NDLI: Building a large-scale corpus for evaluating event detection on twitter

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Building a large-scale corpus for evaluating event detection on twitter

Content Provider	ACM Digital Library
Author	Jose, Joemon M. Moshfeghi, Yashar McMinn, Andrew J.
Abstract	Despite the popularity of Twitter for research, there are very few publicly available corpora, and those which are available are either too small or unsuitable for tasks such as event detection. This is partially due to a number of issues associated with the creation of Twitter corpora, including restrictions on the distribution of the tweets and the difficultly of creating relevance judgements at such a large scale. The difficulty of creating relevance judgements for the task of event detection is further hampered by ambiguity in the definition of event. In this paper, we propose a methodology for the creation of an event detection corpus. Specifically, we first create a new corpus that covers a period of 4 weeks and contains over 120 million tweets, which we make available for research. We then propose a definition of event which fits the characteristics of Twitter, and using this definition, we generate a set of relevance judgements aimed specifically at the task of event detection. To do so, we make use of existing state-of-the-art event detection approaches and Wikipedia to generate a set of candidate events with associated tweets. We then use crowdsourcing to gather relevance judgements, and discuss the quality of results, including how we ensured integrity and prevented spam. As a result of this process, along with our Twitter corpus, we release relevance judgements containing over 150,000 tweets, covering more than 500 events, which can be used for the evaluation of event detection approaches.
Starting Page	409
Ending Page	418
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505695
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Event detection Twitter corpus Reproducibility Social media Mechanical turk Test collection
Content Type	Text
Resource Type	Article

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