相关论文: SMERC: Social media event response clustering usin…

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into…

Detecting events by using social media has been an active research problem. In this work, we investigate and compare the performance of two methods for event detection in Twitter by using Apache Storm as the stream processing…

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and…

Twitter is one of the most popular microblogging services in the world. The great amount of information within Twitter makes it an important information channel for people to learn and share news. Twitter hashtag is an popular feature that…

The unprecedented use of social media through smartphones and other web-enabled mobile devices has enabled the rapid adoption of platforms like Twitter. Event detection has found many applications on the web, including breaking news…

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of…

Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated…

Events detected from social media streams often include early signs of accidents, crimes or disasters. Therefore, they can be used by related parties for timely and efficient response. Although significant progress has been made on event…

Twitter stream has become a large source of information for many people, but the magnitude of tweets and the noisy nature of its content have made harvesting the knowledge from Twitter a challenging task for researchers for a long time.…

The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural…

Over the past decade humans have experienced exponential growth in the use of online resources, in particular social media and microblogging websites such as Facebook, Twitter, YouTube and also mobile applications such as WhatsApp, Line,…

Nowadays, Twitter has become a great source of user-generated information about events. Very often people report causal relationships between events in their tweets. Automatic detection of causality information in these events might play an…

Twitter is recognized as a crucial platform for the dissemination and gathering of Cyber Threat Intelligence (CTI). Its capability to provide real-time, actionable intelligence makes it an indispensable tool for detecting security events,…

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…

We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (marked by significance with respect to similarity with a…

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to…

In recent years, people spend a lot of time on social networks. They use social networks as a place to comment on personal or public events. Thus, a large amount of information is generated and shared daily in these networks. Using such a…

People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts…

Cluster analysis is a field of data analysis that extracts underlying patterns in data. One application of cluster analysis is in text-mining, the analysis of large collections of text to find similarities between documents. We used a…

Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales…