![]() Specifically, several important types of clustering algorithms are first illustrated, including clustering, semi-supervised clustering, heterogeneous data co-clustering, and online clustering. 1.2 and presents the key branches of social media mining applications where clustering holds a potential. This chapter summarizes existing clustering and related approaches for the identified challenges as described in Sect. Experimental results confirm the quality and potential of our approach. We believe this is the first study to provide a generic model for describing semantic-aware events and their relationships extracted from social metadata on the Web. SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects’ descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM’s dimensions. The latter are required as the building blocks for event-based Collective Knowledge (CK) organization, where CK underlines the combination of all known data, information, and metadata concerning a given concept or event. To address this problem, we introduce a generic Social-based Event Detection, Description, and Linkage framework titled SEDDaL, taking as input: a collection of social media objects from heterogeneous sources (e.g., Flickr, YouTube, and Twitter), and producing as output a collection of semantically meaningful events interconnected with spatial, temporal, and semantic relationships. Yet, most of them do not capture the semantic meaning embedded in online social media data, which are usually highly heterogeneous and unstructured, and do not identify event relationships (e.g., car accident temporally occurs after storm, and geographically occurs near soccer match). Various methods have been put forward to perform automatic social-based event detection and description. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. The experimental results demon- strate that our approach is effective in detecting events from the Flickr photo collection. We evaluate the performance of our approach using a set of real data collected from Flickr. Finally, for each tag cluster, photos corresponding to the represented event are extracted. ![]() Afterwards, event-related tags are clustered such that each cluster, rep- resenting an event, consists of tags with similar temporal and locational distribution patterns as well as with simi- lar associated photos. Then, we identify tags related with events, and further distinguish between tags of aperiodic events and those of periodic events. ![]() In particular, the temporal and locational distributions of tag usage are analyzed in the first place, where a wavelet trans- form is employed to suppress noise. This paper presents our effort in detecting events from Flickr photos by exploiting the tags supplied by users to annotate photos. ![]() The problem is challenging considering: (1) Flickr data is noisy, because there are photos unrelated to real-world events (2) It is not easy to capture the content of photos. The results can be used to fa- cilitate user searching and browsing photos by events. Our focus in this pa- per is to detect events from photos on Flickr, an Internet image community website. Detecting events from web resources has attracted increas- ing research interests in recent years. ![]()
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