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Forecasting topic activity with exogenous and endogenous information signals in Twitter

Published: 19 January 2022 Publication History

Abstract

Modeling social media activity has numerous practical implications such as designing and testing intervention techniques to mitigate disinformation or delivering critical information during disaster relief operations. In this paper we propose a modeling technique that forecasts topic-specific daily volume of social media activities by using both exogenous signals, such as news or armed conflicts records, and endogenous data from the social media platform we model. Empirical evaluations with real datasets from Twitter on two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.

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Cited By

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  • (2024)Spatial Analysis of Social Media’s Proxies for Human Emotion and CognitionWisdom, Well-Being, Win-Win10.1007/978-3-031-57860-1_13(175-185)Online publication date: 10-Apr-2024
  • (2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
  • (2023)Experimental evaluation of baselines for forecasting social media timeseriesEPJ Data Science10.1140/epjds/s13688-023-00383-912:1Online publication date: 27-Mar-2023

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          cover image ACM Conferences
          ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
          November 2021
          693 pages
          ISBN:9781450391283
          DOI:10.1145/3487351
          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: 19 January 2022

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          Author Tags

          1. social media
          2. time series forecasting

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          ASONAM '21 Paper Acceptance Rate 22 of 118 submissions, 19%;
          Overall Acceptance Rate 116 of 549 submissions, 21%

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          View all
          • (2024)Spatial Analysis of Social Media’s Proxies for Human Emotion and CognitionWisdom, Well-Being, Win-Win10.1007/978-3-031-57860-1_13(175-185)Online publication date: 10-Apr-2024
          • (2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
          • (2023)Experimental evaluation of baselines for forecasting social media timeseriesEPJ Data Science10.1140/epjds/s13688-023-00383-912:1Online publication date: 27-Mar-2023
          • (2022)Social media activity forecasting with exogenous and endogenous signalsSocial Network Analysis and Mining10.1007/s13278-022-00927-312:1Online publication date: 8-Aug-2022

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