A Data-driven Study of Influences in Twitter Communities

This paper presents a quantitative study of Twitter, one of the most popular micro-blogging services, from the perspective of user influence. We crawl several datasets from the most active communities on Twitter and obtain 20.5 million user profiles, along with 420.2 million directed relations and 105 million tweets among the users. User influence scores are obtained from influence measurement services, Klout and PeerIndex. Our analysis reveals interesting findings, including non-power-law influence distribution, strong reciprocity among users in a community, the existence of homophily and hierarchical relationships in social influences. Most importantly, we observe that whether a user retweets a message is strongly influenced by the first of his followees who posted that message. To capture such an effect, we propose the first influencer (FI) information diffusion model and show through extensive evaluation that compared to the widely adopted independent cascade model, the FI model is more stable and more accurate in predicting influence spreads in Twitter communities.Comment: 11 page

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016

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