| event detection|
(Alternative names for this keyword)
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of keywords|
event detection is included as keyword or extra keyword in 0 datasets, 0 tools and 6 publications.
There is no datasets for this keyword.
There is no tools for this keyword.
|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|MJ no more: Using concurrent wikipedia edit spikes with social network plausibility checks for breaking news detection||Steiner T.
Van Hooland S.
|WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web||English||2013||We have developed an application called Wikipedia Live Monitor that monitors article edits on different language versions of Wikipedia-as they happen in realtime. Wikipedia articles in different languages are highly interlinked. For example, the English article "en:2013 Russian meteor event" on the topic of the February 15 meteoroid that exploded over the region of Chelyabinsk Oblast, Russia, is interlinked with ", the Russian article on the same topic. As we monitor multiple language versions of Wikipedia in parallel, we can exploit this fact to detect concurrent edit spikes of Wikipedia articles covering the same topics, both in only one, and in different languages. We treat such concurrent edit spikes as signals for potential breaking news events, whose plausibility we then check with full-text cross-language searches on multiple social networks. Unlike the reverse approach of monitoring social networks first, and potentially checking plausibility on Wikipedia second, the approach proposed in this paper has the advantage of being less prone to falsepositive alerts, while being equally sensitive to true-positive events, however, at only a fraction of the processing cost. A live demo of our application is available online at the URL http://wikipedia-irc. herokuapp.com/, the source code is available under the terms of the Apache 2.0 license at https://github.com/tomayac/wikipedia-irc.||0||0|
|Temporal summarization of event-related updates in wikipedia||Georgescu M.
|WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web||English||2013||Wikipedia is a free multilingual online encyclopedia cover- ing a wide range of general and specific knowledge. Its con- tent is continuously maintained up-to-date and extended by a supporting community. In many cases, real-world events inuence the collaborative editing of Wikipedia articles of the involved or affected entities. In this paper, we present Wikipedia Event Reporter, a web-based system that sup- ports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates. For a given entity, the system first identifies peaks of update activities for the entity using burst detec- tion and automatically extracts event-related updates using a machine-learning approach. Further, the system deter- mines distinct events through the clustering of updates by exploiting different types of information such as update time, textual similarity, and the position of the updates within an article. Finally, the system generates the meaningful tem- poral summarization of event-related updates and automat- ically annotates the identified events in a timeline.||0||0|
|Visitpedia: Wiki article visit log visualization for event exploration||Sun Y.
|Proceedings - 13th International Conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2013||English||2013||This paper proposes an interactive visualization tool, Visitpedia, to detect and analyze social events based on Wikipedia visit history. It helps users discover real-world events behind the data and study how these events evolve over time. Different from previous work based on on-line news or similar text corpora, we choose Wikipedia visit counts as our data source since the visit count data better reflect user concerns of social events. We tackle the event-based task from a time-series pattern perspective rather than semantic perspective. Various visualization and user interaction techniques are integrated in Visitpedia. Two case studies are conducted to demonstrate the effectiveness of Visitpedia.||0||0|
|Bieber no more: First Story Detection using Twitter and Wikipedia||Miles Osborne
|English||2012||Twitter is a well known source of information regarding breaking news stories. This aspect of Twitter makes it ideal for identifying events as they happen. However, a key problem with Twitter-driven event detection approaches is that they produce many spurious events, i.e., events that are wrongly detected or simply are of no interest to anyone. In this paper, we examine whether Wikipedia (when viewed
as a stream of page views) can be used to improve the quality of discovered events in Twitter. Our results suggest that Wikipedia is a powerful filtering mechanism, allowing for easy blocking of large numbers of spurious events. Our results also indicate that events within Wikipedia tend to lagbehind Twitter.
|Twevent: Segment-based event detection from tweets||Chenliang Li
|ACM International Conference Proceeding Series||English||2012||Event detection from tweets is an important task to understand the current events/topics attracting a large number of common users. However, the unique characteristics of tweets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for well written documents (e.g. news articles) cannot be directly adopted. In this paper, we propose a segment-based event detection system for tweets, called Twevent. Twevent first detects bursty tweet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tweet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tweets containing the segment published within that time window. The similarity between a pair of bursty segments is computed using their associated tweets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events. We evaluate Twevent and compare it with the state-of-the-art method using 4.3 million tweets published by Singapore-based users in June 2010. In our experiments, Twevent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Twevent can be easily interpreted with little background knowledge because of the newsworthy segments. We also show that Twevent is efficient and scalable, leading to a desirable solution for event detection from tweets.||0||0|
|WikiPop - Personalized event detection system based on Wikipedia page view statistics||Marek Ciglan
|International Conference on Information and Knowledge Management, Proceedings||English||2010||In this paper, we describe WikiPop service, a system designed to detect significant increase of popularity of topics related to users' interests. We exploit Wikipedia page view statistics to identify concepts with significant increase of the interest from the public. Daily, there are thousands of articles with increased popularity; thus, a personalization is in order to provide the user only with results related to his/her interest. The WikiPop system allows a user to define a context by stating a set of Wikipedia articles describing topics of interest. The system is then able to search, for the given date, for popular topics related to the user defined context.||0||1|