Kaufhold, Marc-André ; Rupp, Nicola ; Reuter, Christian ; Habdank, Matthias (2019):
Mitigating information overload in social media during conflicts and crises: design and evaluation of a cross-platform alerting system.
In: Behaviour & Information Technology (BIT), 38 (3), pp. 319-342. Taylor & Francis, ISSN 0144-929X, e-ISSN 1362-3001,
DOI: 10.1080/0144929X.2019.1620334,
[Article]
Abstract
The research field of crisis informatics examines, amongst others, the potentials and barriers of social media use during conflicts and crises. Social media allow emergency services to reach the public easily in the context of crisis communication and receive valuable information (e.g. pictures) from social media data. However, the vast amount of data generated during large-scale incidents can lead to issues of information overload and quality. To mitigate these issues, this paper proposes the semi-automatic creation of alerts including keyword, relevance and information quality filters based on cross-platform social media data. We conducted empirical studies and workshops with emergency services across Europe to raise requirements, then iteratively designed and implemented an approach to support emergency services, and performed multiple evaluations, including live demonstrations and field trials, to research the potentials of social media-based alerts. Finally, we present the findings and implications based on semi-structured interviews with emergency services, highlighting the need for usable configurability and white-box algorithm representation.
Item Type: | Article |
---|---|
Erschienen: | 2019 |
Creators: | Kaufhold, Marc-André ; Rupp, Nicola ; Reuter, Christian ; Habdank, Matthias |
Title: | Mitigating information overload in social media during conflicts and crises: design and evaluation of a cross-platform alerting system |
Language: | English |
Abstract: | The research field of crisis informatics examines, amongst others, the potentials and barriers of social media use during conflicts and crises. Social media allow emergency services to reach the public easily in the context of crisis communication and receive valuable information (e.g. pictures) from social media data. However, the vast amount of data generated during large-scale incidents can lead to issues of information overload and quality. To mitigate these issues, this paper proposes the semi-automatic creation of alerts including keyword, relevance and information quality filters based on cross-platform social media data. We conducted empirical studies and workshops with emergency services across Europe to raise requirements, then iteratively designed and implemented an approach to support emergency services, and performed multiple evaluations, including live demonstrations and field trials, to research the potentials of social media-based alerts. Finally, we present the findings and implications based on semi-structured interviews with emergency services, highlighting the need for usable configurability and white-box algorithm representation. |
Journal or Publication Title: | Behaviour & Information Technology (BIT) |
Volume of the journal: | 38 |
Issue Number: | 3 |
Publisher: | Taylor & Francis |
Uncontrolled Keywords: | A-Paper, HCI, KontiKat, SocialMedia, x3 Wissenschaft und Technik für Frieden und Sicherheit PEASEC |
Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Science and Technology for Peace and Security (PEASEC) DFG-Collaborative Research Centres (incl. Transregio) DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres Profile Areas Profile Areas > Cybersecurity (CYSEC) LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CRISP - Center for Research in Security and Privacy Zentrale Einrichtungen Zentrale Einrichtungen > Interdisziplinäre Arbeitsgruppe Naturwissenschaft, Technik und Sicherheit (IANUS) DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet |
Date Deposited: | 22 Aug 2019 13:55 |
DOI: | 10.1080/0144929X.2019.1620334 |
URL / URN: | https://www.tandfonline.com/doi/full/10.1080/0144929X.2019.1... |
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