Effects of disaster characteristics on twitter event signature
Open accessAbstract
Twitter has emerged as a platform that is heavily used during disasters. Therefore, as an event unfolds, it generates varying levels of online engagement from victims as well as onlookers (both physical and virtual). Because methods for mining disaster-related content at scale must contend with the problem of filtering out vast numbers of unrelated posts, any prior knowledge about the characteristics of disaster-related content in the live Twitter feed may help improve the recovery of relevant posts. In this study, we consider the relative abundance of a disasters Twitter content over time (both relative to total event-related content and relative to the overall volume of content generated on Twitter). We refer to this time-varying abundance as the events signature. In an analysis of three different disasters, we find that event signatures are qualitatively different. These differences can be explained in terms of several characteristics of disasters: foreknowledge, duration, severity, and news media engagement.
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The characteristics of crisis events leave a distinctive "print" on social media with respect to time and duration, including variations in the kind of information being posted and by whom.
“the characteristics of crisis events leave a distinctive “print” on social media with respect to time and duration, including variations in the kind of information being posted and by whom”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Saleem et al., 2014
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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“, 2017b); just as the characteristics of crisis events leave a distinctive “print” on social media with respect to time and duration, including variations in the kind of information being posted and by whom (Saleem et al., 2014; Olteanu et al., 2015).”
BackgroundSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
- Authors
- Saleem, H. M., Xu, Y., & Ruths, D.
- Affiliations
- McGill University (CA)
- Corresponding author
- Saleem, Haji Mohammad
- ORCID
- Ruths, Derek
- Venue
- Procedia Engineering
- Publisher
- Elsevier BV
- Published
- 2014
- Volume
- 78
- Pages
- 165-172
- ISSN
- 1877-7058 (ISSN-L)
- OpenAlex
- W2036616070
- Cited by
- 19
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 1 · 2015: 2
- FWCI
- 4.23
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Engineering (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Communication › Public Relations and Crisis Communication
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- disaster characteristics, disaster severity
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