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Development and Evaluation of Multi-Agent Models Predicting Twitter Trends in Multiple Domains

Published: 25 August 2015 Publication History

Abstract

This paper concerns multi-agent models predicting Twitter trends. We use a step-wise approach to develop a novel agent-based model with the following properties: (1) it uses individual behavior parameters for a set of Twitter users and (2) it uses a retweet graph to model the underlying social network structure of these Twitter users to predict trends. The model parameters can be optimized using empirical data. To investigate to what extend this agent-based model can predict Twitter trends, we validate the model performance on two case studies using real Twitter data: tweets on banks and tweets on universities. We furthermore compare a version of the model that only uses the retweet graph (PM1) with the model that also simulates individual behavior (PM2) for small to larger prediction time intervals. For both case studies the results show that PM1 performs better for small prediction time intervals (up to one day in the future), while PM2 performs better for larger time intervals (from a day to a week). We think this opens up the possibility to use similar models for helping organizations to extend their monitoring capabilities of social media with predictive modeling and to become more pro-active and less reactive.

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  • (2018)Trend square: An Android Application for Extracting Twitter Trends Based on Location2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)10.1109/ICCTCT.2018.8551056(1-5)Online publication date: Mar-2018
  1. Development and Evaluation of Multi-Agent Models Predicting Twitter Trends in Multiple Domains

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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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      Author Tags

      1. Banking Sector
      2. Multi-Agent Models
      3. Trend Prediction
      4. Twitter
      5. Universities

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      View all
      • (2020)Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction frameworkSocial Network Analysis and Mining10.1007/s13278-019-0620-810:1Online publication date: 7-Jan-2020
      • (2019)Evaluating the Business Impacts of Social Media Use With System Dynamics and Agent-Based ModelingSocial Entrepreneurship10.4018/978-1-5225-8182-6.ch076(1479-1491)Online publication date: 2019
      • (2018)Trend square: An Android Application for Extracting Twitter Trends Based on Location2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)10.1109/ICCTCT.2018.8551056(1-5)Online publication date: Mar-2018

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