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A conceptual framework for studying collective reactions to events in location-based social media
Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events
Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
Many social Web sites allow users to annotate the content with descriptive
metadata, such as tags, and more recently to organize content hierarchically.
These types of structured metadata provide valuable evidence for learning how a
community organizes knowledge. For instance, we can aggregate many personal
hierarchies into a common taxonomy, also known as a folksonomy, that will aid
users in visualizing and browsing social content, and also to help them in
organizing their own content. However, learning from social metadata presents
several challenges, since it is sparse, shallow, ambiguous, noisy, and
inconsistent. We describe an approach to folksonomy learning based on
relational clustering, which exploits structured metadata contained in personal
hierarchies. Our approach clusters similar hierarchies using their structure
and tag statistics, then incrementally weaves them into a deeper, bushier tree.
We study folksonomy learning using social metadata extracted from the
photo-sharing site Flickr, and demonstrate that the proposed approach addresses
the challenges. Moreover, comparing to previous work, the approach produces
larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on
Knowledge Discovery and Data Mining(KDD) 201
Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining
One of the main problems that emerges in the classic approach to semantics is
the difficulty in acquisition and maintenance of ontologies and semantic
annotations. On the other hand, the Internet explosion and the massive
diffusion of mobile smart devices lead to the creation of a worldwide system,
which information is daily checked and fueled by the contribution of millions
of users who interacts in a collaborative way. Search engines, continually
exploring the Web, are a natural source of information on which to base a
modern approach to semantic annotation. A promising idea is that it is possible
to generalize the semantic similarity, under the assumption that semantically
similar terms behave similarly, and define collaborative proximity measures
based on the indexing information returned by search engines. The PMING
Distance is a proximity measure used in data mining and information retrieval,
which collaborative information express the degree of relationship between two
terms, using only the number of documents returned as result for a query on a
search engine. In this work, the PMINIG Distance is updated, providing a novel
formal algebraic definition, which corrects previous works. The novel point of
view underlines the features of the PMING to be a locally normalized linear
combination of the Pointwise Mutual Information and Normalized Google Distance.
The analyzed measure dynamically reflects the collaborative change made on the
web resources
Reverse spatial visual top-k query
With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top- query (RSVQ k ) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top- queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2 -Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2 -Tree, called CVR 2 -Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2 -Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently
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