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Forecasting High Tide: Predicting Times of Elevated Activity in Online Social Media

Published: 25 August 2015 Publication History

Abstract

Social media provides a powerful platform for influencers to broadcast content to a large audience of followers. In order to reach the greatest number of users, an important first step is to identify times when a large portion of a target population is active on social media, which requires modeling the behavior of those individuals. We propose three methods for behavior modeling: a simple seasonality approach based on time-of-day and day-of-week, an autoregressive approach based on aggregate fluctuations from seasonality, and an aggregation-of-individuals approach based on modeling the behavior of individual users. We test these methods on data collected from a set of users on Twitter in 2011 and 2012. We find that the performance of the methods at predicting times of high activity depends strongly on the tradeoff between true and false positives, with no method dominating. Our results highlight the challenges and opportunities involved in modeling complex social systems, and demonstrate how influencers interested in forecasting potential user engagement can use complexity modeling to make better decisions.

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cover image ACM Conferences
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
August 2015
835 pages
ISBN:9781450338547
DOI:10.1145/2808797
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2015

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View all
  • (2022)Timestamp analysis of mental health tweets of Twitter users along with COVID-19 confirmed casesProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545543(1-6)Online publication date: 7-Aug-2022
  • (2019)Identifying Influencers using Time Series Analysis2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS.2019.8931833(21-27)Online publication date: Oct-2019
  • (2019)Panel Discussion: Moving Social‐Behavioral Modeling ForwardSocial‐Behavioral Modeling for Complex Systems10.1002/9781119485001.ch33(753-787)Online publication date: 29-Mar-2019
  • (2019)Theory‐Interpretable, Data‐Driven Agent‐Based ModelingSocial‐Behavioral Modeling for Complex Systems10.1002/9781119485001.ch15(337-357)Online publication date: 29-Mar-2019
  • (2017)Finding influential sources and breaking news in news media using graph analysis techniquesInternational Journal of Web Engineering and Technology10.1504/IJWET.2017.08644912:2(143-164)Online publication date: 1-Jan-2017
  • (2017)Network-Based Modeling for Characterizing Human Collective Behaviors During Extreme EventsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.260865847:1(171-183)Online publication date: Jan-2017
  • (2017)Prediction of Elevated Activity in Online Social Media Using Aggregated and Individualized ModelsTrends in Social Network Analysis10.1007/978-3-319-53420-6_7(169-187)Online publication date: 30-Apr-2017

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