Detecting Emergent Conflicts through Web Mining and Visualization

Johansson, Fredrik; Brynielsson, Joel; Hörling, Pontus; Malm, Michael; Mårtenson, Christian; Truvé, Staffan; Rosell, Magnus

doi:10.1109/eisic.2011.21

Cited by 8 publications

(6 citation statements)

References 18 publications

(14 reference statements)

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“…In the same manner, it can be used to assess the emotions expressed by, e.g., bloggers, in order to try to identify signs of emergent conflicts before they actually take place (cf. [16,29]). Similarly, the tool and algorithms described in this article could also be adapted to be used for evaluating the effects of information campaigns or psychological operations during military missions [30].…”

Section: Discussionmentioning

confidence: 99%

Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises

et al. 2014

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One of the key factors influencing how people react to and behave during a crisis is their digital or non-digital social network, and the information they receive through this network. Publicly available online social media sites make it possible for crisis management organizations to use some of these experiences as input for their decision-making. We describe a methodology for collecting a large number of relevant tweets and annotating them with emotional labels. This methodology has been used for creating a training dataset consisting of manually annotated tweets from the Sandy hurricane. Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets. Results show that a support vector machine achieves the best results with about 60% accuracy on the multi-classification problem. This classifier has been used as a basis for constructing a decision support tool where emotional trends are visualized. To evaluate the tool, it has been successfully integrated with a pan-European alerting system, and demonstrated as part of a crisis management concept during a public event involving relevant stakeholders.

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Section: Discussionmentioning

confidence: 99%

Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises

et al. 2014

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Section: Discussionmentioning

confidence: 94%

Learning to classify emotional content in crisis-related tweets

Brynielsson

Johansson

Westling

2013

2013 IEEE International Conference on Intelligence and Security Informatics

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Social media is increasingly being used during crises. This makes it possible for crisis responders to collect and process crisis-related user generated content to allow for improved situational awareness. We describe a methodology for collecting a large number of relevant tweets and annotating them with emotional labels. This methodology has been used for creating a training data set consisting of manually annotated tweets from the Sandy hurricane. Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets. Results show that a support vector machine achieves the best results (60% accuracy on the multi-classification problem).

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“…Several scholars have noted that publicly available data on the Internet can be systematically used for intelligence purposes [18,40]. It has been observed that some of the automated data processing techniques used for systematic processing of corporate data, i.e., business intelligence, have migrated into security intelligence [10], and that the further development of these techniques has the potential to provide a competitive edge [12,31,42]. There are, however, challenges as of how to find and extract relevant and meaningful pieces of information relative to the task at hand.…”

Section: Related Workmentioning

confidence: 99%

Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

Varga

Brynielsson

Horndahl

et al. 2020

Lecture Notes in Social Networks

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With the availability of an abundance of data through the Internet, the premises to solve some intelligence analysis tasks have changed for the better. The study presented herein sets out to examine whether and how a data-driven approach can contribute to solve intelligence tasks. During a full day observational study, an ordinary military intelligence unit was divided into two uniform teams. Each team was independently asked to solve the same realistic intelligence analysis task. Both teams were allowed to use their ordinary set of tools, but in addition one team was also given access to a novel text analysis prototype tool specifically designed to support data-driven intelligence analysis of social media data. The results, obtained from the case study with a high ecological validity, suggest that the prototype tool provided valuable insights by bringing forth information from a more diverse set of sources, specifically from private citizens that would not have been easily discovered otherwise. Also, regardless of its objective contribution, the capabilities and the usage of the tool were embraced and subjectively perceived as useful by all involved analysts.

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Detecting Emergent Conflicts through Web Mining and Visualization

Cited by 8 publications

References 18 publications

Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises

Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises

Learning to classify emotional content in crisis-related tweets

Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

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