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Author  |
Mehdi Ben Lazreg; Usman Anjum; Vladimir Zadorozhny; Morten Goodwin |
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Title |
Semantic Decay Filter for Event Detection |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
14-26 |
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Keywords |
String Metric, Event Detection, Crisis Management. |
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Abstract |
Peaks in a time series of social media posts can be used to identify events. Using peaks in the number of posts and keyword bursts has become the go-to method for event detection from social media. However, those methods suffer from the random peaks in posts attributed to the regular daily use of social media. This paper proposes a novel approach to remedy that problem by introducing a semantic decay filter (SDF). The filter's role is to eliminate the random peaks and preserve the peak related to an event. The filter combines two relevant features, namely the number of posts and the decay in the number of similar tweets in an event-related peak. We tested the filter on three different data sets corresponding to three events: the STEM school shooting, London bridge attacks, and Virginia beach attacks. We show that, for all the events, the filter can eliminate random peaks and preserve the event-related peaks. |
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Address |
Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-2 |
ISBN |
2411-3388 |
Medium |
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Track |
AI Systems for Crisis and Risks |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
mehdi.ben.lazreg@uia.no |
Approved |
no |
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Call Number |
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Serial |
2203 |
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Author |
Usman Anjum; Vladimir Zadorozhny; Prashant Krishnamurthy |
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Title |
TBAM: Towards An Agent-Based Model to Enrich Twitter Data |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Volume |
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Issue |
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Pages |
146-158 |
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Keywords |
Agent-Based Model, Twitter, Modeling and Simulation, Event Detection |
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Abstract |
Twitter is widely being used by researchers to understand human behavior, e.g. how people behave when an event occurs and how it changes their microblogging pattern. The changing microblogging behavior can have an important application in the form of detecting events. However, the Twitter data that is available has limitations in it has incomplete and noisy information and has irregular samples. In this paper we create a model, calledTwitter Behavior Agent-Based Model (TBAM)to simulate Twitter pattern and behavior using Agent-Based Modeling(ABM). The generated data can be used in place or to complement the real-world data and improve the accuracy of event detection. We confirm the validity of our model by comparing it with real data collected from Twitter |
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Address |
University of Pittsburgh; University of Pittsburgh; University of Pittsburgh |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Volume |
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Edition |
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ISSN |
978-1-949373-61-5 |
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Track |
Analytical Modeling and Simulation |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
usa3@pitt.edu |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2321 |
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Author |
Vladimir Zadorozhny; Pei-Ju Lee; Michael Lewis |
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Title |
Collaborative Information Sensemaking for Multi-Robot Search and Rescue |
Type |
Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Issue |
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Pages |
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Keywords |
crowdsoursing; information fusion; Mobile robots; search and rescue mission; sensemaking |
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Abstract |
In this paper, we consider novel information sensemaking methods for search and rescue operations that combine principles of information fusion and collective intelligence in scalable solutions. We will elaborate on several approaches that originated in different areas of information integration, sensor data management, and multi-robot urban search and rescue missions. |
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Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
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Track |
Decision Support Systems |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
yes |
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Call Number |
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Serial |
1289 |
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