities of organizations. However, due to the volatility of social media, the wrong message posted at the wrong time can result in significant damage to hard-built brand image. This calls for a mechanism to gauge the reactions a post will evoke from a given social community. The community can vary from customers of a particular brand to brand loyalists interacting through its social pages (for example, on Facebook). In this paper, we focus on learning the communitys reaction from past posts and providing a predictive model for gauging the reaction of the community before the post is published. This helps the marketer take better-informed decisions. Short-text posts in social media leads to a sparse feature space, we propose additional meta-features that improve reaction modeling. Given the feature representation, we discuss the possibility of casting the underlying problem under different paradigms - classification, regression and learning to rank. We study the performances of each of these paradigms on real data from Facebook. We will discuss the challenges involved, and ways to mitigate them, in addition to our observations, results and insights.

Community reaction: from blogs to Facebook

Online social media is all pervasive in this digitally connected world. It provides a great platform to share information and news, and have public discussions on these topics. These interactions happen on owned-sites as well as on earned social media. But it is reasonable to hypothesize that the communities on the various platforms will engage differently. We explore this hypothesis using data collected from a blog as well as their Facebook page. We observe significant differences between the blog and Facebook as two mediums. First, we see how the topic of the post leads to differences in how posts are received on the two mediums. We next characterize the distribution of the time to comments, displaying how blog and Facebook differ. Finally, we describe how the sentiment of posts and popular entities differ across mediums. In conclusion, reactions across the two mediums are so diverse that it calls for different strategies across different social mediums, we provide our recommendations towards this goal.