Peer and company influence on consumer responses and brand perceptions in company social network sites

This project focuses on an analysis of a large MySQL database that contains the content of interactions on company or brand Facebook pages, collected through the Facebook API. The database is made available by Social Embassy, a leading Dutch social media agency, and contains the profiles of 2 million consumers and their interactions with to 35k brand posts of over 50 brands: 7 million likes and 2.5 million comments. Read more…

The idea is to design a graph based prediction system for social networks where users comment on brand pages. The objective is to analyze and visualize users’ comments in a graph ​-based fashion. We hypothesise it is possible to predict users’ comments based on the graph’s structure.
Firstly, we build a graph representation of users commenting on pages. Nodes are brands pages, edges represent users cross ​posting over the nodes. Then, we consider the structure of users’ cross ​-comments across pages. In particular, we highlight some highly connected sets of pages (cliques) that show a good amount of cross- ​posting activity. We outline a comments
prediction system based on cliques, we claim it can predict the evolution of a clique in terms of users comments. Furthermore, we describe how the system can be validated using historical data.
The hypothesis is how users present in a clique q can be used to predict future comments within the same clique. Specifically, users that are in q and have commented on some pages of q are expected to comment on pages where they have not commented yet. The validation of such system can be done by considering historical data. Given the cliques at time t 0, for each clique we generate the predictions and we compare them against what we observe at time t 1. This is possible since the data has timestamps and spans over 3 years of social network activities.