Extracting Criminal-Related Events from Arabic Tweets

2017 ◽  
Vol 10 (3) ◽  
pp. 34-47
Author(s):  
Feriel Abdelkoui ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Social Networks ◽  
Natural Hazards ◽  
Real World ◽  
Event Detection ◽  
Temporal Information ◽  
Automated Extraction ◽  
Temporal Expressions ◽  
Spatio Temporal ◽  
Event Based ◽  
F Measure

Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places and temporal expressions that appear in the tweet message, related tweeting time, and additional locations from the user's profile. The effectiveness of the system was evaluated in term of recall, precision and f-measure.

scholarly journals Digital coded exposure formation of frames from event-based imagery

2022 ◽  
Author(s):  
Andrew Gothard ◽  
Daniel Jones ◽  
Andre Green ◽  
Michael Torrez ◽  
Alessandro Cattaneo ◽  
...  
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Temporal Information ◽  
Hybrid Approaches ◽  
Event Driven ◽  
High Dynamic ◽  
Spatio Temporal ◽  
Processing Techniques ◽  
Event Based ◽  
Coded Exposure

Abstract Event-driven neuromorphic imagers have a number of attractive properties including low-power consumption, high dynamic range, the ability to detect fast events, low memory consumption and low band-width requirements. One of the biggest challenges with using event-driven imagery is that the field of event data processing is still embryonic. In contrast, decades worth of effort have been invested in the analysis of frame-based imagery. Hybrid approaches for applying established frame-based analysis techniques to event-driven imagery have been studied since event-driven imagers came into existence. However, the process for forming frames from event-driven imagery has not been studied in detail. This work presents a principled digital coded exposure approach for forming frames from event-driven imagery that is inspired by the physics exploited in a conventional camera featuring a shutter. The technique described in this work provides a fundamental tool for understanding the temporal information content that contributes to the formation of a frame from event-driven imagery data. Event-driven imagery allows for the application of arbitrary virtual digital shutter functions to form the final frame on a pixel-by-pixel basis. The proposed approach allows for the careful control of the spatio-temporal information that is captured in the frame. Furthermore, unlike a conventional physical camera, event-driven imagery can be formed into any variety of possible frames in post-processing after the data is captured. Furthermore, unlike a conventional physical camera, coded-exposure virtual shutter functions can assume arbitrary values including positive, negative, real, and complex values. The coded exposure approach also enables the ability to perform applications of industrial interest such as digital stroboscopy without any additional hardware. The ability to form frames from event-driven imagery in a principled manner opens up new possibilities in the ability to use conventional frame-based image processing techniques on event-driven imagery.

scholarly journals Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

2021 ◽  
Vol 15 (5) ◽  
pp. 1-33
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Yangqiu Song ◽  
Renyu Yang ◽  
Rajiv Ranjan ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Event Detection ◽  
Social Event ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Social Events ◽  
Meta Path ◽  
Social Event Detection ◽  
Event Based

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

scholarly journals Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 577
Author(s):  
Dojin Choi ◽  
Soobin Park ◽  
Dongho Ham ◽  
Hunjin Lim ◽  
Kyoungsoo Bok ◽  
...  
Keyword(s):  
Social Networks ◽  
Event Detection ◽  
Performance Evaluations ◽  
Data Dictionary ◽  
Detection Scheme ◽  
Geographical Data ◽  
Network Characteristics ◽  
Related Information ◽  
Event Based ◽  
Detection Schemes

In this paper, we propose a local event detection scheme by analyzing relevant documents in social networks to improve the accuracy of event detection. To detect local events by using geographical data, the proposed scheme embeds them using a geographical data dictionary and generates a weighted keyword graph using social network characteristics. The data left by users in social networks include not only postings but also related documents such as comments and threads. In this way, the proposed scheme detects a local event based on a keyword graph that is constructed through the analysis of the relevant documents. This can improve the accuracy of local event detection by analyzing relevant documents embedded with region-related information using a geographical data dictionary, without requiring users to tag geographic data. In order to verify the superiority of the proposed scheme, we compare it with the existing event detection schemes through various performance evaluations.

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

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