In the Internet age, there are numerous platforms for disseminating text, images, and videos. Content with wide visibility are mostly shared by influential accounts on these platforms. Influence is commonly measured by number of accounts following or subscribed to content shared by those influencers. Here, we quantify the changes in influence, popularity, and productivity of accounts for a period. We model account trajectories using a stochastic model to uncouple effects of popularity and productivity from individual qualities quantified as social impact factor for each account.

Here we define a metric, Q_T ,for Twitter user's ability to compose messages that engages its audience. The higher your Q_T, the more likely that any of your followers will find your message of sufficient interest to retweet it to their followers. Q_T is independent of the follower number, allowing us to meaningfully compare engagement for accounts of widely different popularity. Check mine at @onurvarol

News@Northeastern prepared a great coverage of our work and they designed an interactive story. You can find it here

Many real-world prediction tasks have outcome (a.k.a. target or response) variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have accurate predictions. Most existing approaches are not dedicated to learning heavy-tailed distribution; thus, they heavily under-predict such instances. To tackle this problem, we introduce Learning to Place (L2P) , which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances. L2P consists of two stages. In Stage 1, L2P learns a pairwise preference classifier: is instance A > instance B?. In Stage 2, L2P learns to place a new instance into an ordinal ranking of known instances. Based on its placement, the new instance is then assigned a value for its outcome variable. Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and capability to reproduce heavy-tailed outcome distribution. In addition, L2P can provide an interpretable model with explainable outcomes by placing each predicted instance in context with its comparable neighbors.

More information about L2P methodology and code, please visit project website.