WikiPop: personalized event detection system based on Wikipedia page view statistics

@article{Ciglan2010WikiPopPE,
  title={WikiPop: personalized event detection system based on Wikipedia page view statistics},
  author={Marek Ciglan and Kjetil N{\o}rv{\aa}g},
  journal={Proceedings of the 19th ACM international conference on Information and knowledge management},
  year={2010}
}
  • M. Ciglan, K. Nørvåg
  • Published 26 October 2010
  • Computer Science
  • Proceedings of the 19th ACM international conference on Information and knowledge management
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… 

Figures from this paper

Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information

TLDR
This work proposes a novel model that complements traditional text-based approaches by rewarding entities that exhibit a high temporal correlation with topics during their burst time period by exploiting temporal information from the Wikipedia edit history and page view logs.

Extracting Event-Related Information from Article Updates in Wikipedia

TLDR
An in-depth analysis of event-related updates in Wikipedia by examining different indicators for events including language, meta annotations, and update bursts and finding promising results towards generating entity-specific news tickers and timelines.

Temporal summarization of event-related updates in wikipedia

TLDR
Wikipedia Event Reporter is presented, a web-based system that supports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates.

WikipEvent: Leveraging Wikipedia Edit History for Event Detection

TLDR
This work introduces a new method to extract complex event structures from Wikipedia using WikipEvent using Co-References method, and proposes a new model to represent events by engaging multiple entities, generalizable to an arbitrary language.

Wikipedia as a time machine

TLDR
The main content and meta-data temporal signals available along with illustrative analysis are discussed and the source and nature of each signal are discussed, and any issues that may complicate extraction and use are discussed.

Characterising Concepts of Interest Leveraging Linked Data and the Social Web

TLDR
A novel algorithm for computing specificity leveraging the semantics of Linked Data is described and evaluated and the impact of the model on user profiles of interests is evaluated.

CrowdTiles: presenting crowd-based information for event-driven information needs

TLDR
A novel approach to presenting a summary of new information and users related to recent or ongoing events associated with the user's search topic, therefore aiding most recent information discovery is proposed.

A Probabilistic Model for Time-Aware Entity Recommendation

TLDR
This paper proposes the first probabilistic model that takes time-awareness into consideration for entity recommendation by leveraging heterogeneous knowledge of entities extracted from different data sources publicly available on the Web.

Indexing and analyzing wikipedia's current events portal, the daily news summaries by the crowd

TLDR
The results show that WCEP has reached a stable state in terms of the volume of contributions as well as the size of its crowd, which makes it an important source of news summaries for the public and the research community.

Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic Assessment

TLDR
This work introduces a new approach for extracting complex structures of events from Wikipedia and advocates a new model to represent events by engaging more than one entities that are generalizable to an arbitrary language.
...

References

SHOWING 1-3 OF 3 REFERENCES

Learning to Find Interesting Connections in Wikipedia

TLDR
This paper uses a modified Spreading Activation algorithm to identify connections between input concepts and proposes two approaches for link weighting and shows a strong correlation between used weighting methods and user preferences.

Mining Meaning from Wikipedia

Application of Spreading Activation Techniques in Information Retrieval

  • F. Crestani
  • Computer Science
    Artificial Intelligence Review
  • 2004
TLDR
This paper surveys the use of Spreading Activation techniques on Semantic Networks in Associative Information Retrieval and critically analyzed a number of works in this area to study the relevance of Sp spreading Activation for associative IR.