2015/04/16 by Nir Levine, Levine, Nir, Timothy Mann +4
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing #Social and Information Networks (cs.SI) #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.48550/arxiv.1504.04114
arxiv created 2015/04/16 · openalex publication_date 2015/04/16 · arxiv updated 2015/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Twitter, a popular social network, presents great opportunities for on-line machine learning research. However, previous research has focused almost entirely on learning from passively collected data. We study the problem of learning to acquire followers through normative user behavior, as opposed to the mass following policies applied by many bots. We formalize the problem as a contextual bandit problem, in which we consider retweeting content to be the action chosen and each tweet (content) is accompanied by context. We design reward signals based on the change in followers. The result of our month long experiment with 60 agents suggests that (1) aggregating experience across agents can adversely impact prediction accuracy and (2) the Twitter community's response to different actions is non-stationary. Our findings suggest that actively learning on-line can provide deeper insights about how to attract followers than machine learning over passively collected data alone.