Emerging event detection in social networks with location sensitivity

Unankard, Sayan; Li, Xue; Sharaf, Mohamed A.

doi:10.1007/s11280-014-0291-3

Cited by 94 publications

(42 citation statements)

References 26 publications

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“…Research on topic detection and tracking (TDT) over microblogs has attracted great attention in recent years, due to its wide applications in emergency detection like earthquake [28], geo-location detection [29], political election outcomes prediction [30], and so on. In this section, we first briefly summarize the related work and then present the differences between existing work and ours.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic method for emerging topic tracking in Microblog stream

et al. 2016

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Microblog is a popular and open platform for discovering and sharing the latest news about social issues and daily life. The quickly-updated microblog streams make it urgent to develop an effective tool to monitor such streams. Emerging topic tracking is one of such tools to reveal which new events are attracting the most online attention at present. However, due to the fast changing, high noise and short length of microblog feeds, two challenges should be addressed in emerging topic tracking. One is the problem of detecting emerging topics early, long before they become hot, and the other is how to effectively monitor evolving topics over time. In this study, we propose a novel emerging topics tracking method, which aligns emerging word detection from temporal perspective with coherent topic mining from spatial perspective. Specifically, we first design metrics to estimate word novelty and fading based on local weighted linear regression (LWLR), which can highlight emerging-topic-relevant words and suppress existing-topic-relevant words. We then track emerging topics by leveraging topic novelty and fading probabilities, which are learnt by designing and solving an optimization problem. We evaluate our method on a microblog stream containing over one million feeds. Experimental results show the promising performance of the proposed method in detecting emerging topic and tracking topic evolution over time on both effectiveness and efficiency.

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Section: Related Workmentioning

confidence: 99%

A probabilistic method for emerging topic tracking in Microblog stream

et al. 2016

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“…This research [107] was published in the proceedings of the 15th Asia-Pacific Web Conference (APWeb) 2013 and the extended version [108] was published in the World Wide Web Journal (WWWJ), 2014.…”

Section: Location-based Emerging Event Detection In Social Networkmentioning

confidence: 99%

“…The notion of event detection was proposed in our recent work [108] for location-based hotspot emerging events. However, the problem that we address in this work is how to group a set of microblog messages into a cluster (or sub-event) for a particular longer-running event (i.e., an election).…”

Section: Sub-event Detection For a Particular Eventmentioning

confidence: 99%

“…With the number of sub-events being unknown in advance, we applied event detection using hierarchical clustering from our previous work [108] with some modifications. We use a sliding window to divide the messages.…”

Section: Sub-event Detection For a Particular Eventmentioning

confidence: 99%

“…It is difficult for people to comprehend a large number of messages in a chronological order and to monitor an event as it unfolds. Although traditional techniques such as clustering are able to capture major events in social networks [114,56,108], it is difficult to capture the incidental events that may or may not be relevant to the current focused event. For example, when a natural disaster has just occurred, people may initially talk about the natural phenomenon that they have just witnessed.…”

Section: Problems and Challengesmentioning

confidence: 99%

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Event detection in social networks

Unankard¹

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Micro-blog services such as Twitter generate a large amount of messages carrying event information and users' opinions over a wide range of topics. The events discussed on social networks can be associated with topics, locations, and time periods. The events can be a variety, such as celebrities or political affairs, local social events, accidents, protests, or natural disasters. Messages are posted by users after they have experienced or witnessed the events happening in the real-world and they want to share their experiences immediately. People also express themselves spontaneously with respect to the social events in their social networks. Alternatively, policy-makers may want to know the feelings of users for a particular event to make informed decisions. With the increasing number of real-world events that are originated and discussed over social networks, event detection and tracking is becoming a compelling research issue. However, the traditional approaches to event detection and event tracking on large text streams are not applicable because of the following problems. First, they are not designed to deal with a large number of short and noisy messages. Second, social networks contain network structures such as friends, followers, replies, and re-tweets. Third, social network messages are associated with locations, which can be either senders' current locations or event locations. Fourth, each message is also associated with a timestamp. Messages often contain revealing and timely event information however, traditional text processing approaches assume documents are non-temporal. Moreover, given a particular time frame and a location the user is interested in, events that occurred in the given time frame from the chosen area are more valuable than others. Finding localized events has not been well studied yet.The goals of this thesis are to: (1) identify subsistent problems and challenges in event detection and tracking in streaming micro-blog text, (2) design approaches for event detection and event tracking in social networks, (3) design approaches for sentiment analysis for given event topics, and (4) evaluate the proposed approaches in real-world streaming datasets.In this thesis, our research is considered in three parts. Firstly, in order to detect emerging events from a large number of short and noisy messages, we propose an approach for the early detection of emerging hotspot events in social networks with location sensitivity. An algorithm is designed for slang conversion, synonym expansion and conceptual similarity to provide a rich semantic context for measuring message similarity to improve clustering results. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the hotspot emerging events. A sliding window manager is used to keep track of messages arriving in the system. The size of the sliding window is defined as the time interval. We evaluate our...

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