Online Social Networks Event Detection: A Survey
Today online social network services are challenging state-of-the-art social media mining algorithms and techniques due to its real-time nature, scale and amount of unstructured data generated. The continuous interactions between online social network par
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University of Porto, Porto, Portugal [email protected] INESC TEC Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal [email protected]
Abstract. Today online social network services are challenging stateof-the-art social media mining algorithms and techniques due to its realtime nature, scale and amount of unstructured data generated. The continuous interactions between online social network participants generate streams of unbounded text content and evolutionary network structures within the social streams that make classical text mining and network analysis techniques obsolete and not suitable to deal with such new challenges. Performing event detection on online social networks is no exception, state-of-the-art algorithms rely on text mining techniques applied to pre-known datasets that are being processed with no restrictions on the computational complexity and required execution time per document analysis. Moreover, network analysis algorithms used to extract knowledge from users relations and interactions were not designed to handle evolutionary networks of such order of magnitude in terms of the number of nodes and edges. This specific problem of event detection becomes even more serious due to the real-time nature of online social networks. New or unforeseen events need to be identified and tracked on a real-time basis providing accurate results as quick as possible. It makes no sense to have an algorithm that provides detected event results a few hours after being announced by traditional newswire. Keywords: Event detection
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· Social networks
Introduction
Today, online social networking services like Twitter [102], Facebook [99], Google+ [100], LinkedIn [101], among others, play an important role in the dissemination of information on a real-time basis [91]. Recent observation proves that some events and news emerge and spread first using those media channels rather than other traditional media like the online news sites, blogs or even television and radio breaking news [50,88]. Natural disasters, celebrity news, products announcements, or mainstream event coverage show that people increasingly make use of those tools to be informed, discuss and c Springer International Publishing Switzerland 2016 S. Michaelis et al. (Eds.): Morik Festschrift, LNAI 9580, pp. 1–41, 2016. DOI: 10.1007/978-3-319-41706-6 1
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M. Cordeiro and J. Gama
exchange information [38]. Empirical studies [50,88] show that the online social networking service Twitter is often the first medium to break important natural events such as earthquakes often in a matter of seconds after they occur. Being Twitter the “what’s-happening-right-now” tool [91] and given the nature of it’s data — an real-time flow of text messages (tweets) coming from very different sources covering varied kinds of subjects in distinct languages and locations — makes the Twitter public stream an example of an interesting source of data for “real time” event detection based on text mining techniques. Note that “real time”
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