Predicting Successful Memes using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM 2014

Discovering items with potential popularity on social media

Predicting the future popularity of online content is highly important in many applications. Preferential attachment phenomena is encountered in scale free networks.Under it's influece popular items get more popular thereby resulting in long tailed distribution problem. Consequently, new items which can be popular (potential ones), are suppressed by the already popular items. This paper proposes a novel model which is able to identify potential items. It identifies the potentially popular items by considering the number of links or ratings it has recieved in recent past along with it's popularity decay. For obtaining an effecient model we consider only temporal features of the content, avoiding the cost of extracting other features. We have found that people follow recent behaviours of their peers. In presence of fit or quality items already popular items lose it's popularity. Prediction accuracy is measured on three industrial datasets namely Movielens, Netflix and Facebook wall post. Experimental results show that compare to state-of-the-art model our model have better prediction accuracy.Comment: 7 pages in ACM style.7 figures and 1 tabl