5,383 research outputs found
Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling
Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue
and Zika in Brasil and other tropical regions has long been a priority for
governments in affected areas. Streaming social media content, such as Twitter,
is increasingly being used for health vigilance applications such as flu
detection. However, previous work has not addressed the complexity of drastic
seasonal changes on Twitter content across multiple epidemic outbreaks. In
order to address this gap, this paper contrasts two complementary approaches to
detecting Twitter content that is relevant for Dengue outbreak detection,
namely supervised classification and unsupervised clustering using topic
modelling. Each approach has benefits and shortcomings. Our classifier achieves
a prediction accuracy of about 80\% based on a small training set of about
1,000 instances, but the need for manual annotation makes it hard to track
seasonal changes in the nature of the epidemics, such as the emergence of new
types of virus in certain geographical locations. In contrast, LDA-based topic
modelling scales well, generating cohesive and well-separated clusters from
larger samples. While clusters can be easily re-generated following changes in
epidemics, however, this approach makes it hard to clearly segregate relevant
tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano,
Switzerlan
Recommended from our members
Extracting Personal Behavioral Patterns from Geo-Referenced Tweets
This paper presents an exploratory study of the potential of geo-referenced Twitter data for extracting knowledge about significant personal places, behaviors and potential interests of people. The study was done analysing two months’ worth of tweets from residents of the greater Seattle area
Recommended from our members
Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned
Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions
Clustering Memes in Social Media
The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM'13), 201
STV-based Video Feature Processing for Action Recognition
In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
- …