1,406 research outputs found
Exploring Social Media for Event Attendance
Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event
Fusing Text and Image for Event Detection in Twitter
In this contribution, we develop an accurate and effective event detection
method to detect events from a Twitter stream, which uses visual and textual
information to improve the performance of the mining process. The method
monitors a Twitter stream to pick up tweets having texts and images and stores
them into a database. This is followed by applying a mining algorithm to detect
an event. The procedure starts with detecting events based on text only by
using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Then it detects the event
based on image only by using visual features including histogram of oriented
gradients (HOG) descriptors, grey-level cooccurrence matrix (GLCM), and color
histogram. K nearest neighbours (Knn) classification is used in the detection.
The final decision of the event detection is made based on the reliabilities of
text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.94, comparing with 0.89 with
texts only, and 0.86 with images only.Comment: 9 Pages, 4 figuer
Visual analytics of location-based social networks for decision support
Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds
Geo-Information Harvesting from Social Media Data
As unconventional sources of geo-information, massive imagery and text
messages from open platforms and social media form a temporally quasi-seamless,
spatially multi-perspective stream, but with unknown and diverse quality. Due
to its complementarity to remote sensing data, geo-information from these
sources offers promising perspectives, but harvesting is not trivial due to its
data characteristics. In this article, we address key aspects in the field,
including data availability, analysis-ready data preparation and data
management, geo-information extraction from social media text messages and
images, and the fusion of social media and remote sensing data. We then
showcase some exemplary geographic applications. In addition, we present the
first extensive discussion of ethical considerations of social media data in
the context of geo-information harvesting and geographic applications. With
this effort, we wish to stimulate curiosity and lay the groundwork for
researchers who intend to explore social media data for geo-applications. We
encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
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